Alizé Mercier

Nathalie Bareille

Arnaud Tarantola

Maguelonne Teisseire

Nadia Haddad D'

Spécialité : Epidémiologie

Keywords: epidemic intelligence, early detection, infectious diseases, integration, pluridisciplinary List of associated publications

Approche intégrée de la veille sanitaire : importance de la pluridisciplinarité dans le cadre du développement de méthodes pour la collecte et l'analyse de données sanitaires Thèse présentée pour obtenir le grade de

Résumé

Malgré une amélioration des systèmes de surveillance et des avancées technologiques et scientifiques, la menace des maladies infectieuses émergentes continue de s'accroître. Dans le contexte actuel de changements globaux et de mobilité accrue des personnes, animaux et des biens, les systèmes de surveillance traditionnels basés sur la surveillance d'indicateurs ne suffisent plus pour assurer une détection précoce des émergences et de la propagation de foyers infectieux, particulièrement dans le cas d'agents pathogènes inconnus. Pour faire face à cette menace, de nouvelles approches telles que la veille sanitaire et l'intégration des données non-officielles ont été développées pour améliorer la performance des systèmes de surveillance et permettre aux autorités d'anticiper les menaces liées aux maladies infectieuses émergentes.

La veille sanitaire (également appelée intelligence épidémiologique d'après la traduction du terme anglais « epidemic intelligence ») est le processus de collecte, de vérification, d'analyse et de communication de toute information permettant la détection, la vérification, l'évaluation et l'investigation de dangers sanitaires dans un but d'alerte précoce (source : OMS).

La veille sanitaire intègre des informations provenant de deux composantes complémentaires : la surveillance basée sur des indicateurs (SBI) et la surveillance basée sur des évènements (SBE). La SBI est fondée sur la surveillance en continu d'informations structurées récoltées par des systèmes de surveillance routiniers. La SBE se base sur la détection d'évènements sanitaires inhabituels à travers le suivi de sources d'information non structurées qui peuvent être officielles ou nonofficielles (ex : médias, communication personnelle).

Le processus de la veille sanitaire comprend cinq étapes : la collecte, le tri, la vérification, l'analyse et la communication d'informations concernant de potentielles menaces sanitaires afin d'alerter les autorités sanitaires et de pouvoir fournir des recommandations de mesures de lutte efficace. Chaque étape de ce processus se fait en étroite collaboration avec un réseau pluridisciplinaire d'experts nationaux et internationaux, pour assurer la vérification et l'interprétation des évènements sanitaires détectés.

Un exemple de système de veille sanitaire est le dispositif « Veille sanitaire internationale » (VSI) de la Plateforme d'épidémiosurveillance en santé animale (ESA), que j'ai animé en réseau avec d'autres acteurs de la santé animale en France dont l'Anses. La Plateforme ESA a été créée en octobre 2011 et associe les principaux acteurs de la santé animale : Etat, scientifiques, organisations d'éleveurs, de chasseurs, de vétérinaires et de laboratoires d'analyse. Cette plateforme vise à améliorer l'efficience de la surveillance à travers notamment la mise en place de synergies entre recherche et surveillance, ainsi que le développement, l'amélioration et la promotion de différents dispositifs de surveillance.

La veille sanitaire internationale (VSI), et notamment la détection précoce de l'émergence et de la propagation d'agents pathogènes au niveau mondial, est l'un des maillons permettant de prévenir l'introduction en France de dangers sanitaires. L'intégration de la nouvelle thématique VSI au sein du programme d'activité de la Plateforme ESA a ainsi été décidée en janvier 2013. La VSI a pour objectif la détection, la vérification et l'interprétation d'événements inhabituels au niveau international pouvant diffuser et présenter un caractère de gravité pour la santé animale en France. Elle produit des informations sanitaires validées et interprétées à des fins d'évaluation du risque, de gestion du risque et d'information des professionnels de santé. Les signaux sanitaires détectés et analysés par la VSI proviennent de sources officielles (ex : bases de données de notifications et rapports de l'OIE, la Commission européenne et de la FAO), mais aussi des sources non officielles, de natures diverses, telles que les médias ou des communications obtenues à travers un réseau d'experts nationaux et internationaux. La VSI participe également au développement d'outils permettant d'améliorer la performance du dispositif à travers, par exemple, la collecte automatisée d'informations à partir de médias sur internet ou la modélisation de la vitesse de propagation d'agents pathogènes infectieux.

Dans le cadre de problématiques de santé qui sont à la fois complexes et trans-sectorielles, la pluridisciplinarité est un enjeu majeur de la veille sanitaire qui permet d'adopter une approche globale et intégrée de la surveillance des risques sanitaires. Elle permet de disposer de visions complémentaires et polyvalentes de la problématique pour en assurer une meilleure compréhension. L'intérêt d'une approche pluridisciplinaire est illustré dans ce manuscrit par quatre cas d'étude répartis en deux chapitres, l'un basé sur l'amélioration de la collecte de données pour alerter et l'autre sur l'analyse de données pour caractériser et prédire.

Les deux premiers cas d'étude (chapitre 3) présentent deux outils, PADI-web et OpenESSENCE, développés dans le cadre d'une approche pluridisciplinaire visant à améliorer la détection et l'alerte précoce de dangers sanitaires dans des contextes de santé animale et de santé humaine.

L'outil PADI-web (« Platform for Automated extraction of animal Disease Information from the web ») est un outil de veille automatisée des médias électroniques qui se base sur des techniques innovantes de fouilles de texte. L'outil identifie des articles de médias en ligne en utilisant une liste prédéfinie de mots clés qui peuvent être spécifiques (nom de maladies) ou non spécifiques (liste de syndromes ou d'hôtes). PADI-web a été développé pour la VSI de la Plateforme ESA afin d'améliorer la détection précoce d'évènements sanitaires à partir de données non officielles et non structurées disponibles sur internet. Les experts en veille sanitaire de la VSI ont été inclus dès les premières étapes du développement au groupe comprenant des chercheurs en sciences informatiques et en épidémiologie, afin d'assurer une co-conception de l'outil pour qu'il réponde aux besoins des utilisateurs et que ces derniers puissent se l'approprier plus facilement et durablement.

Dans le contexte de crise sanitaire et de mobilisation d'outils existants, PADI-web a été utilisé pour identifier les premiers articles médias traitant de l'émergence de la COVID-19 en Chine en décembre 2019, et en comparant cette détection précoce à deux autres outils de biosurveillance que sont HealtMap et ProMED. Outre la précocité de la première détection, les termes utilisés dans ces premiers articles médias ont été analysés pour mieux caractériser le vocabulaire en lien avec des phénomènes émergents encore inconnus. Cette étude a montré que l'outil PADI-web était tout aussi efficace que d'autres outils de biosurveillance internationaux et donc suffisamment générique pour pouvoir être utilisé dans un contexte One Health. Les résultats ont aussi permis d'identifier le vocabulaire le plus adapté à détecter un phénomène de nouvelle émergence d'un agent pathogène inconnu. En intégrant ce nouveau vocabulaire, les outils de biosurveillance seront un atout considérable dans la détection précoce de nouvelles menaces sanitaires, en complément des systèmes traditionnels basés sur la surveillance d'indicateurs.

L'outil OpenESSENCE est un outil dont l'utilisation s'inscrit dans le cadre du programme mis en place par la Communauté du Pacifique (CPS) afin de renforcer la surveillance syndromique mise en place lors de rassemblements de masse organisés dans le Pacifique. Ces évènements (religieux, socio-culturels, sportifs ou autre) rassemblent un nombre important de personnes et risquent de peser lourdement sur les ressources du système sanitaire local en termes de planification et d'action. A travers les fortes densités de personnes qu'ils génèrent, ces évènements augmentent considérablement le risque d'introduction et de propagation d'agents pathogènes infectieux, ainsi que leur exportation dans d'autres régions au retour des participants dans leur lieu de résidence. La surveillance électronique des maladies permet de réduire les délais en termes de collecte, d'analyse et de communication d'information sanitaire, particulièrement dans des contextes de ressources limitées, et d'améliorer ainsi l'alerte précoce et le contrôle des maladies infectieuses. L'outil OpenESSENCE, développé en collaboration avec des chercheurs de l'Université de Johns Hopkins, permet la collecte, l'analyse et la consolidation de rapports de manière centralisée et électronique, donc un gain de temps significatif dans la production journalière de rapports pour les autorités sanitaires et organisateurs de l'évènement. Le deuxième cas d'étude présente l'utilisation de cet outil pour le renforcement de la surveillance pour les Jeux de la Micronésie qui ont eu lieu à Pohnpei en juillet 2014. L'outil a été adapté à l'évènement par une équipe composée d'informaticiens, de programmateurs, d'épidémiologistes et d'experts en veille sanitaire, et des autorités gouvernementales locales, permettant ainsi la formation du personnel de santé, l'appropriation de l'outil par les autorités sanitaires locales, et le renforcement durable des capacités locales en veille sanitaire.

Ces deux cas d'étude illustrent l'intérêt de l'automatisation des méthodes utilisées à différentes étapes du processus de veille sanitaire, de la collecte à l'analyse et la conception de rapports permettant la dissémination d'information validée et analysée. Toutefois, l'intervention humaine demeure indispensable pour valider et interpréter ces informations afin de mieux formuler les recommandations de mesures de contrôle qui pourraient en découler. L'intégration pluridisciplinaire peut concerner différentes disciplines scientifiques dans le cadre de la recherche, mais également l'inclusion des utilisateurs tels que les professionnels de santé en charge de l'opérationnalité des systèmes de veille sanitaire. Au-delà des professionnels de santé, inclure les parties prenantes et les décideurs permet de donner un appui plus solide à la prise de décision des politiques et de les sensibiliser aux enjeux.

Dans le chapitre 4, l'importance d'une démarche pluridisciplinaire est à nouveau illustrée par deux cas d'étude présentant des méthodes d'analyse de données de veille sanitaire visant à caractériser les dangers sanitaires pour alimenter les analyses de risque et les modèles de prédiction.

La première étude présente l'analyse spatio-temporelle de la circulation de la dengue, du chikungunya et du Zika dans les pays et territoires insulaires du Pacifique de 2012 à 2014. Cette étude a été réalisée par la CPS avec des données de circulation de dengue, chikungunya et Zika dans le Pacifique de janvier 2012 à septembre 2014, dans le but d'analyser une possible évolution de l'épidémiologie de ces arboviroses dans la région. A travers une série temporelle et une carte de la localisation des épidémies et de circulation (= lorsque des cas sont déclarés sans qu'il n'y ait de mention d'une épidémie par les autorités locales) des trois arboviroses, l'étude a confirmé l'intensification de la circulation des trois arboviroses à l'échelle régionale, avec la réémergence du chikungunya en 2011 et l'émergence du Zika en 2013 dans la région, ainsi qu'une augmentation de la co-circulation de plusieurs sérotypes de virus de la dengue. Cette dernière constatation représente un changement dans l'épidémiologie de la dengue dans le Pacifique, qui était jusquelà marquée par la circulation d'un sérotype dominant ensuite remplacé par un nouveau sérotype introduit et se propageant à travers la région sous forme de vague épidémique. Cette propagation en vagues épidémiques est caractéristique d'une épidémiologie insulaire, marquée par un isolement géographique, une importante mobilité et la présence de plusieurs espèces de vecteurs d'arboviroses, ce qui augmente le risque d'épidémie. L'étude présente aussi une carte de distribution des principales espèces vectrices de moustiques Aedes présentes dans la région. Cette étude a été réalisée dans une démarche pluridisciplinaire rassemblant des épidémiologistes, des microbiologistes et des entomologistes afin de mobiliser plusieurs expertises complémentaires pour mieux caractériser et comprendre la circulation des arboviroses à l'échelle régionale.

La deuxième étude présente la modélisation de la vitesse de propagation de la dermatose nodulaire contagieuse bovine (DNCB) dans les Balkans de 2015 à 2016. Cette étude, que j'ai menée lorsque j'étais en poste au CIRAD, se base sur des donnés de foyers de DNCB dans les Balkans de mai 2015 à décembre 2016, extraites de bases de données de la Commission européenne et de la FAO. Le modèle utilise la date et la géolocalisation de foyers de DNCB pour en extraire et interpoler la semaine de première invasion sur toute la zone d'étude. La vitesse de propagation (km/semaine) est estimée à partir de l'inverse d'un indice de friction (semaine/km), correspondant à la résistance de l'environnement à la propagation du virus. La vitesse médiane de propagation de la DNCB dans les Balkans a été estimée à 7,3 km/semaine. La saisonnalité de la DNCB semble marquée par une augmentation du nombre de foyers de mai à août, ce qui correspond à des températures plus élevées et favorables à l'activité d'insectes vecteurs. Les résultats de l'étude nous ont permis d'identifier deux processus de diffusion distincts. Le premier processus, caractérisé par de faibles vitesses de propagation (inférieures à 15 km/semaine), correspond à une diffusion locale de la maladie essentiellement due à la transmission vectorielle ou un contact entre animaux malades et sains. Le deuxième processus est marqué par des vitesses de propagation de plus de 15 km/semaine (dont un maximum observé de 544 km/semaine) et correspond plutôt au transport d'animaux malades ou de produits contaminés, ce qui expliquerait ce « bond » de plusieurs centaines de kilomètres. Nous prévoyons d'incorporer des variables explicatives dans le modèle afin d'expliquer ces différences de vitesse observées. Une des limites du modèle est qu'il ne tient pas compte des campagnes de vaccination qui ont été mises en place à travers la région et qui ont permis de ralentir et au final d'arrêter la progression de la maladie à d'autres régions d'Europe.

L'adaptation et l'application de ce modèle d'estimation de la vitesse de propagation ont été mises en place grâce à un effort pluridisciplinaire regroupant des épidémiologistes et des modélisateurs, permettant aux épidémiologistes de comprendre les processus sous-jacents du modèle et de mieux en interpréter les résultats. Un package R appelé « spreadrate » a été développé pour faciliter l'utilisation du modèle par les experts de la VSI pour caractériser d'autres dangers sanitaires surveillés par la VSI. Ces deux cas d'étude présentent des exemples d'analyses rapides à mettre en place, menées par des équipes de veille sanitaire, permettant ainsi d'optimiser la caractérisation des dangers sanitaires détectés et l'information des autorités sanitaires. Les résultats de ces analyses peuvent aussi être utilisés pour des analyses de risque plus détaillées. Le renforcement du lien entre la détection, la caractérisation et l'analyse du risque associé à des dangers sanitaires permet d'effectuer des analyses de risque avec des informations mises à jour en temps réel par la veille sanitaire, et de pouvoir cibler les systèmes de veille sanitaire en fonction des résultats des analyses de risque.

Mes contributions aux quatre cas d'étude présentés se décline à deux niveaux. Le premier niveau correspond à une contribution méthodologique, visant à proposer des méthodes innovantes et des ajustements techniques d'outils de veille permettant d'en faciliter l'utilisation pour les utilisateurs. Le deuxième niveau est un méta-niveau qui se base sur l'intégration des connaissances d'experts afin de s'assurer que les outils développés répondent aux besoins, à la fois théoriques et techniques, des utilisateurs, favorisant ainsi une appropriation et utilisation durable par la communauté d'utilisateurs.

Il existe de nombreuses variantes à la définition de la veille sanitaire (ou intelligence épidémiologique), et je suis en accord avec celles fournies par l'ECDC et l'OMS. Contrairement à l'ECDC et à l'OIE par exemple, je considère que la veille sanitaire inclut la détection et la caractérisation du danger sanitaire, mais n'inclut pas l'étape de quantification et d'estimation du risque qui requiert une expertise différente, liée à la maladie, à l'agent pathogène et au mode de transmission. Cependant, la détection et l'analyse de risques sanitaires font partie d'un processus continu, qui nécessite donc de renforcer les liens entre la veille sanitaire et les analyses de risque. Ce lien peut varier à l'échelle internationale en fonction du mandat de l'institution, de l'expertise des différents experts et des ressources disponibles.

La mise en oeuvre d'une approche pluridisciplinaire et intégrée des différents processus de la veille sanitaire est essentielle pour améliorer la performance des systèmes à travers la mise en place d'outils et de méthodes innovantes. L'intégration de diverses disciplines scientifiques et l'implication des acteurs impliqués dans le processus de développement de ces méthodes permet d'avoir une vision globale de la problématique, de mieux comprendre les principes sous-jacents et d'en interpréter les résultats, mais aussi de plus facilement s'approprier les outils et méthodes développés. La crise COVID-19 est le dernier exemple en date du besoin de collaborations pluridisciplinaires et entre acteurs, avec la mobilisation du secteur de la santé animale et la mise à disposition de matériels, équipements et experts.

Au-delà de l'intégration de plusieurs disciplines et du renforcement des liens entre la détection et l'analyse du risque, l'intégration intersectorielle entre la santé humaine et animale est aussi essentielle afin de mutualiser les données, connaissances, ressources et outils dans le but d'améliorer la détection précoce de nouvelles émergences, qui se situent souvent à l'interface homme-animal. L'intégration des différents niveaux géographiques permet aussi une vision globale des enjeux et stratégies, ainsi qu'une remontée d'information et de problématiques depuis le niveau local. Elle promeut l'inclusion des différents acteurs locaux, nationaux et internationaux.

Dans le cadre de la modélisation du phénomène infectieux, l'inclusion de variables explicatives environnementales ou socio-économiques permet de mieux comprendre et de prédire les phénomènes d'émergence et de transmission des maladies infectieuses. Ceci est notamment prévu pour le modèle d'estimation de la vitesse de propagation. Avec l'expansion de l'internet et les récentes avancées technologiques, de nouvelles sources de données et de nouvelles technologies sont disponibles. L'intégration d'informations provenant des réseaux sociaux, de blogs, de forums et de requêtes sur internet, ainsi que la mise en place d'échanges d'informations avec les citoyens via des applications téléphoniques, peuvent permettent d'étendre le champ de surveillance des systèmes de veille et ainsi améliorer la précocité de la détection de dangers sanitaires.

Comme l'illustrent les trois émergences de nouveaux coronavirus ces deux dernières décennies, la menace de nouveaux agents pathogènes inconnus (correspondant à la « maladie x » désignée par l'OMS) est bien réelle et nécessite un renforcement de la surveillance de signaux faibles dans les systèmes de veille sanitaire. Cela peut passer par l'intégration de mots clés spécifiques aux émergences, tels qu'identifiés dans notre étude sur la COVID-19 avec PADI-web, au sein des outils de biosurveillance afin d'améliorer la SBE. Comme l'illustre la crise sanitaire COVID-19, il est indispensable de pouvoir mieux anticiper ces menaces en mobilisant une grande diversité d'expertises, pratiques autant que théoriques, et aborder la préparation et la prévention de manière plus proactive.

List of acronyms

Glossary of main terms

Alert: an event that requires the implementation of control or prevention measures by health authorities

Emerging infectious diseases (EID): diseases that have newly appeared in a population or have existed previously but are rapidly increasing in incidence or geographic range Epidemic intelligence (EI): discussed in the discussion (section 6.2)

Event-based surveillance (EBS): the organized collection, monitoring, assessment and interpretation of mainly unstructured ad hoc information regarding health events or risks, which may represent a risk to human health. The information collected for EBS originates from multiple, often not-predetermined sources both official and unofficial, including rumors reported by the media or ad hoc reports from informal networks (source: WHO)

Event: a signal that has been selected (i.e. represents a potential threat according to criteria defined by health authorities) and verified (i.e. to confirm the veracity of the information using official sources)

Health hazard: pathogenic agent that could produce adverse consequences on populations (also referred to as health threat)

Health risk: the probability of the occurrence of a health event with adverse consequences

Indicator-based surveillance (IBS): the systematic and routine collection, monitoring, analysis, and interpretation of structured data, i.e. indicators, produced by a number of well-identified, predominantly health-based official sources. (source: WHO)

Interdisciplinary: involving several disciplines to build common strategies through collaborative conception and implementation of research questions, methods and tools, and results

Multidisciplinary:

involving several disciplines working individually, in parallel, to contribute to the topic, with no collaboration between disciplines Outbreak: the occurrence of one or more epidemiologically-linked cases of a disease within an epidemiological unit Pluridisciplinary: involving more than one discipline (see also multi-disciplinary, inter-disciplinary and trans-disciplinary)

Risk analysis: discussed in section 5.1 -includes risk assessment, risk estimation and risk evaluation Signal: a health-related information that could potentially impact the target population Transdisciplinary: involving several disciplines working on a research topic that is co-constructed through collaborative and participatory methods, and includes strong involvement of social science stakeholders

Zoonotic disease: a disease in animals that creates a potential for a disease in humans as a result of human exposure to the animal source (or vector) (source: WHO)

List of illustrations

List of tables

Chapter 1 Introduction

Objectives

Epidemic intelligence is defined as the process of early detection, verification, assessment and reporting of health events to inform health control measures [START_REF] Paquet | Epidemic intelligence: a new framework for strengthening disease surveillance in Europe[END_REF]. It is key to anticipate and prevent the introduction and spread of health threats. Although it is a relatively novel concept, epidemic intelligence activities are thriving with the increasing global threat of emerging infectious diseases. This highlights the need for event-based surveillance to complement traditional surveillance systems and improve early detection and alerting.

I became aware of the importance and the transversal role of epidemic intelligence during my experiences in human and animal health, in the Pacific and in Europe, in research centers and in intergovernmental agencies. These experiences came from working as an epidemiologist at the French Research Institute for Development (IRD), the Pacific Community (SPC) and the French Agricultural Research Center for International Development (CIRAD) from 2013 to 2020. These experiences taught me the importance of connectivity between each step of the epidemic intelligence process, from data collection to reporting, from methodological tool design to operational implementation and sustainable use, from the local detection of an unusual event to national reporting and recommendation of control measures. I discovered similarities in methods, challenges and procedures between human health and animal health relating to epidemic intelligence, even though the geographical or disease scopes of my experiences were different. I noticed gaps in existing systems and aimed to improve these systems while adopting and promoting a pluridisciplinary approach.

The objective of this thesis is to describe and draw from different experiences the importance of adopting an integrated pluridisciplinary approach when implementing epidemic intelligence activities, from the early detection and alerting of health threats to their characterization and prediction, spanning across geographic scales, sectors and disciplines.

Following a brief introduction of the context of emerging infectious diseases, a first section presents the concepts and steps of epidemic intelligence, illustrated with the example of the French animal health epidemiological surveillance platform and its epidemic intelligence system [START_REF] Mercier | The French Epidemic Intelligence System: comparing disease surveillance at national and international level using data from the Program for Monitoring Emerging Diseases[END_REF]. I then proceed with several case studies of pluridisciplinary collaborations 22 in the context of tool development (to improve early detection and alerting of health threats) and the analysis of epidemic intelligence data (to better characterize and predict health threats). The case studies presented are the following:

-For the development of tools to improve data collection and early detection:

o An automated tool to collect health-related information from online media articles in order to detect the emergence of the novel coronavirus in China in 2019

o An electronic data collection tool to enhance disease surveillance during mass gatherings in the Pacific (Hoy et al., 2015;[START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF]) -For the analysis of epidemic intelligence data to characterize health threats:

o The analysis of the circulation of dengue, chikungunya and Zika viruses in 2012-2014 in the Pacific (Roth et al., 2014a) o The estimation of the spread rate of lumpy skin disease in 2015-2016 in the Balkans [START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF] Finally, the main ideas of this thesis are summarized and discussed, and I conclude with perspectives of future developments and strategies.

Context

Risk of disease emergence

Global changes including globalization, high mobility of people and goods, land-use changes, and climate change have favored the (re-)emergence of known and unknown infectious diseases [START_REF] Morens | The challenge of emerging and re-emerging infectious diseases[END_REF][START_REF] Rodríguez-Prieto | Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations[END_REF]. Emerging infectious diseases (EIDs) are "infections that have newly appeared in a population, or have existed previously but are rapidly increasing in incidence or geographic range" [START_REF] Morse | Factors in the Emergence of Infectious Diseases[END_REF]. A literature review conducted in 2005 revealed that 13% of the 1 407 known pathogens infecting humans were considered as emerging or re-emerging [START_REF] Woolhouse | Host range and emerging and reemerging pathogens[END_REF]. EIDs are mainly of animal origin (60% are zoonoses, i.e. diseases that can transmitted from animals to humans), and their incidence has significantly increased over time placing an additional burden on human and animal health services [START_REF] Morens | The challenge of emerging and re-emerging infectious diseases[END_REF][START_REF] Jones | Global trends in emerging infectious diseases[END_REF]. They are characterized by their unpredictability and instability in terms of occurrence, evolution and impact (health, social or economic), which makes them difficult to predict and prevent. Infectious pathogens are constantly evolving, and human actions can impact these pathogens, their epidemiological cycle and the interactions between pathogen, host and environment, and hence potentially increase the risk of outbreaks [START_REF] Vaillant | Les principes de l'investigation d'une épidémie dans une finalité de santé publique[END_REF].

International air travel has improved connectivity between endemic/epidemic and non-affected regions, thereby increasing the risk of rapid spread of infectious diseases across the world. Infectious diseases are not only spreading faster, they appear to be emerging more quickly. Since the 1970s, one or more new emerging diseases have been identified each year (World Health Organization, 2007). In Europe, the risk of introduction and spread of new infectious diseases has increased over the past 20 years along with the movements of humans and animals between Member States mainly due to the expansion of the European Union, the increase in population, and the promotion of 'open trade' between countries [START_REF] Rodríguez-Prieto | Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations[END_REF].

Of 335 EID events recorded between 1940 and 2004, 54% were caused by bacteria (e.g. Bacillus anthracis causing anthrax or Leptospira interrogans causing leptospirosis) -keeping in mind the role of antibiotic resistance for which the availability of quantitative information remains limited -, 25% by viral or prion pathogens, 11% by protozoa (e.g. Plasmodium falciparum causing malaria or Toxoplasma gondii causing toxoplasmosis), 6% by fungi (e.g. Penicillium marneffei or Candida albicans infections), and 3% by helminths (e.g. Trichinella spiralis causing trichinellosis or the Taenia solium tapeworm) [START_REF] Jones | Global trends in emerging infectious diseases[END_REF]. Viruses play an important role in disease emergence. Although they were not responsible for the majority of emergence events, the most devastating recent emergences (e.g. human immunodeficiency viruses -HIV, influenza H1N1, Ebola, coronaviruses) have involved ribonucleic acid (RNA) viruses, which can replicate in many host species and have high mutability potential. About a third of the viruses that have recently emerged are arboviruses (transmitted by arthropods, most often mosquitoes). The term 'disease' will be used in this manuscript to refer to infectious diseases, often of viral nature.

Examples of recent disease emergences

In a global economy with open markets and frequent exchanges of goods and animals over long distances, the unprotected and crowded farm animal population in Europe provides an ideal context for the (re-)emergence of exotic animal infectious diseases (Sobrino & Domingo 2002).

Previous outbreaks have underlined the sensitivity of European animal populations to EIDs, such as the introduction of foot-and-mouth disease (FMD) in the United Kingdom in 2001, responsible for over 1 000 outbreaks and a sub-sequent spread to France and the Netherlands, or the 2015-2016 emergence of lumpy skin disease in seven countries of the Balkans following an introduction in Greece in 2015 [START_REF] Sobrino | Foot-and-mouth disease in Europe[END_REF][START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF]. Several new highly pathogenic avian influenza viruses have also been reported such as the H5N8 virus that emerged in Europe in 2016, affecting over 21 countries including France [START_REF] Guinat | Spatio-temporal patterns of highly pathogenic avian influenza virus subtype H5N8 spread, France, 2016 to 2017[END_REF]. Another example is African swine fever (ASF), which was introduced in Europe in 2007 from Russia, and has since been reported in more than ten Eastern European countries (European Food Safety Authority, 2020). In September 2018, ASF emerged in wild boar populations in Belgium, probably introduced by human action (e.g. transport of animals or animal products), and led to 833 cases reported in the South of the country as of 28 th September 2020 according to the European Commission's database. ASF then emerged in Germany in September 2020, with 37 cases notified to the European Commission as of 29 th September.

There have also been several emergences of human diseases, notably caused by arboviruses (e.g. dengue, chikungunya, Zika), but also coronaviruses.

Importance of anticipating health threats

New emergence and spread of diseases are impossible to predict accurately. We can, however, anticipate these risks by improving disease surveillance to detect early signals of emergence [START_REF] Vaillant | Les principes de l'investigation d'une épidémie dans une finalité de santé publique[END_REF]. Anticipating health threats is crucial to mitigate the impact of a potential emergence of an infectious disease in a previously unaffected area. A recent example is the emergence of ASF in wild boar populations in Belgium in September 2018. Following this first detection of ASF in Belgium, a neighboring country, French animal health authorities rapidly implemented measures to prevent the introduction of ASF in France. These measures included:

 enhancing surveillance at the French-Belgian border,  a multi-national taskforce was created between Belgium, Luxemburg and France to facilitate the timely exchange of information and prevention/control strategies between countries,

 building fences at the French-Belgian border to avoid the crossing of potentially infected wild boars from Belgium to France,  raising awareness among health professionals, and  collaborating with hunters to eradicate wild boars in a defined perimeter near the border.

Due to the timely implementation of these prevention measures, France remained free from ASF more than a year after the emergence of the disease just several kilometers from the French-Belgian border. This example illustrates the importance of anticipating health risks and implementing prevention measures instead of waiting for the introduction of the disease to set up control measures. It also stresses the need for cross-border surveillance and collaborations.

The level of anticipation depends on whether the disease spread was diffusive and progressive (e.g. Germany, near the Polish border), or if there was a significant jump in the spatial distribution linked to the movement of infected animals or animal products (e.g. Belgium).

These last few years, surveillance systems have improved and outbreak information is communicated more widely and rapidly through information channels such as online media [START_REF] Valk | Les maladies infectieuses émergentes : importance en santé publique, aspects épidémiologiques, déterminants et prévention[END_REF]. The integration of epidemic intelligence activities has improved the timely detection and reporting of health threats to alert health authorities. This is particularly important given the significant consequences and costs of delayed detection of a novel disease spreading through a naïve population. For example, the FMD outbreak in the United Kingdom in 2001 was estimated to have cost 1.1 billion GBP in compensation costs alone, and the Ebola outbreak in Western Africa in 2014-2016 was estimated to have caused 53 billion US dollars in economic costs, including indirect and social costs [START_REF] Thompson | Economic costs of the foot and mouth disease outbreak in the United Kingdom in 2001[END_REF][START_REF] Huber | The Economic and Social Burden of the 2014 Ebola Outbreak in West Africa[END_REF]. Early alerting allows health authorities and professionals to prepare, for instance by setting up awareness campaigns, stocking up on vaccines and medical equipment, or enhancing surveillance in high-risk areas or potential entry points (e.g. borders, airports).

Improving surveillance

Recent disease emergence such as the novel 2009 influenza A (H1N1) outbreak and the COVID-19 pandemic in 2020 have demonstrated how rapidly pathogens can spread worldwide. The threat of EIDs, combined with a concern over intentional biological or chemical events, motivated the World Health Organization (WHO) to update its International Health Regulations (IHR) in 2005 (World Health Organization, 2008b). The 2005 updated IHR, a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions to help prevent and respond to global public health threats. In the revised 2005 IHR, WHO clearly expressed that the reporting requirement for countries is no longer based exclusively on a list of infectious diseases with epidemic potential, but rather consists of the obligation to report any event that may constitute a Public Health Emergency of International Concern (PHEIC) (World Health Organization, 2008b).

Surveillance and response are two key elements to control EIDs. The timely detection and reporting of health threats, along with the rapid implementation of control measures, are crucial to limit the spread of the disease and mitigate the impact on populations and the subsequent health, social and economic costs, as is illustrated in Figure 1 with the breaking of the epidemic curve which limits the spread of the disease. Chapter 2

Epidemic intelligence

Definition

According to the European Centre for Disease Prevention and Control (ECDC), epidemic intelligence (EI) activities focus on the early identification, verification, assessment and investigation of potential health hazards in order to recommend control measures [START_REF] Paquet | Epidemic intelligence: a new framework for strengthening disease surveillance in Europe[END_REF]. WHO also defines EI as the early detection, verification, assessment and communication of health threats (World Health Organization, 2014).

EI provides countries with a conceptual framework to complement traditional surveillance systems and help adapt them to face the challenges of emerging infections. The aim of EI is the early detection and monitoring of health threats. It does not focus on the long-term monitoring of trends or morbidity.

EI focuses on the detection, monitoring and assessment of health threats, but does not intervene in the implementation of control measures and risk management. Depending on the institution's mandate, EI might not be in charge of large-scale communication through the media, but EI communicates information relating to detected alerts and notifies health authorities.

Early detection surveillance can focus on the emergence of a known disease in a previously unaffected area (e.g. FMD in the United Kingdom), the emergence of a new and previously unknown disease (e.g. COVID-19 pandemic) or an unusual increase in the frequency of occurrence of a known and circulating disease (e.g. influenza). In this manuscript, I will focus mainly on the first two objectives which are the most important for the early warning of known and unknown threats in order to avoid potentially high costs and burden on health infrastructures.

Elements of language

Some elements of language need to be clarified in order to ensure the comprehension of the EI process.

Risk and hazard

The World Organization for Animal Health (OIE) defines a health hazard as "any pathogenic agent that could produce adverse consequences on populations" (World Organisation for Animal Health, 2019b).

A risk is defined as "the possibility of an unfortunate occurrence" [START_REF] Aven | Society for Risk Analysis Glossary[END_REF].

A hazard is something with the potential to cause harm, whereas a risk refers to the probability or likelihood of an occurrence, as well as the magnitude of the consequences. In this manuscript, I will use the term "health threat", which corresponds to health hazard i.e. a health-related event, most often an outbreak or a disease emergence, representing a potential threat for a target population.

An outbreak corresponds to the occurrence of one or more epidemiologically-linked cases of a disease within an epidemiological unit [START_REF] Cameron | Quantification of the sensitivity of early detection surveillance[END_REF]. The epidemiological unit considered by the surveillance system needs to be clearly defined, and can correspond to an animal or an individual, or a specific population depending on the scope of the system (e.g. herd, farm, village, district, country).

Indicator and event-based surveillances

EI integrates both indicator-based surveillance (IBS) and event-based surveillance (EBS) (Figure 2).

IBS refers to structured data collected through routine and traditional surveillance systems, such as official notifications made by health authorities to WHO or OIE. These notifications usually contain epidemiological, geographic and clinical information relating to a specific disease outbreak (e.g. location, date of detection and confirmation, virus, host, control measures implemented).

Traditional disease surveillance systems rely on IBS for the monitoring of health indicators from a range of sources, such as mortality, morbidity, clinical data, laboratory reports, surveys, animal or vector studies, hospital statistics and disease registries, over-the-counter drug sales, or school and work absenteeism. Traditional indicator-based systems have, however, demonstrated weaknesses in terms of early detection of health threats due to large amounts of data leading to delays in disease verification, analysis and reporting.

EBS focuses on the ad hoc collection and interpretation of real-time unstructured data gathered from heterogeneous sources that are not predefined and include non-official sources, such as electronic media, communications from field experts and social networks.

The use of EBS can improve early detection, but unofficial information (i.e. not officially validated) still needs to be verified by local health authorities because they could stem from false rumors [START_REF] Khan | Infectious disease surveillance and modelling across geographic frontiers and scientific specialties[END_REF]. Unofficial sources can help overcome limitations of traditional surveillance sources, including delays in detection and reporting, inconsistent population coverage and poor sensitivity in terms of early detection of emerging diseases [START_REF] Khan | Infectious disease surveillance and modelling across geographic frontiers and scientific specialties[END_REF]. They can increase the sensitivity of surveillance systems but, because the information is not verified, they can also increase the detection of false signals. The aggregation of several unofficial sources and the verification of the signal by local health authorities can help avoid wasting resources to investigate and respond to false signals.

The 2005 IHR emphasize the importance of both event-based and indicator-based components of EI for the early detection of health threats (Figure 2) [START_REF] Barboza | Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events[END_REF].

The process of epidemic intelligence: from data collection to reporting

To detect health threats in a timely manner and monitor their evolution, EI activities are based on the systematic review of a variety of information sources, both official and unofficial.

The EI procedure is divided into several steps: data collection (including screening and filtering), verification, analysis and communication to health authorities and decision-makers (Figure 3). These steps are usually formalized through an SOP (standard operating procedure). Depending on the situation, these steps can take time, particularly the validation and interpretation of the signal as they might involve several experts who might not be available to react instantly. Despite the number of steps in the EI procedure and the many people it might involve, the goal is to validate all the steps from data collection to reporting rapidly in order to ensure timely detection and communication of health threats to authorities and, in turn, timely action.

Figure 3. Epidemic intelligence framework

A signal is defined as any health-related information collected by the EI system, which could potentially impact human or animal populations (depending on the scope of the system).

An event is a signal that has been selected (i.e. represents a potential threat according to criteria defined by health authorities) and verified.

Different definitions exist for a signal and an event. I choose to use the terms as currently defined by ECDC and WHO, and not the definitions from Paquet et al. which are inversed, i.e. a signal corresponds to a verified event.

If the event is analyzed and judged to require the implementation of control or prevention measures by health authorities, it becomes an alert.

Data collection

When setting up EI activities, it is important to define the mandate and the scope of the system by determining the risk group. In the case of the French epidemic intelligence system (FEIS) which I led for four years, the EI team monitors any health-related event that could have a potential impact on the animal population in France, with a main focus on livestock (poultry, cattle) but also including wild animals, pets and other domestic animals.

Health-related information can be collected from many different information sources, both official and unofficial. Considering multiple sources of information, particularly combining unofficial and official sources, has been shown to improve the timeliness of the detection and the accuracy of the information collected (Yan et al., 2017a). For animal health, the main official sources of information for international EI activities are the Empres-I database from the Food and Agriculture Organization (FAO), the World Animal Health Information System (WAHIS) of the OIE and the Animal Disease Notification System (ADNS) of the European Commission. For human health, some examples of official information sources are the WHO, the ECDC and the Center for Disease Prevention and Control (CDC). Official information sources also include national or sub-national governmental health agencies, whether for veterinary or human health. Examples of unofficial sources for both veterinary and human health include media articles, rumors and unstructured personal communications from experts. The scope of sources monitored remains flexible and can rapidly and easily be adapted to the epidemiological context, for instance by strengthening EBS and monitoring local media outlets more closely for a country where a suspected outbreak has been detected.

Once the data has been screened, it is reviewed and filtered in order to detect new or updated information concerning potential health-related threats. Relevant information regarding health events are for example the occurrence of an outbreak of an EID, a significant increase in the endemic circulation of a known infectious disease, reports of high mortality or morbidity in animals with an unknown etiological cause, or epidemiological updates on ongoing EID outbreaks. Data filtering allows experts to discard information out of the surveillance scope (e.g. benign or non-infectious diseases), false rumors and duplicates in the collected data. This step is closely linked to the mandate of the institution and the scope of the EI system.

Data verification

The information collected is verified (or validated) in order to confirm its authenticity and veracity of its content.

Information gathered through IBS must be checked for possible surveillance biases and errors in reporting. For EBS, the verification of the information is particularly important when the information is collected from unofficial sources such as media articles or online blogs. Indeed, some information can generate false signals such as a false positive to a diagnostic test or a significant increase in reported cases with confirmation dates spanning over several months.

The information can be verified by corroborating the information with reliable sources of information such as health authorities (national or international), an expert (either a disease expert or a geographic expert in-country) or through networks of health professionals.

Signals are checked to verify the reliability of the information source, the veracity of the information, the clinical and epidemiological description of the event (e.g. clinical signs compatible with the suggested diagnosis) and elements of context such as control measures or biological confirmation [START_REF] Bitar | Veille sanitaire et appréciation du risque infectieux émergent: méthode et critères d'analyse du risque[END_REF]. This can be done through a review of scientific literature or networking with disease experts and health professionals.

The step of data verification also presents an opportunity to gather complementary information to provide context, which is useful for verification, but also for analysis and interpretation.

Data analysis

For the FEIS, data analysis consists of gathering context information to provide useful information to stakeholders regarding the potential risk of the event (e.g. historical trends, date of last outbreak in the region, neighboring countries affected, control measures implemented). For animal health, historical data can be gathered from official databases such as OIE's WAHIS, FAO's Empres-I or the European Commission's ADNS. If limited official information is available due to underreporting or endemic circulation, scientific literature can be consulted for information on past and current outbreaks.

Reference laboratories can be consulted to provide an interpretation of the event in its context.

Other types of information can be used to describe the context such as weather data, distribution data for insect vectors or mobility data relating to animal movements or human air travel.

Depending on the institution's mandate, the data can be further analyzed for risk assessment purposes. This includes gathering information relating to the pathogen, the host and the environment in order to better characterize the risk factors and the potential for evolution. This can be complemented by the use of epidemiological models to predict the importation of the pathogen, possible scenarios of spread, or to model the impact of different control and prevention measures [START_REF] Bitar | Veille sanitaire et appréciation du risque infectieux émergent: méthode et critères d'analyse du risque[END_REF].

Data reporting

Once the event is analyzed, the validated and interpreted information is communicated to health authorities and professionals. We can distinguish two levels of communication: internal communication (i.e. within an institution or national EI system) and external communication.

External communication can target health professionals or, in a larger sense, the general population. The dissemination of information can be oriented towards the general public, in collaboration with or by health authorities. In any case, the content of the communication should be adapted to the targeted audience. When the communication targets the general public, technical vocabulary should be avoided and the message should be vulgarized in order to be understandable and clear to non-health professionals and civilians. This would apply to awareness campaigns, for example, to improve awareness and acceptability of prevention and control measures. It is important to inform without alarming, particularly during health crises such as the recent novel coronavirus pandemic.

Existing systems

Existing EI systems, whether at national or international level, rely on the different steps of the EI procedure (from data collection to reporting) to ensure timely detection and reporting of health threats to appropriate authorities in charge of risk management.

Some international organizations have set up EI systems, such as ECDC, WHO, OIE or the FAO. These international systems aim to timely detect and monitor disease outbreaks at the international level. The geographic scope of the diseases monitored varies depending on the mandate of each organization. ECDC monitors disease threats for European Member States while WHO, OIE and the FAO have a more global scope.

Some countries have set up an EI system at national-level through national health agencies. Some examples of these countries include France for human health [START_REF] Rotureau | International Epidemic Intelligence at the Institut de Veille Sanitaire, France[END_REF] and animal health [START_REF] Mercier | The French Epidemic Intelligence System: comparing disease surveillance at national and international level using data from the Program for Monitoring Emerging Diseases[END_REF], England for human health [START_REF] Wilburn | Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?[END_REF] and animal health (Animal and Plant Health Agency, 2017), or the Netherlands for human health [START_REF] Bijkerk | ECDC Round Table Report and ProMed-mail most useful international information sources for the Netherlands Early Warning Committee[END_REF].

These systems can be centralized through a specifically dedicated team (e.g. animal health in France) or dispatched among several disease-specific units (e.g. human health in France). The EI system in place for animal health surveillance in France will be detailed in the following section.

A comprehensive review of existing EI systems in Europe, for both animal and human health, is currently being conducted in the framework of the Horizon 2020 (H2020) research project funded by the European Commission, entitled "Monitoring outbreak events for disease surveillance in a data science context" (MOOD). This detailed review will focus on both national level (five case study countries: France, Italy, Spain, Serbia and Finland) and international level (WHO, OIE, ECDC, FAO) EI systems. It will cover dataflow and workflow, organizational structure, SOPs, dedicated resources, tools and databases used. I am co-leading this activity with colleagues from the public health agency in Finland. Results should be available by early 2021.

Presentation of the French epidemic intelligence system for animal health

The French animal health epidemiological surveillance platform

In France, the main challenges in animal health have evolved in the past decades with recent emergences leading to significant economic losses. Examples include bluetongue serotype 8 in 2006 [START_REF] Sailleau | Re-Emergence of Bluetongue Virus Serotype 8 in France, 2015[END_REF], Schmallenberg virus in 2011 [START_REF] Dominguez | Spread and impact of the Schmallenberg virus epidemic in France in 2012-2013[END_REF], and highly pathogenic avian influenza H5N8 in 2016 [START_REF] Guinat | Spatio-temporal patterns of highly pathogenic avian influenza virus subtype H5N8 spread, France, 2016 to 2017[END_REF]. Thankfully, surveillance, prevention and control efforts have improved in the past years. Following the acquisition of a disease-free status for multiple contagious diseases (e.g. FMD or rabies), the focus of disease surveillance in France switched to vigilance. Instead of monitoring the evolution of diseases already present in the country, health authorities had to focus on the early detection of new and emerging diseases at the international level to anticipate new introductions of emerging health threats.

Existing surveillance systems needed to be adapted in order to face the new challenges of disease emergence. For example, setting up a non-disease-specific surveillance through the implementation of syndromic surveillance would cover a broader scope of diseases with similar clinical manifestations. Partly due to these considerations, the French Ministry of Agriculture suggested the creation of a national platform for epidemiological surveillance in animal health.

The French animal health epidemiological surveillance (ESA) platform 1 was set up in 2011 to improve the efficiency of epidemiological surveillance at national level (Figure 4).

34

-Adilva), hunters (National Hunters' Federation -FNC), and wildlife and biodiversity services (French Office for Biodiversity -OFB) (Figure 5).

Through its members, the ESA Platform provides scientific support to improve animal health surveillance efficiency through a public/private partnership. The main objective of the ESA Platform is to ensure that animal health surveillance systems are efficient and are developed according to methodological standards for surveillance. It aims to develop, adapt and promote surveillance systems, conduct EI activities, provide reports on the current epidemiological situation of health hazards, participate in the development of synergies between surveillance and research, ensure a co-construction of surveillance modalities between animal health professionals, and understand the determinants of health events to elaborate adapted surveillance measures. More generally, the activities of the ESA Platform focus on:

 supporting the implementation and improving epidemiological surveillance systems,  centralizing, analyzing and sharing health data, and  contributing to the analysis and dissemination of health information.

The scope of the platform covers all health threats that have or could have an impact on animal health and/or human health (zoonoses) in France, and for which surveillance is required for animals.

The ESA Platform provides a common structure to bring animal health stakeholders together, around common strategies, methods and tools. It is organized in 20 working groups, most of which focus on a specific disease or animal group. These working groups are presented in relation to the main disease group and animal type they focus on (Figure 6). Since its implementation in 2011, the ESA Platform has demonstrated its utility and efficiency, particularly during animal health outbreaks such as highly pathogenic avian influenza, Schmallenberg virus or bluetongue. On 20 th June 2018, a new national framework convention was signed, ensuring the continuation of the platform's activities for another ten years. In addition, two sister platforms were created in 2018, following the model of the ESA Platform, for plant epidemiological surveillance ("plateforme d'épidemiosurveillance en santé végétale", ESV) and food safety ("plateforme d'épidémiosurveillance de la chaîne alimentaire", SCA).

Scope and members

In 2013, to better anticipate the threat of EIDs, international animal health monitoring was integrated into the ESA Platform's activities with the creation of the French Epidemic Intelligence System (FEIS), or "Veille sanitaire internationale" in French (Figure 7). The FEIS is in charge of animal health EI for the French Ministry of Agriculture. Its main activity is to detect, monitor and report health events, at national and international level, that could potentially threaten animal populations in France, in order to inform French authorities. Some coordination and administrative work are necessary to ensure the system's optimal functioning, e.g. keeping the network active and updated on current activities by organizing regular feedback meetings with the different stakeholders.

Members of the FEIS include all members of the ESA Platform so that the FEIS can meet the needs of all relevant stakeholders involved in surveillance activities (from farmers to veterinarians, policy makers, scientists, risk assessment experts or experts in EI methodology) in terms of access to updated, validated and interpreted information on animal health events. The FEIS team is led by CIRAD and ANSES.

Regarding surveillance for French overseas territories, the FEIS relies on regional surveillance networks such as the Caribbean animal health network (CaribVet) and the Public Health Surveillance, Alert and Response network SEGA ("Surveillance Epidémiologique et Gestion d'Alertes") in the Indian Ocean [START_REF] Gongora | The Caribbean Animal Health Network: New Tools for Harmonization and Reinforcement of Animal Disease Surveillance[END_REF][START_REF] Halm | Four years into the Indian ocean field epidemiology training programme[END_REF].

International agencies have set up EI systems to monitor health risks worldwide (e.g. ECDC or the International Society for Infectious Diseases -ISID -with its Program for Monitoring Emerging Diseases -ProMED). Because the FEIS monitors animal health risks for France at the international level, it monitors the same signals as supra-national systems. However, the FEIS stands out by also monitoring other sources of information to include in its reports, in addition to country notifications, in order to provide detailed reports with interpreted and contextual information to decision-makers. The existence of the FEIS is also justified by the extra layer of filter, which allows tailoring animal health surveillance according to the national context. Indeed, needs in terms of animal health surveillance may vary from one country to another depending on strategies and priorities (e.g. priority given to caterogy-1 health hazards as defined by French animal health authorities) (Le Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt, 2013).

Epidemic intelligence activities

The FEIS combines IBS and EBS by monitoring both official (e.g. French animal health authorities, OIE, FAO, the European Commission) and unofficial (e.g. media, ProMED-mail, personal communication) sources of information relating to animal health. Once collected, the information is verified and analyzed with the FEIS' network of national and international experts (Figure 8). This allows the FEIS to analyze and interpret the event according to the context when writing a report, and include information relating to the viral strain, seasonality patterns, the previous occurrence or the historical circulation of the virus in the country. Information relating to different disciplines, such as epidemiology, virology or entomology, is also useful to provide context and a holistic view taking into account complementary perspectives. This reinforces the need for a pluridisciplinary approach to EI, which is why the FEIS team and expert network include experts from a wide range of disciplines such as epidemiology, informatics, entomology, virology, and many other specialties.

Reporting and publications

The FEIS publishes reports to inform animal health authorities and professionals of potential animal health risks. A report is published on a specific event when there is a quantitative or qualitative change in the epidemiological situation of the disease, i.e. re-emergence, geographic spread or increase in number of outbreaks. The dissemination of FEIS reports can be either public on the ESA Platform website or confidential (sent to the ESA Platform steering committee) depending on the source of the information it contains.

In addition to these reports, the FEIS produces a weekly EI bulletin named "Bulletin hebdomadaire de veille sanitaire internationale en santé animale", or BHVSI-SA. This bulletin includes various sections corresponding to specific diseases with, for example, sections for highly pathogenic avian influenza, ASF, West Nile or FMD. The bulletin is generally 5 to 15 pages long, depending on the global epidemiological situation. It is published online on the ESA Platform's website since October 2018.

Research activities

To further strengthen its EI activities, the FEIS is also involved in research projects. The FEIS plays a role in nurturing useful research questions which it then contributes to in order to find methodological solutions in line with its needs. These research projects aim to develop innovative tools and methods to improve the early detection and analysis of health threats. Data collection and analysis tools developed in the framework of the FEIS' participation in research collaborations include PADI-web, an online media monitoring tool, or a disease spread rate model that estimates the speed of spread of diseases. These two examples of tool will be described further in the manuscript (Sections 3.2 and 4.3). The FEIS is also actively involved in the H2020 MOOD project.

The FEIS participated in the project proposal, and will be among the end-users consulted and interviewed as part of the comprehensive review of EI activities in Europe. The four-year project (2020-2024) operates on a budget of 15 million euros. The FEIS' involvement in this project will set the stage for new research questions and future projects, aiming at further improving EI activities through the integration of different sources and types of information (e.g. environmental or molecular data), as well as new technologies.

Preliminary evaluation of the FEIS

The contribution of the FEIS to the EI process (data collection, verification, analysis and reporting) have been presented in a scientific article. After three years of leading and improving the FEIS, I decided to conduct a short study to evaluate if the FEIS detected and reported on all important events relating to animal health according to its scope, as a way to test its efficiency. To do so, I compared the reports produced by the FEIS to posts published by ProMED from 1 st January 2016 to 31 st December 2017, to identify if the system had missed any information. I set up a partnership with ProMED directors (based in the United States of America) and moderators in order to promote international collaborations through the bilateral exchange of information and discussions relating to methodology. The results of the analysis of the reports published by the FEIS from 2016 to 2017, and the comparison of these reports to ProMED alerts are presented in Mercier et al., 2020.

Limitations and challenges

EI systems face several technical and organizational challenges that can hamper the exhaustiveness, timeliness and efficiency of these systems:

 Completeness of the collected information and its ability to reflect the current epidemiological situation is a main challenge. As mentioned, traditional surveillance systems relying on IBS to gather validated and official information in the form of indicators. However, this method may miss information in the early stages of disease emergence, and lacks timeliness and sensitivity in the detection of unusual events relating to emerging diseases. To overcome this challenge, some EI systems have set up an EBS component and use automated biosurveillance tools to monitor unofficial information sources such as online media reports or scientific publications.

 Timely verification of the information collected, particularly if the information originates from an unofficial source like the media or rumors, is another challenge. As previously stated, unofficial information can be validated using official sources or a network of experts. In the framework of the FEIS, this challenge was addressed by setting up an extensive network of national and international experts with different fields of expertise (e.g. epidemiology relating to both human and animal health, virology, entomology, farming systems). This network includes all of the French national reference laboratories for animal diseases as well as several European and OIE reference laboratories, which can be a very useful source of validation of information regarding the occurrence of an emerging disease.

 Timely reporting of health threats to authorities in charge of response can also be challenging. The internal hierarchical chain of validation of reports can delay the dissemination of information, particularly in the case of large institutions or agencies. To address this issue, French animal health authorities have set up the ESA Platform to gather animal health professionals and stakeholders from different sectors (veterinary agencies, farmers, veterinarians, wildlife services, hunters, laboratories and research centers) and facilitate the exchange of information, methods and expertise.

International EI activities conducted by international organizations such as WHO, OIE or ECDC have their limits, but can be complemented by national-level EI systems. International systems provide a global coverage and monitor a large variety of information sources, which makes them very efficient at timely detection of health risks. The analysis of the threat, however, is very country-dependent. This is why some countries have set up an international EI system that uses international systems as information sources, but also provide an interpretation and analysis tailored to the country's needs. This means that health threats are filtered depending on the level of risk they represent for the specific country. This risk depends on several factors such as historical disease circulation, economic ties (e.g. trade, governance), travel access or geographical proximity. This challenge was addressed in France with the creation of a French international EI system as part of the French animal health epidemiological surveillance platform.

The integration of new methods, data sources or tools can be complicated by several factors. First, we should aim to improve and complement the existing systems already in place instead of creating new and independent tools, which can duplicate and complicate the work. Also, the developed methods or tools should fit the needs of end-users in order to ensure appropriation and sustained use of the tool. We are addressing these challenges in the framework of the H2020 MOOD project proposal by integrating a thorough review of existing activities, systems and tools, and adding a social science component in one of the work packages. This component aims to identify the main gaps and challenges relating to EI data and work flows within institutions of EI activities, and identify end users' needs in terms of data, tools and methods for the co-conception of solutions. The integration of social science processes such as participatory methods can facilitate interactions and communication in research projects involving many different stakeholders, disciplines and geographical scales.

Chapter 3

A pluridisciplinary approach to data collection in the context of tool development

Introduction

"Disciplines are the result of the artificial fragmentation of knowledge" [START_REF] Choi | Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness[END_REF].

Some researchers, health professionals and policy-makers have a tendency to work individually, in silos, with limited interactions and collaborations. Information and tools often cross from one silo to another, but with limited sharing of knowledge and resources with a common goal. The development and implementation of tools to improve disease surveillance, for example, tend to be conducted unilaterally. This means tools are developed by researchers in informatics and computer sciences with limited consultation of end-users who might later have to implement and use a tool unfit for their needs or not practical to use. This can result in limited appropriation of the tool by end-users and limited sustainability of solutions for stakeholders.

Collaboration between experts from different disciplines is increasingly promoted in health research and services, particularly given the complex and interdisciplinary nature of current health issues [START_REF] D'amour | The conceptual basis for interprofessional collaboration: Core concepts and theoretical frameworks[END_REF][START_REF] Choi | Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness[END_REF]. The term collaboration implies the notions of sharing and collective action towards a common goal. Teamwork involving several disciplines in the framework of tool development can be implemented at different levels. Several terms can describe such collaborations including multidisciplinary, transdisciplinary, interdisciplinary or pluridisciplinary. These terms are not always clearly defined and are sometimes used interchangeably. Here are a few definitions of pluridisciplinary approaches (involving more than one discipline) adapted from Schelling, 2016 and Darbellay, 2008 that I have chosen for this manuscript:

-A multidisciplinary approach involves several disciplines working individually, in parallel, to contribute to the topic, with no collaboration between disciplines. -An interdisciplinary approach aims to build common strategies through collaborative conception and implementation of research questions, methods, tools and results.

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-A transdisciplinary approach focuses on a research topic that is co-constructed through collaborative and participatory methods, and includes strong involvement of social science stakeholders.

Multidisciplinary (additive), interdisciplinary (integrative) and transdisciplinary (holistic) approaches reflect different degrees of collaborations along a continuum of pluridisciplinary work [START_REF] Choi | Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness[END_REF].

In this section, I present two case studies based on my experience conducting EI activities in animal health in France (CIRAD, 2016(CIRAD, -2020) ) and in human health in the Pacific (SPC, 2014(SPC, -2016)). These case studies describe examples of pluridisciplinary collaborations between researchers and endusers (in this context health professionals) in the framework of tool development to improve health surveillance activities.

In both of these studies, I actively contributed on two levels. The first is a meta-level to promote a global and generic approach based on a pluridisciplinary process and to identify the role of each stakeholder involved in the process, to ensure operationality. The second is a specific and technical level to suggest innovative methods and ensure the sustainable use of the tools by endusers. Both levels aim to place each type of stakeholder at the center of discussions to improve pluridisciplinarity. I acted as an intermediate link between end-users and tool developers, between research and surveillance, to allow the pluridisciplinary co-construction of tools.

Development of an automated media monitoring tool for animal health (PADI-web)

The first case study presents a pluridisciplinary collaboration to design an electronic media monitoring tool and use it to retrospectively detect the emergence of the novel coronavirus in China in December 2019 (Valentin et al., 2020a).

Internet event-based surveillance

Internet biosurveillance, or digital disease detection, relies on unstructured information collected from a variety of online sources, and focuses on the early detection and alerting of health risks [START_REF] Brownstein | Digital disease detection -Harnessing the web for public health surveillance[END_REF][START_REF] Hartley | An overview of Internet biosurveillance[END_REF]. With increasing volumes of information and media outlets available online, internet biosurveillance has become a useful tool to monitor unofficial sources on the internet and complement traditional surveillance systems mainly relying on official sources.

Unofficial sources of information are important to consider and monitor in addition to official sources because their integration can improve the timeliness and sensitivity of disease surveillance systems (Yan et al., 2017a). WHO has reported that more than 60% of initial disease outbreak reports originated from unofficial information sources (World Health Organization, 2016). In combination with the significant increase in internet use and accessibility worldwide, internet-based EBS has become an increasingly pertinent and useful tool for EI activities (Yan et al., 2017a).

Several EBS systems exist to monitor unofficial sources from the web and detect occurrences of a wide range of health threats [START_REF] Barboza | Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events[END_REF]. Some examples include ProMED developed in 1994 by ISID in the United States of America [START_REF] Carrion | ProMED-mail: 22 years of digital surveillance of emerging infectious diseases[END_REF], the Global Public Health Intelligence Network (GPHIN) developed in 1997 by the Public Health Agency of Canada [START_REF] Dion | Big Data and the Global Public Health Intelligence Network (GPHIN)[END_REF], and the MedISys tool developed in 2004 by the European Commission's Joint Research Center [START_REF] Rortais | MedISys: An earlywarning system for the detection of (re-)emerging food-and feed-borne hazards[END_REF]. These biosurveillance tools are developed independently and their functionalities may vary (e.g. number of languages, type of health threats monitored and scope).

ProMED is based on a fully human-moderated process, while other systems are fully automated (MedISys, PADI-web) or include both automatic and manual processes (HealthMap, GPHIN). Figure 9 presents the main existing biosurveillance tools which include animal health threats in their scope, and compares their features and data processing techniques.

Figure 9. Comparative table of existing biosurveillance tools including animal health threats, according to their scope and data processing approaches (Valentin et al., 2020b).

These systems are not domain-specific and focus mainly on human health. Although some include animal-related news, none of them focuses specifically on animal health threats.

Platform for automated extraction of animal disease information from the web (PADI-web)

Although many biosurveillance tools exist and have proven their efficiency in detecting outbreak news on the web, they provide limited coverage of animal diseases (Valentin et al., 2020b).

The Platform for Automated extraction of animal Disease Information from the web (PADI-web) is an automated text mining platform that detects, categorizes and extracts disease outbreak information from news articles published on the Internet (Figure 10) [START_REF] Arsevska | Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System[END_REF]. PADI-web was designed for the FEIS to facilitate the monitoring of animal disease outbreaks from unofficial sources of information such as online news articles. It incorporates intelligent systems based on text mining which include natural language processing, machine learning and data mining techniques [START_REF] Arsevska | Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System[END_REF].

A first module of the platform aims to identify and collect online news articles using a series of customized Really Simple Syndication (RSS) feeds on Google News. Google News was chosen as the main data source because it is freely accessible and allows users to tailor search parameters.

The RSS feeds use a list of predefined keywords relating to hosts, symptoms and disease names (Arsevska et al., 2016). They can be disease-specific (using disease names) or non-specific (using a combination of symptoms and hosts). This allows the tool to monitor nine specific diseases of interest (ASF, classical swine fever, avian influenza, FMD, bluetongue, Schmallenberg virus, lumpy skin disease, peste des petits ruminants and West Nile), as well as information on other diseases or syndromes (e.g. early stages of emergence, before official confirmation of etiology).

The tool identifies and collects news articles containing a predefined combination of keywords.

The majority of RSS feeds use English keywords, but we have also implemented feeds in other languages to increase local media coverage. These languages were selected to target areas with enzootic circulation of specific diseases or at high risk of disease emergence (e.g. adding Arabic to monitor FMD in endemic Arabic countries, or Chinese to monitor ASF). The integration of multilingual RSS feeds has significantly increased the number of relevant news articles detected (Valentin et al., 2020b).

Once collected, news articles are processed to remove duplicates from the database. News articles that were retrieved using non-English RSS feeds are translated into English. News items are then classified to determine if they are pertinent, i.e. relating to an animal disease event (e.g. describing a current outbreak, prevention and control measures, or socioeconomic impact of a disease). This classification step is run by a supervised machine learning process. In this process, experts define two classification categories (pertinent/non pertinent) and manually annotate a small dataset in order to build a model (supervised classification). Based on labeled data, the machine-learning approach takes into account textual content of both classes in order to construct the classification model. This model is then able to establish definitions for the two classes based on the expert-annotated dataset, and annotate new data (machine learning). The model is continuously enriched as new data is added to the database and classified (daily update of the model). This underlines the importance of expert annotation and input at the beginning of the process to set the foundation for the model classification, and to continuously update the tool's keywords if new disease threats emerge. A more specific type of classification has been recently implemented, and consists of five topic categories to go beyond binary relevance classification (Valentin et al., 2020c):

 confirmed outbreak: information about a new or ongoing confirmed outbreak,  suspected outbreak: information about new or ongoing cases which have not yet been diagnosed but are associated with a suspected disease,  unknown outbreak: information about new or current cases not yet diagnosed and not associated with a suspected disease,  preparedness: information about prevention measures in a country not yet affected by the diseases but on alert to prevent the introduction of the disease, and  impact: information on economic, societal or political impact of disease outbreaks in an affected country or region.

The last module relates to information extraction, which aims to extract from the content of the news article epidemiological information describing the event, such as disease, dates, geographic location, affected host, clinical signs and number of cases. The information extraction process is based on a combination of data mining techniques, expert validation and ruled-based systems using a series of dictionaries [START_REF] Arsevska | Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System[END_REF]. The experts first annotate epidemiological identities in news articles from a specific dataset, from which the model learns rules to identify these epidemiological features (rule-based). Then, the model applies these rules to identify pertinent entities in other news articles (machine learning).

Figure 11 illustrates the PADI-web process, from data collection to information extraction.

Figure 11. PADI-web process (Valentin et al., 2020c) The PADI-web interface allows users to view the content of each news article and visually identify the key epidemiological information extracted (Figure 12). It also allows the user to view trends (e.g. monthly number of relevant articles for a specific disease), filter the view of selected news articles according to several parameters (e.g. disease, dates), and download the extracted epidemiological information for a selection of news articles. The query can also be exported through an RSS feed, allowing users to save its parameters and results. and5) details on the selected location (Valentin et al., 2020b) PADI-web was developed to improve the EI activities of the FEIS. As team leader of the FEIS and member of the PADI-web team from 2016 to 2020, I contributed on two levels. The first level is a meta-level and includes the integration of expert knowledge to ensure the co-design of the tool.

The second level focuses on the participation to technical developments. I actively contributed to finding solutions in terms of methodological developments to fit the needs of the FEIS in terms of disease and geographic scopes, languages, extracted information, visualization and user interface for the tool's development and testing. I contributed to the improvement of the classification process (selection of classes, defining pertinence), identifying pertinent epidemiological entities to better define information extraction, and providing feedback on the tool's use for EI activities.

I also contributed to the development and evaluation of additional modules of PADI-web which aimed to improve online diseases surveillance through the use of novel methods for data collection such as visual analytics and visualization of extracted information. My contributions focused on integrating expert knowledge to 1) implement innovative visualization methods and 2) collect and integrate annotated data in automated processes. One of these modules is EpidVis, a tool that uses visualization techniques to identify the most pertinent keyword associations and provide suggestions to improve data queries in PADI-web [START_REF] Fadloun | EpidVis: A visual web querying tool for animal epidemiology surveillance[END_REF]. I contributed to the discussions on the design of the interface (Annex 1) and the link with PADI-web, and led a case study on the use of EpidVis for EI. Another example is EpidNews, a visual module that creates figures presenting the percentage of articles for each keyword and maps of the epidemiological entities to facilitate the visual detection of anomalies (e.g. identifying a disease in a region where this disease is absent according to official sources) (Annex 2) [START_REF] Goel | EpidNews: Extracting, exploring and annotating news for monitoring animal diseases[END_REF]. I contributed throughout the development process by: 1) leading the identification of user needs, 2) coconstructing the method and 3) analyzing and interpreting results through different case studies.

A first version of PADI-web was developed in 2013 in collaboration with researchers from CIRAD (TETIS unit), INRAE, the Informatics, Robotics and Microelectronics Laboratory in Montpellier (LIRMM), and epidemiologists from CIRAD (ASTRE unit) involved in the FEIS. Developmental work was carried out since 2013 to continuously improve the tool, particularly the classification module in order to reduce the number of non-relevant articles collected (Valentin et al., 2020b). A second version of PADI-web is available since January 2019, and continuous developments implemented in 2020 will provide an updated version at the end of the year. Further methodological developments will focus on improving the classification module by setting up more detailed classes to go beyond pertinence (e.g. first occurrence of a disease, control measures or economic and social impact) (Valentin et al., 2020c). It will also build on a machine-learning process to construct a classification module. The tool has been integrated in the FEIS' activities on an ad hoc use depending on epidemiological news, for example to alert on circulation of avian influenza viruses in Southeast Asia or outbreaks of FMD in Africa several days before official OIE notification [START_REF] Arsevska | Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System[END_REF][START_REF] Valentin | Surveillance automatisée de sources non-officielles pour la veille sanitaire internationale[END_REF].

Retrospective study of the novel coronavirus disease (COVID-19) in China using PADI-web

Context of the study

On 31 st December 2019, the Municipal Health Commission in Wuhan, China (Hubei province) reported a cluster of pneumonia cases with an unknown etiology. A common link to Wuhan's Huanan Seafood Wholesale Market (a fish and live animal market selling different animal species) was reported by authorities. The market was closed to the public on 1 st January 2020. Cases showed symptoms of influenza-like illness such as fever, dry cough and radiological findings of bilateral lung infiltrates. On 9 th January 2020, a novel coronavirus was identified as the cause of the outbreak of respiratory illness (World Health Organization, 2020b). The virus was identified as SARS-CoV-2 (previously referred to as 2019-nCoV) and the disease it causes was named COVID-2019 (coronavirus disease 2019). As of 21 st September 2020, the outbreak has resulted in a global pandemic with over 31 091 469 cases and 961 352 deaths reported worldwide (European Centre for Disease Prevention and Control, 2020).

CIRAD coordinates the H2020 MOOD project. The project started in January 2020 for a duration of four years, and I actively contributed to the design of the project proposal. In January 2020, following the rapid spread of COVID-19 to other countries, the European Commission asked the coordination team of the MOOD project to adapt the project's activities to support European national public health agencies in the response to the COVID-19 pandemic. Even though the MOOD project had just started, several tools were already made available by its members, including PADI-web. PADI-web was designed to be generic and applicable to other domains such as human health through its customizable keywords defined by users.

The objective of the study was to test the genericity of the tool in a One Health context, i.e. its ability to detect the emergence of a new virus in humans with lists of keywords designed for animal health surveillance. We designed a retrospective study to analyze PADI-web's capacity to identify online news articles relating to the COVID-19 emergence in China, in comparison with two other biosurveillance tools, ProMED and HealthMap. ProMED is a human-moderated system that relies on a large network of experts worldwide who produce and share verified reports on disease outbreaks through a common platform [START_REF] Carrion | ProMED-mail: 22 years of digital surveillance of emerging infectious diseases[END_REF]. HealthMap is a semi-automated system founded by the Boston Children's Hospital in 2006 to monitor both official and unofficial news sources on the web [START_REF] Freifeld | HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports[END_REF]. HealthMap and ProMED both monitor a broad range of human, animal and environmental known and unknown threats. They were selected because they are two main sources of unofficial information freely accessible online.

This retrospective study focused on three specific objectives. First, we aimed to evaluate the three EBS systems on their capacity to timely detect the COVID-19 emergence in China. Second, we focused on PADI-web to understand how an animal health EBS tool contributed to the detection of a human EID. We analyzed the RSS feeds from PADI-web that detected the COVID-19-related news articles. Third, we assessed the vocabulary in the news articles detected by PADI-web and its evolution in relation to the identification of the pathogen and the spread of the disease.

As part of the MOOD team and as second author of the paper, I actively contributed to the study design particularly in terms of integrating expert knowledge on EI for human health and early detection of unknown threats. I also contributed to the manual tagging of news articles, data analysis and interpretation of results to provide solutions to improve EI activities.

Material and methods

In this study, we defined "news articles" as online media reports, whereas scientific publications are referred to as "papers".

To assess the timeliness of the three EBS systems (PADI-web, ProMED and HealthMap), we searched for COVID-19-related news articles from 1 st to 31 st December 2019. We compared the first news article detected by each EBS system in terms of publication date, language and source.

To understand how PADI-web detected news articles relating to the COVID-19 emergence, we filtered a second corpus of news articles from 31 st December 2019 to 6 th January 2020, containing at least one of the following keywords: "pneumonia", "respiratory illness", "coronavirus", "nCoV" (the first temporary name of COVID-19) and "Wuhan" in the body or the title of the news article. Following manual verification, 58 news articles were excluded from the initial set of 333 news articles because they were either duplicates or unrelated to the novel coronavirus outbreak. The final dataset contained 275 news articles.

To analyze the link between PADI-web's animal health RSS feeds and the detected news articles, we screened each news article and assigned it to one of the following two categories depending on the type of RSS feed that detected the news article: 1) disease-specific RSS feeds (containing specific disease names) and 2) syndromic RSS feeds (containing combinations of symptoms and animal hosts).

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We analyzed the evolution of the vocabulary from the initial discovery of the COVID-19 emergence to its spread outside China by extracting terms from the whole corpus referring either to the virus or the disease in the news content. We manually categorized the terms into the following categories: "mystery" (terms referring to an unknown threat), "pneumonia" (terms referring to the clinical signs), "coronavirus" (terms referring to the virus taxonomy) and "technical" (technical terms and acronyms referring to the virus) (Table 1). One news article can contain terms from several categories. We calculated the daily proportion of each category, expressed as the sum of the occurrences of the category divided by the total number of occurrences for that specific day. We merged daily counts for Saturday and Sunday to account for weekdays/week-end trends.

Main results

Out of 275 news articles relating to COVID-19 identified by PADI-web, 54.5% (n=150) were detected by syndromic RSS feeds and 45.5% (n=125) were detected by disease-specific RSS feeds.

When relating to a disease, news articles were detected because they compared COVID-19 to another disease (31.7%), because a disease was ruled out from the diagnosis (18.2%) or because they described ongoing disease outbreaks, including COVID-19 (7.7%). When relating to an animal, news articles were detected because they described animal species susceptible to coronaviruses (24.4%), because they referred to animal species present in the Chinese market, thus as a potential COVID-19 virus source (2.5%), or because they advised to avoid contact with animals (0.7%).

Before the official identification of the virus, keywords referring to clinical signs ("pneumonia") and the unknown cause of the outbreak ("mystery") were most often used to describe the emergence (Figure 13). Once the causative agent was identified, the category "coronavirus" became most used, along with the appearance of technical terms and acronyms such as "2019-nCoV".

Study discussion

This case study showcased an example of pluridisciplinary collaboration between epidemiologists, EI experts, computer scientists and text mining experts in the aim of facilitating and automatizing data collection to improve the early detection of health threats.

The results of our study showed that ProMED was the first to detect a news article relating to the COVID-19 emergence in China, just one day before the first detection by PADI-web and HealthMap and the official notification. Out of the three biosurveillance tools, ProMED is the only one to rely on a network of experts to collect and share first-hand information on health events at the local level to alert on potential health threats. This suggests that the network of local field experts is crucial for the rapid detection of EID events. PADI-web and Healthmap detected the first news article on the same day as the official notification by Chinese health authorities. This points out a 50 limitation of some biosurveillance tools, and the need to integrate and promote the inclusion of local experts in the sources of information of EBS systems.

PADI-web's disease-specific RSS feeds contributed as much as syndrome-related RSS feeds to the detection of COVID-19 news. This was unexpected but illustrates the importance of combining both specific and non-specific RSS feeds to increase the coverage of search queries. Indeed, I noticed in my experience that many media articles provide information on the epidemiological situation of more than one disease, usually a clinically-similar disease, another disease circulating in the same area or country, or a disease with an economic impact of the same magnitude (e.g. comparison of ASF and avian influenza). This "bulk" feature of media articles mentioning several outbreaks of different diseases allows EBS tools such as PADI-web to broaden the scope of their search with a limited number of RSS feeds. This key feature compared to IBS systems allows them to increase the probability and the timeliness of the detection of EID events, particularly when relating to zoonotic events.

Our results indicated that the vocabulary changed as the disease spread. The analysis of the evolution of the vocabulary during the early stages of emergence (e.g. suspicion, confirmation of etiology, international spread) illustrates the importance of specifically selecting keywords relating to "mystery" or "unknown" to detect suspicions of novel virus emergence, both in animals and humans. EBS tools used to monitor information from online media sources should, therefore, be adapted to the different stages of disease epidemiology.

PADI-web was able to detect news relating to an emergence event affecting humans through its animal-health related RSS feeds, thus demonstrating its value as a trans-sectoral tool for the timely detection of health events affecting both animals and humans, of both known and unknown origins. To promote this use in a One Health approach, PADI-web has been presented and shared in the framework of inter-sectoral collaborations with institutions involved in human health (e.g. WHO and the French network of epidemic intelligence) and in plant health (e.g. ESV Platform and INRAE). PADI-web is also part of the MOOD project which promotes the development and improvement of new and existing tools in a One Health context, in collaboration with over 40 European partners in both animal and human health.

The three EBS systems included in our study monitor information in multiple languages, which facilitates the detection of events in local media outlets. Increasing the number of languages monitored by the systems can enhance their sensitivity and improve timely detection [START_REF] Barboza | Evaluation des systèmes d'intelligence épidemiologique appliqués à la détection précoce des maladies infectieuses au niveau mondial[END_REF]. The three EBS systems are also complementary in terms of scope (animal and public health) and moderation (manual, semi-automated, automated).

In hindsight, I have identified a few ways to improve the study. Instead of manually extracting terms in our analysis of the vocabulary, we could have used text mining techniques to automate the process, such as the BioTex software which automatically extracts relevant terminology. We could, for instance, add a new category of keywords relating to unknown health events in PADIweb and analyze the news articles detected by the tool in a retrospective study of another emergence (e.g. Ebola, SARS). This would allow us to validate and refine our findings relating to the vocabulary linked to novel pathogen emergence (e.g. "unknown", "mystery").

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PADI-web offers a strong coverage of the animal sector, a multilingual approach and an efficient and automated information extraction module compared to other biosurveillance tools.

Multilingual approaches are uncommon (only 32% of reviewed biosurveillance tools are multilingual) and increase the amount of information collected, thus improving early detection of an event by including local media sources [START_REF] O'shea | Digital disease detection: A systematic review of event-based internet biosurveillance systems[END_REF]. Previous studies have also shown that PADI-web detects information on health events with a wider thematic scope, e.g. the detection of information relating to control measures such as vaccination [START_REF] Arsevska | Élaboration d'une méthode semi-automatique pour l'identification et le traitement des signaux d'émergence pour la veille internationale sur les maladies animales infectieuses[END_REF].

Through this case study, PADI-web illustrated the key contribution of unstructured data extracted from news articles. However, several challenges of managing unstructured data have been discussed, such as user bias relating to human moderation, the need for an annotated training dataset for machine learning algorithms, determining suitable baselines to detect anomalies in disease trends, or selecting pertinent keyword associations for online queries (Yan et al., 2017a).

Although digital disease detection improves the timeliness and sensitivity of disease surveillance systems, the use of informal sources is likely to generate bias (Yan et al., 2017b). The information extracted from these unofficial sources should be verified and validated by experts before analysis, hence the need for pluridisciplinary collaborations. Because biosurveillance tools collect information from many common sources (e.g. social media, Google News) and sometimes collect information from one another (e.g. ProMED and HealthMap mutually), data collection using internet-based biosurveillance tools can create duplicates and overlap in the collected information [START_REF] O'shea | Digital disease detection: A systematic review of event-based internet biosurveillance systems[END_REF]. This highlights the need to improve the detection of information duplicates when combining data collected from different tools and heterogeneous sources.

Although the first news article detected usually contains limited, preliminary and vague information, biosurveillance tools provide a first warning for the emergence of new and unknown pathogens. Biosurveillance tools rely on syndrome keywords to detect such threats and, although there might be some level of noise regarding the collected information due to the limited specificity of syndromes, these tools are the first line of detection of new disease emergences. This supports the need to integrate weak signals in biosurveillance tools to increase the sensitivity of EBS systems and better detect the early stages of disease emergence, even when available information is limited. For this reason, I have led the integration of PADI-web into the FEIS' EI activities on an ad hoc basis to increase the coverage of the information monitored. We plan to integrate PADI-web on a permanent basis to go beyond ad hoc use, but this will generate more information to validate and analyze on a daily basis and might require additional tool adaptation, planning and resources. The level of additional resources and time needed for the integration of EBS biosurveillance tools in existing EI systems depends on whether these tools complement the monitoring of official sources (e.g. FAO or European commission databases) or if the EI system solely relies on these biosurveillance tools, which may be the case if limited official notification data is available.

Existing biosurveillance tools display complementary features relating to data collection, languages, moderation, extraction and dissemination of information (Yan et al., 2017a).

Combining them can increase the sensitivity and timeliness of disease surveillance systems, from data collection to reporting [START_REF] Barboza | Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events[END_REF]. This was the idea behind the Epidemic Intelligence from Open Sources (EIOS) platform [START_REF] Abdelmalik | The Epidemic Intelligence from Open Sources initiative: a collaboration to harmonize and standardize early detection and epidemic intelligence among public health organizations[END_REF]. The EIOS initiative is a unique collaboration between WHO, the Global Outbreak, Alert and Response Network (GOARN), 52 FAO, OIE, the Global Health Security Initiative (GHSI), ECDC, Africa CDC and the Joint Research Centre of the European Commission. EIOS aims to create a common, all-hazards, One Health platform by using open source information for the early detection, analysis and reporting of health threats [START_REF] Abdelmalik | The Epidemic Intelligence from Open Sources initiative: a collaboration to harmonize and standardize early detection and epidemic intelligence among public health organizations[END_REF]. The initiative brings together end-users (health professionals working in EI), system developers, data providers and stakeholders to mutualize expertise, efforts and resources, in order to combine existing developments and improve systems' performance in terms of early detection and monitoring of threats.

The combination of complementary tools has also been implemented in PADI-web, with the integration of complementary modules such as EpidVis and EpidNews. PADI-web is the product of several years of pluridisciplinary collaborations between experts in computer sciences, visual analytics, animal health, EI and epidemiology. This co-conception of the tool ensured the developments met the end-user needs in terms of online media monitoring to complement unofficial sources of information monitored by the FEIS. As stated, expert input is essential for text-mining and machine learning algorithms, which make up PADI-web. For this reason, it was important for me to actively contribute to each work meeting and pluridisciplinary group discussions with experts in data science and text mining, as part of the PADI-web team, to provide my expertise in EI and my operational needs as a main end-user. During the early stages of development of PADI-web, a Delphi consensus method was used to facilitate discussions between experts in order to agree on class definitions and keyword selection, a potentially challenging task given the different experiences and perspectives of participants [START_REF] Rayens | Building Consensus Using the Policy Delphi Method[END_REF]Arsevska et al., 2016).

It is important to integrate end-users in the early stages of development to ensure the tool meets their needs. The need for the tool itself should also be validated by end-users as a first step of the project, through an in-depth assessment of user needs prior to the launching of the project. End users involved in the development of the tool should be representative of all end-users. As the development advances, additional key stakeholders can be involved in the process through iterative feedback loops (as is planned in the H2020 MOOD project). These feedback loops can extend beyond the development phase and continue during the tool's implementation to ensure it can adapt to the evolution of the users' needs. This might be at the source of some issues we have faced with PADI-web's implementation beyond ad hoc use. Unfortunately, we noticed that the tool does not meet some specific needs of the FEIS end-users such as a concise and synthetized view of all collected news articles, allowing a rapid overview of all news articles. Such specific needs are operational and could have been identified during feedback and testing loops during and following the tool's development. Today, the implementation of the PADI-web in the FEIS' daily activities is delayed due to the need for technical adjustments to address these operational needs. Also, the development of the tool was conducted in parallel to the FEIS which was still in the process of being set up and implemented when the development of PADI-web was underway. Both were launched in 2013. Some operational needs might not have been identified during the tool's design simply because they had not yet been identified by the FEIS members at the time. The tool's development process, in collaboration with FEIS members, might have been different if the project had been launched once the FEIS was operational, mature and able to provide operational feedback on specific needs.

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In retrospect and with the knowledge acquired during my experience, I have identified several ways to improve the development and facilitate the implementation of newly developed tools, such as PADI-web, in existing surveillance systems. Including a representative group of end users in the design of the tool can help identify key operational needs to make sure the tool fits the needs of the users. Identifying a facilitator, preferably an end-user, is also key to further facilitate and drive the process. I also suggest to organize feedback meetings with additional end users at different stages of the process. These meetings would allow the tool development team to identify addition operational needs by progressively including more and more end-users in an iterative process, to validate the tool's design and to organize testing loops with the future users. This approach requires more time at the development phase but will save time during the implementation phase. We have chosen such an approach for the H2020 MOOD project, with the help of sociologists to facilitate this co-conception with end-users.

Development of an online data collection tool for mass gathering surveillance

The second case study is another example of the pluridisciplinary co-development of an electronic tool to improve disease surveillance. The objective of this case study was to develop and implement an online tool for electronic data collection and analysis of health data to enhance disease surveillance in the context of mass gathering events in the Pacific region, in this case the 8 th Micronesian Games hosted by the Federated States of Micronesia in 2014 [START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF].

Context of mass gatherings

WHO defines mass gatherings as "events attended by a sufficient number of people to strain the planning and response resources of a community, state or nation" (World Health Organization, 2008a). Mass gatherings can be recurrent or sporadic. These events can be for example religious (e.g. the Hajj), athletic (e.g. the Olympics) or socio-cultural (e.g. the Festival of Pacific Arts) (Figure 14). They should be distinguished from humanitarian emergencies because mass gatherings are non-emergency events that allow sufficient time to prepare for the events [START_REF] Kaiser | Epidemic intelligence during mass gatherings[END_REF].

These events are characterized by a large attendance, although specific size is not linked to the definition because the capacity of local communities and health services to manage large crowds varies from one region to another (World Health Organization, 2015). Their size may vary from several thousand people to several million people such as the 3.2 million spectators of the "Fédération Internationale de Football" Association World Cup in Germany in 2006 [START_REF] Josephsen | Preparations for infectious disease surveillance during the football World Cup tournament, Germany 2006[END_REF].

54 Through the favorable context they provide, mass gatherings can represent serious health risks [START_REF] Lombardo | Public health surveillance for mass gatherings[END_REF]. Public health risks associated with mass gatherings are well documented and encompass a variety of focus areas, from environmental health hazards to infectious diseases [START_REF] Milsten | Mass-Gathering Medical Care: A Review of the Literature[END_REF][START_REF] Arbon | Mass-Gathering Medicine: A Review of the Evidence and Future Directions for Research[END_REF]. Crowding favors the emergence of infectious diseases through increased contacts between participants. The temporarily increased population density at the location of the event can exacerbate an on-going outbreak in the host country and strain the planning and response resources of the country or community. In addition, by involving international participants, mass gatherings represent a threat of importation of new diseases into the host countries from participants' countries upon their arrival, or the exportation of a disease worldwide when infected participants return to their home country. An important lesson learnt from past epidemics like SARS in 2003 was that weaknesses in "surveillance and response capacity in a single country can endanger national populations and the public health security of the entire world" [START_REF] Heymann | Global surveillance, national surveillance, and SARS[END_REF].

The hosting country's health agency must be prepared to enhance their surveillance capacity to detect, investigate and respond to an infectious disease outbreak that could damage the reputation of the event, spread to other regions or countries, and exert a substantial human or economic impact. Enhancing surveillance implies increasing the timeliness and accuracy of existing surveillance capacities and potentially implementing additional active surveillance systems [START_REF] Fleischauer | Enhancing Surveillance for Mass Gatherings: The Role of Syndromic Surveillance[END_REF].

Preparing public health systems for managing a mass gathering is a complex procedure. In accordance with IHR, WHO offers guidance for public health planning, surveillance and response during mass gatherings (World Health Organization, 2008b, 2015). To enhance existing surveillance systems, thorough risk assessments for participating countries and host country are essential prior to the event in order to identify potential public health risks and available resources (World Health Organization, 2015). As part of the medical service preparations, health authorities often implement an enhanced syndromic surveillance system [START_REF] Kaiser | Epidemic intelligence during mass gatherings[END_REF]. The enhancement of the surveillance system focuses on capacity for diagnostics (e.g. available human and material resources), control (e.g. investigation, isolation, treatment) and prevention (e.g. awareness, vaccination).

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The majority of outbreaks that occur at mass gatherings involve vaccine-preventable diseases, mainly influenza, measles, mumps and hepatitis A [START_REF] Gautret | Communicable diseases as health risks at mass gatherings other than Hajj: what is the evidence?[END_REF]. Several outbreaks during mass gatherings led to international spread of communicable diseases, with secondary and sometimes tertiary cases reported in a participant's country. For example, a measles outbreak during two sport competitions in Italy in 2011 led to secondary cases in Germany and Slovenia [START_REF] Santibanez | Measles virus spread initiated at international mass gatherings in Europe, 2011[END_REF], and another measles outbreak during a mass gathering in Taizé, France in 2010 led to secondary and tertiary cases reported in Germany [START_REF] Pfaff | Spotlight on measles 2010: Measles outbreak among travellers returning from a mass gathering, Germany, September to October 2010[END_REF]. Another example is the introduction of dengue serotype 4 in American Samoa in 2008 during its hosting of the Festival of Pacific Arts (Institut de Veille Sanitaire, 2012).

Disease surveillance in the Pacific

The Pacific region covers about a third of the Earth's surface and counts more than 3 000 islands in 22 Pacific island countries and territories (PICTs), which include some of the world's smallest nations with under 25 000 residents (Kool et al., 2012a). PICTs are culturally diverse and some can be very geographically isolated.

Rates of infectious diseases are high in the Pacific region [START_REF] Gani | Some aspects of communicable and non-communicable diseases in pacific island countries[END_REF]. PICTs are characterized by insularity, relative remoteness, significant mobility both within islands and outside the region, which make them particularly vulnerable to disease outbreak and spread [START_REF] Souarès | Telehealth and outbreak prevention and control: the foundations and advances of the Pacific Public Health Surveillance Network[END_REF][START_REF] Nelesone | Strengthening sub-national communicable disease surveillance in a remote Pacific Island country by adapting a successful African outbreak surveillance model[END_REF]. In addition to a vulnerable context increasing the risk of disease outbreaks, outbreak detection and response in PICTs are constrained by several factors including limited health infrastructures and communication networks, limited availability of trained public health staff and many notifiable conditions for which overseas laboratory confirmation is required, which can result in poor reporting [START_REF] Souarès | Telehealth and outbreak prevention and control: the foundations and advances of the Pacific Public Health Surveillance Network[END_REF]Kool et al., 2012b). These factors can overwhelm health services (often only one person) and weaken their early warning and response capacities, resulting in poor timeliness, completeness and sustainability of surveillance systems.

Syndromic surveillance has been successfully implemented in developing countries in Africa and Asia [START_REF] Durrheim | The use of hospital-based nurses for the surveillance of potential disease outbreaks[END_REF][START_REF] John | Communicable diseases monitored by disease surveillance in Kottayam district, Kerala state, India[END_REF]. Case definitions are based on clinical signs and symptoms rather than laboratory confirmation, making it particularly useful in settings with limited access to laboratories, a common feature in most PICTs. The gain in timeliness provided by a syndromic system is substantial and can lead to rapid response and limited disease spread [START_REF] Nelesone | Strengthening sub-national communicable disease surveillance in a remote Pacific Island country by adapting a successful African outbreak surveillance model[END_REF].

The SPC (originally Secretariat of the Pacific Community) is an international development organization governed by its 26 members, which include 22 PICTs, Australia, France, New Zealand and the United States of America. SPC aims to facilitate the sharing of technical experience and knowledge, and provides support to its members to implement specific development projects and activities. SPC's expertise is structured into nine divisions, including the Public Health Division (PHD). The Research, Evidence and Information Program (REI), which is part of the PHD, aims to provide support to its PICT members in terms of infectious disease surveillance. The REI team coordinates regional EI activities to provide up-to-date information on the circulation of infectious diseases in the region. The team also coordinates the Pacific Public Health Surveillance Network (PPHSN), created in 1996 under the auspices of WHO and SPC. The PPHSN is a voluntary network of countries and organizations dedicated to the promotion of public health surveillance and appropriate response to the health challenges of 22 PICTs. To improve public health surveillance and response in the Pacific in a sustainable way, the network aims to harmonize health data needs and surveillance, develop supportive computer applications, train national staff in epidemiology and public health surveillance, promote the sharing of information between PICTs, publish regular bulletins to disseminate health information, and improve laboratory capacity and connectivity. PPHSN services include an email list to promote timely exchange of information among over 680 health professionals (PacNet), and a diagnostic support network working on the identification and verification of pathogens (LabNet).

In 2010, the Pacific Syndromic Surveillance System (PSSS) was introduced in the PPHSN to simplify disease surveillance and improve early warning for the main outbreak-prone diseases in the Pacific. The PSSS focuses on four core syndromes defined according to standardized case definitions: acute fever and rash (AFR), diarrhea, influenza-like-illness (ILI) and prolonged fever (Table 2).

The PSSS complements existing surveillance systems in terms of early warning and builds on existing data collection, reporting and response procedures (Kool et al., 2012b). It does not replace laboratory confirmation, still needed for major outbreak-prone diseases. The PSSS provides an efficient early warning system for many PICTs, enabling them to achieve several key capacities required by the 2005 IHR (World Health Organization, 2008b).

Table 2. Case definitions for the four identified syndromes of the Pacific Syndromic Surveillance System (fever: 38°C or higher) (Kool et al., 2012). Since 2012, SPC has implemented a mass gathering program to support PICTs in enhancing their disease surveillance system. It aims to limit the risk of infectious disease outbreaks during mass gatherings. Enhanced syndromic surveillance (ESS) has three primary objectives: 1) to provide a simple disease surveillance system allowing timely detection and response of disease events, 2) to provide daily dissemination of situation reports to national and regional health authorities and stakeholders, and 3) to improve the existing disease surveillance system in a sustainable manner [START_REF] White | Enhanced surveillance for the Third United Nations Conference on Small Island Developing States, Apia, Samoa, September 2014[END_REF]. ESS involves increasing the number of syndromes and sentinel sites monitored, switching from weekly to daily reporting, and going from aggregated data to casebased reporting [START_REF] Hoy | Enhanced syndromic surveillance for mass gatherings in the Pacific: a case study of the 11th Festival of Pacific Arts in Solomon Islands, 2012[END_REF]. ESS is increasingly implemented for mass gatherings, although it has generally been limited to high-income countries [START_REF] Nelesone | Strengthening sub-national communicable disease surveillance in a remote Pacific Island country by adapting a successful African outbreak surveillance model[END_REF][START_REF] Lombardo | Public health surveillance for mass gatherings[END_REF]. In low to medium-income countries, establishing ESS for mass gatherings can provide a great opportunity to strengthen health system capacity in a sustainable way, building from and strengthening the existing surveillance system [START_REF] Henning | What is Syndromic Surveillance?[END_REF].

Syndrome

Electronic disease surveillance

Over the past 15 years, there have been great improvements in the collection, analysis, visualization, and reporting of health data, mainly driven by advances in information technology [START_REF] Feighner | SAGES overview: Open-source software tools for electronic disease surveillance in resource-limited settings[END_REF]. Incorporating technological advances significantly increased the timeliness of data collection, analysis and reporting, and allowed more complex and automated analyses of disease data, thus facilitating early detection, preparedness and rapid control of disease outbreaks.

Electronic disease surveillance can improve health security by reducing delays in data availability and analysis, and improving early detection and reporting [START_REF] Jajosky | Evaluation of reporting timeliness of public health surveillance systems for infectious diseases[END_REF][START_REF] Chretien | Syndromic Surveillance: Adapting Innovations to Developing Settings[END_REF][START_REF] Soto | Challenges in the implementation of an electronic surveillance system in a resourcelimited setting: Alerta, in Peru[END_REF]. Countries can, thereby, enhance their compliance with the WHO's 2005 IHRs which require strengthening disease detection and response capacities in order to report, within 24 hours of assessment, any public health event of international concern (World Health Organization, 2008b). An electronic disease surveillance system can address a number of pertinent objectives, including: 1) detection of disease outbreaks, 2) monitoring trends in disease rates or the effectiveness of an intervention, and 3) strengthening local capacity in outbreak detection and response [START_REF] Henning | What is Syndromic Surveillance?[END_REF].

Electronic disease surveillance, particularly electronic syndromic surveillance, holds promise to improve health security in resource-limited environments [START_REF] Chretien | Syndromic Surveillance: Adapting Innovations to Developing Settings[END_REF][START_REF] Soto | Challenges in the implementation of an electronic surveillance system in a resourcelimited setting: Alerta, in Peru[END_REF].

Resource-limited countries have lagged behind due to challenges with information infrastructures and available resources. In developing countries, epidemiological surveillance is usually led by the Ministry of Health, which often still relies on traditional pen and paper methods for disease reporting [START_REF] Soto | Challenges in the implementation of an electronic surveillance system in a resourcelimited setting: Alerta, in Peru[END_REF]. In developing countries where public health infrastructure can sometimes be suboptimum, efforts are in progress to implement robust data collection and analysis methods and improve local capacity for electronic disease surveillance [START_REF] Chretien | Syndromic Surveillance: Adapting Innovations to Developing Settings[END_REF]. When considering available information and communication technologies, it is essential to select technologies that are both easy to incorporate into existing health systems and sustainable with little or no additional financial investment, thus ensuring proper implementation and sustained use [START_REF] Feighner | SAGES overview: Open-source software tools for electronic disease surveillance in resource-limited settings[END_REF].

SAGES tool

SAGES (Suite for Automated Global Electronic bioSurveillance), developed in 2008 by the Johns Hopkins University Applied Physics Laboratory (JHU-APL), provides an inexpensive, customized, and sustainable disease surveillance system that can complement and strengthen existing systems. SAGES is a collection of modular, flexible, freely-available software tools designed to enhance electronic disease surveillance capacity in resource-limited settings [START_REF] Lewis | SAGES: A Suite of Freely-Available Software Tools for Electronic Disease Surveillance in Resource-Limited Settings[END_REF][START_REF] Feighner | SAGES overview: Open-source software tools for electronic disease surveillance in resource-limited settings[END_REF]. SAGES tools are organized in four categories: 1) data collection, 2) analysis and visualization, 3) communications, and 4) modelling, simulation and evaluation. They are built in a modular nature, which allows each locality or country to select all or specific tools from SAGES on the basis of their needs, capabilities and existing systems to create a customized electronic disease surveillance system [START_REF] Feighner | SAGES overview: Open-source software tools for electronic disease surveillance in resource-limited settings[END_REF].

The SAGES analysis and visualization tools are built on the features and functionality of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system. This system was previously developed by JUH-APL in collaboration with the United States Department of Defense [START_REF] Lewis | SAGES: A Suite of Freely-Available Software Tools for Electronic Disease Surveillance in Resource-Limited Settings[END_REF]. ESSENCE allows real-time collection, analysis and visualization of non-traditional data sources (e.g. poison control center calls, pharmaceutical sales) leading to the timely detection and reporting of anomalies in disease activity within a community [START_REF] Lombardo | A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II)[END_REF]. The current SAGES initiative leverages the experience gained with ESSENCE and its analysis and visualization components are built with the same features.

One of the most successful tools of the SAGES toolbox is OpenESSENCE (OE), a multi-user webbased tool for data entry, analysis and visualization that enables epidemiologists to monitor the population's health [START_REF] Feighner | SAGES overview: Open-source software tools for electronic disease surveillance in resource-limited settings[END_REF]. SAGES OE provides a web-based data collection and analysis platform that automatizes key steps of reporting such as generating time series or detecting threshold crossing, thus allowing timely data analysis and reporting, which are crucial for enhanced surveillance.

A collaboration was set up between SPC and JHU-APL to adapt OE to the context of disease surveillance enhancement for mass gatherings in the Pacific. SAGES' OE tool was successfully implemented for the 8 th Micronesian Games which I will present as a case study in this next section (Hoy et al., 2015;[START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF]. From 19 th to 29 th July 2014, Pohnpei hosted the 8 th Micronesian Games (or Micro Games) which gathered participants from seven Micronesian island countries and territories: Kiribati, Guam, Marshall Islands, Nauru, the Commonwealth of the Northern Mariana Islands, Palau and FSM (Figure 16) [START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF]. The event took place in the area around the main town of Kolonia and attracted about 1 700 athletes and officials. The event was held two months after a widespread measles outbreak in Pohnpei, which presented a risk of spread across FSM and the Pacific region following the mass gathering.

Application of SAGES to enhance disease surveillance during the Micronesian Games in the

During my experience as a public health surveillance officer at SPC from 2014 to 2016, I participated in the REI team's projects, which included the mass gathering enhancement program. Similar to the PADI-web case study, I contributed on two levels to the adaptation of OE to mass gathering surveillance: a meta level to integrate user knowledge, and a methodological level to contribute to the technical and methodological developments of the tool. As part of a pluridisciplinary team of experts in computer sciences, epidemiology, epidemic intelligence and public health, I contributed to the integration of expert knowledge on the EI process in the tool's design and implementation. I also participated in methodological developments of the tool to adapt it to the local context, thus ensuring it fits the needs of local health authorities. I contributed to the analysis and reporting of the data collected daily, provided back-office support when In addition to my contribution to the surveillance enhancement for the Micro Games in FSM, I also participated in SPC's support for another mass gathering event that occurred in 2014, just two months after the Micro Games: the 3 rd United Nations SIDS Conference hosted in Samoa. The SIDS conference took place at the same time as the Teuila festival also organized in Samoa, while a chikungunya outbreak was reported locally and the largest Ebola outbreak was ongoing in Western Africa. SPC implemented the same program for mass gathering enhanced surveillance. We had initially planned to use the SAGES OE tool. However, we were not able to use the tool due to technical challenges in hosting the tool server in Samoa and the country's worries about data confidentiality with cloud hosting. We had to adapt by creating an Excel spreadsheet system with programmed threshold alerts, data analysis and visualization, to which I contributed. I traveled to Apia, Samoa two weeks prior to the event to meet health authorities and health professionals at the different sentinel sites (existing and newly set-up for the mass gathering), to train the staff on the use of the spreadsheet tool, as well as the creation and publication of daily situation reports to inform stakeholders. I provided support for the enhancement of the surveillance system to all sentinel sites, then to a daily basis instead of weekly, with test runs implemented. Upon my return to SPC offices, I provided back-office support for the dissemination of the daily situation reports.

Material and methods

SPC's program to enhance disease surveillance during mass gatherings consists of three steps that focus on preparation, operational implementation and sustainability.

The first phase of the mass gathering surveillance process is preparation. Health authorities of the hosting country or territory contact SPC prior to the mass gathering to ask for support in strengthening the island's disease surveillance system. This step is formalized by a signed agreement between SPC and the hosting country or territory. This contact is ideally established at least a year before the event, though it is usually done a few months before in practice.

The next step of the protocol for disease surveillance enhancement is to assess the existing disease surveillance system in the hosting country or territory. Most of the existing surveillance systems are based on weekly reporting, but need to be enhanced to daily reporting for mass gathering surveillance in order to ensure timely detection of outbreaks, i.e. unusual increase in syndrome cases, and rapid response. The team also conducts a disease risk assessment by monitoring ongoing disease circulation in the hosting country to identify risks of disease exportation, and in the participant's country of origin to identify risks of disease introduction in the hosting country.

A work plan is developed based on these findings.

A surveillance tool is then developed based on the needs of the national health authorities. We can start from an existing tool, if they are already using one, that can fit the need for enhanced surveillance with daily reporting. The REI team adapted the OE tool which can easily be applied to different mass gathering contexts.

Following the preparation phase, operational implementation is carried out, starting ideally at least six months prior to the event in order to allow time to test the tool and the enhanced system, to train the staff, and to plan for necessary resources (e.g. cars, phones, office supplies). The training delivered to health authorities and sentinel site staff focused on the use of the OE tool, collection and referral of laboratory samples, completion of the surveillance registers, and comprehension of the case definitions.

The enhancement of the system is performed in two ways: by increasing the number of sentinel sites and syndromes, and by increasing the reporting frequency. First, the number of sentinel sites is increased to increase the geographic coverage of the island. Then, the frequency is increased from weekly to daily for the collection, analysis and reporting of the health data. The enhancement is done progressively to limit the strain on the existing system, with a first expansion to all sentinel sites, followed by an expansion to daily reports.

A surveillance form available at each sentinel site was filled in manually and captured the daily number of patient encounters (or visits) and syndrome cases (Annex 4). One encounter can be counted as one or more syndromes depending on the patient's symptoms. The paper surveillance form was collected and replaced with a new form daily. The data from these forms was manually entered into the SAGES OE tool (Figure 17). Unfortunately, data collection could not be directly entered at sentinel sites due to limited connectivity coverage across the area, although the SPC team had envisaged to use wireless tablets. This demonstrates the necessary flexibility of the system which needs to be adapted to each specific context for each mass gathering. The data was analyzed in SAGES OE, which provided time series to monitor the trends in syndrome cases and were used for the daily situation reports. These reports were disseminated daily to stakeholders in the Department of Health and Social Affairs at national level and to the Games organizing committee. They were also posted on PacNet. The enhanced surveillance system usually starts a week before the event to better determine threshold baselines and pilot the new system and tool in real conditions. It continues one to two weeks after the event, to take into account incubation periods and delays due to detection or patients visiting a doctor.

The REI team also provided support to health authorities for investigation measures, once an unusual increase (above a predefined threshold) of syndrome cases was detected.

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The third and final stage focuses on sustainability. Mass gatherings represent an opportunity to strengthen local health surveillance and response capacities in a sustainable manner, by training staff or setting up new sentinel sites for example. Following an evaluation of the enhanced surveillance phase to identify strong points and weaknesses of the system, the system can transition back to the existing system by returning to the usual number of sentinel sites and syndromes, and return to weekly reporting. The system can also be enhanced by, for instance, increasing the number of usual sentinel sites or syndromes, or promoting evidence-based decision-making to influence policy-making. Post-event surveillance plays a key role in this process as it accompanies the country in implementation of event-related efforts into routine surveillance and response activities.

Figure 18 illustrates all the steps of the surveillance enhancement process.

Figure 18. SPC's process map for the enhancement of disease surveillance in the framework of mass gatherings [START_REF] White | Enhanced surveillance for the Third United Nations Conference on Small Island Developing States, Apia, Samoa, September 2014[END_REF].

Several country visits are needed in order to better understand the local system and the stakeholders involved, train the staff and provide support during the mass gathering and after the event, if needed, in order to sustainably improve the national surveillance system.

For the 8 th Micro Games in FSM, the number of sentinel sites was expanded to 11 to include two Game venues. The number of syndromes was increased from five to eight (Table 3). The enhanced surveillance system was implemented two days before the Games, due to short notice before the event, and continued for a week after the event.

Main results

During the 8 th Micronesian Games, 408 syndrome cases were reported during the 21 days of enhanced surveillance, which represented on average 7% of encounters (patient visits). ILI was the most frequent syndrome recorded (55%) followed by watery diarrhea (24%), non-watery diarrhea (9%) and AFR (8%). Watery diarrhea and AFR are indicative of more severe diseases (Table 3), and a rapid investigation was set up by the response team.

The proportion of syndrome cases for rural and urban sentinel sites is shown in Figure 19. We can notice a variation in the distribution of syndrome cases between rural sites (with more diarrhea cases) and urban sites (with more ILI and AFR cases).

The measles outbreak that was ongoing in Pohnpei at the time of the event had no detrimental impact on the mass gathering. [START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF].

A total of 18 daily situation reports were produced, and were well received by health professionals through PPHSN's PacNet network [START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF]. OE was rapidly adapted within a couple of weeks to fit the needs of the event and hosting country prior to the event. The tool was well received by local health authorities and provided a useful web application for data entry, storage, analysis and reporting, partly due to simultaneous multi-user access and cloud storage (

Study discussion

The study revealed that the enhancement of the sentinel sites increased the coverage and the sensitivity of disease surveillance, allowing us to identify a disparity in the distribution of syndrome cases between rural and urban environments (Figure 19). Greater burden of ILI cases in urban sites might be linked to greater respiratory transmission in highly populated areas, whereas higher rates of diarrhea cases in rural areas might indicate issues with waste water treatment and access to clean water. Also, the Games-specific sentinel sites had the third highest rate of syndrome cases but only 3% of encounter cases (visits). This rate of syndromes was much higher than for Pohnpei State Hospital which had the largest number of encounters but fewer syndrome cases. These results illustrate the need to adapt surveillance strategies to the mass gathering event and ensure good coverage of both rural and urban sites. They can help identify gaps in public health surveillance and help local authorities design adapted, prioritized and targeted public health interventions.

The results of the study allowed us to identify issues with the distribution of laboratory samples which was unequal compared to syndromic activity (Figure 20). This strengthens the need for training prior to the event for all actors involved at local level, including clinicians, sentinel site personnel and laboratory personnel.

In our study, electronic surveillance technologies played a significant role in facilitating the enhancement of surveillance activities in-country. The use of OE greatly improved the timeliness of data collection, analysis and reporting, providing reassurance to the event organizers that infectious diseases would not impact the event's activities. The results of the study showed that, despite an ongoing measles outbreak in the hosting country, no infectious disease spread threatened the health of the event's participants.

The efficient implementation of an enhanced syndromic surveillance system can face some challenges. The case study of the Micro Games emphasized the need for good planning, preparation and testing prior to the event, which requires a sufficiently long pilot phase. Unfortunately, health authorities often ask for SPC's assistance only a few weeks before the mass gathering, which significantly reduces the time for the adaptation and the testing of the available tools. Perhaps SPC should proactively contact PICTs who have planned upcoming mass gatherings to propose an enhancement plan if they would like regional assistance. Also, organizing regional workshops with PICT health authorities to present the enhancement program and showcase examples and feedback of previous events might help increase awareness in terms of the time needed to efficiently implement such a program. Several challenges may arise from a limited pilot phase, such as system functions, availability of resources or organizational challenges during the 66 beginning of the event. Some examples from my experience include the lack of available computers, the roles of local public health agents not being clearly defined ahead of time, or the vehicle of the Ministry of Health not being available the morning we had to make our rounds to collect surveillance forms.

Training of staff ensures a clear understanding of the case definitions and procedures of the newly enhanced surveillance system. The high sensitivity needed for the system to detect anomalies in syndrome trends requires high quality, timely and representative data. This underlines the importance of clearly determining syndrome thresholds in close collaboration with local health authorities, as we had done for the Micronesian Games in the weeks prior to the event. Ensuring a sufficiently long pilot phase can help better define syndrome threshold baselines which may vary according to the time period and the location. Also, although similar methods are implemented and we can learn from past events, experiences of mass gatherings in routinely changing locations and seasons might not be transferable due to the significant variability linked to the context of each event (e.g. location, timing, global distribution of participants). As emphasized by the SIDS conference experience, data confidentiality and ownership issues (particularly relating to health data) should be discussed and addressed with local health authorities during the pilot phase in order to assess the possibility of cloud hosting. This stresses the need to adapt the tool and procedure to the new mass gathering event and its local context.

In hindsight, I have identified several potential improvements to facilitate the implementation of the tool in the local surveillance system. The study could have benefited from an entirelyelectronic data collection process, without relying on paper forms to collect the data. This, however, would require improved internet and cellphone coverage at all sentinel sites. Perhaps, instead of implementing a direct data collection into the electronic system through computers at each sentinel sites, we could design an electronic data collection system using mobile phones, ideally through an application, or -if not possible -by sending a text message to a centralized unit. This would lead to a significant time gain because the rounds to collect paper forms would often take all morning. In addition to organizing regional workshops on mass gathering surveillance enhancement as mentioned above, I insist once again on the need for a sufficiently long pilot phase and a possible proactive approach to contact health authorities six months to a year before the event. This would allow a thorough assessment of the local context to adapt the tool to the needs of the local authorities, and an assessment of available resources sufficiently long in advance to allow for the funding and purchase of additional equipment if necessary. The SPC team in charge of providing support to local health authorities could also include public health personnel from another PICT health agency who were involved in their own country or territory's mass gathering event, in addition to SPC staff. Along with the suggestion of a regional workshop, this regional team roster promotes inter-PICT collaborations and strengthens regional enhanced surveillance capacity.

The enhanced surveillance of mass gatherings presents an opportunity to strengthen the disease surveillance system once the event is finished by implementing long-term enhancements through the sustainability plan elaborated with health authorities, as was the case in the framework of the Micronesian Games. The training of health professionals in governmental agencies, laboratories and sentinel points can sustainably improve awareness of circulating disease threats but also of 67 other potential emerging pathogens circulating at regional or global level. Mass gathering surveillance enhancements can also improve communication between different stakeholders, not only during the event but also after. Instead of progressively returning to the number of sentinel sites and syndromes used prior to the event, local health authorities can, for instance, decide to keep an increased number of sites or syndromes depending on results from the mass gathering enhanced surveillance to improve surveillance coverage. This transition from mass gathering surveillance improvements to routine surveillance requires adequate resources because, as mentioned, enhancing disease surveillance can further strain the system particularly in resourcelimited settings. But this can be part of the local health authorities' strategy to sustainably improve disease surveillance at local and national level.

This case study showcased an example of pluridisciplinary collaboration between epidemiologists, EI experts, microbiologists, computer scientists as well as local health authorities and stakeholders, to facilitate data collection and reporting in the context of mass gatherings and improve local early detection and alerting capacities in a sustainable manner.

As mass gatherings occur over a short period of time but require intense activity, they can overburden public health systems, particularly in developing countries. Enhancing surveillance by building on existing systems can help minimize this strain on staff and resources. It can also facilitate the transition phase back to routine surveillance and enable sustainable improvements in terms of capacity (e.g. training of staff, improvements of surveillance system functions). This requires both the involvement of local health authorities to adapt the tool to the local context and ensure it fits the needs of users, and strengthened communication between all stakeholders including clinicians, surveillance officers and disease control authorities. Strengthening the link with local laboratories for sample testing and with clinicians for sampling of syndrome cases is also crucial to ensure rapid alerting of health authorities and timely investigation and response.

Given the large attendance of participants from many different countries, international collaborations and partnerships are increasingly necessary in order to prevent the introduction and international spread of infectious diseases between the host country and participating countries [START_REF] Memish | Mass gatherings medicine: international cooperation and progress[END_REF]. Indeed, mass gatherings can be seen as a global-to-local-to-global risk. Health threats can be imported in the hosting country from participants' countries, these health threats can spread during the event, and be exported to other countries worldwide upon the participants' return. Strengthening the links with health authorities in the participants' countries enables follow-up of the participants' health to detect if they contracted any disease while participating and, if so, to alert other participating nations.

Section discussion

The PADI-web case study presented an example of tool development to improve and automatize data collection from online media sources based on innovative text mining and machine learning techniques. This tool allows the collection and monitoring of unstructured data from online media sources, which represent an additional valuable source of information for EI activities.

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The OE case study presented an example of tool development to improve data collection and analysis for reporting. Automatizing data analysis by automatically updating and exporting time series and maps of syndromes once the new data is entered in the system saves time for reporting. This is particularly valuable when data collection, analysis and reporting are done on a daily basis.

In these two cases studies, the automatization process focuses on different steps of the EI process.

For PADI-web, the automatization process lies in the data identification, collection and extraction.

For OE, the automatization process focuses on the analysis of the information through visualization (e.g. creating time series and graphs). For OE, the data collection process could also be improved through direct electronic data entry at each sentinel site if sufficient internet access or phone network coverage could allow the implementation of the tool on computers or the use of wireless tablets. This would help avoid the intermediary use of paper surveillance forms which duplicates data entry but is used as a back-up system in resource-limited settings. Although they are applied to two different contexts with different needs, these two automatization processes could be combined as modules in a common platform of automated enhanced disease surveillance, providing information from EBS (e.g. online media) and IBS (e.g. sentinels sites and clinicians).

Including multiple information sources has been shown to improve the detection of signals and, with increasing volumes of information available online, automatization facilitates the processing of this significant quantity of information. However, human moderation remains essential for validation, review and interpretation of health-related events (Yan et al., 2017b;[START_REF] Wilburn | Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?[END_REF]. The automatization of data collection and analysis should therefore complement and support health professionals instead of replacing their input in the EI process. This is particularly important when some might consider that a 'click-button' tool is enough to conduct EI activities, when it is clearly not the case. Tools help improve EI systems but these systems are run by experts who are able to transform gathered information into useable knowledge and intelligence. EI is a complex and time-consuming process, and significant resources need to be dedicated, even with the use of automated tools, in order to ensure the efficiency of the system and provide quality validated information in real-time [START_REF] Barboza | Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases 121 outbreaks[END_REF].

Several challenges come with the use of biosurveillance tools to collect unstructured data or the use of enhanced syndromic surveillance to timely detect health events. These include the need for real-time high-quality data, duplicate detection and human moderation for validation and interpretation. Despite these challenges, the use of biosurveillance tools has demonstrated improved timeliness, sensitivity, accessibility and cost-effectiveness compared to traditional surveillance systems relying on official indicator-based sources, which prove to be more accurate and specific (Yan et al., 2017a). The trade-off between timeliness and accuracy when choosing tools depends on the objective and scope of the EI system. EBS, internet-based digital disease detection and enhanced syndromic surveillance can complement traditional surveillance systems to help enhance the early detection and monitoring of disease threats, whether by targeting periods of increased risk (e.g. mass gatherings) or new information sources (e.g. online media).

The OE and PADI-web case studies provided examples of pluridisciplinary collaborations to facilitate the collection of health data in order to improve the early detection and alerting of health threats. The two case studies highlight the importance of integrating tool developments in a global, functional and pluridisciplinary approach involving stakeholders. This is in line with the 69 meta-level of my contributions, which focuses on integrating expert knowledge and promoting interactions with stakeholders. To better identify the needs in terms of output, interface, data storage and other important tool features, researchers should involve end-users, such as epidemiologists, in tool development from the early stages of conception and design. This collaboration throughout the project also allows end-users to have a global understanding of the process, the underlying principles and the algorithms used to develop the tool, otherwise perceived as a 'black box'. It ensures the tool fits the needs of the users and is adapted to the local context, thus ensuring appropriation by end-users and a sustainable implementation in the user community, i.e. high Technology Readiness Level (TRL). This approach has been implemented in SPC's mass gathering enhancement program, as well as the H2020 MOOD project coordinated by CIRAD. The MOOD project has a work package almost entirely dedicated to sociological research and the use of social sciences to facilitate co-conception. It is based on participatory methods aiming to identify the needs of the users and the challenges they encounter during their EI activities, thus ensuring a co-conception of the tool and its successful and sustainable implementation. This strong participatory sociological component has strongly contributed to the project's acceptance by the European Commission in August 2019.

Tool genericity, and particularly the use of user-customizable features, can facilitate the adaptation of the tools to different contexts, e.g. from animal and plant health to bioterrorism. The development of generic tools allows the sharing of knowledge, expertise and resources, and promotes pluridisciplinary collaborations between projects. This aspect is also central to the H2020 MOOD project, which aims to strengthen disease surveillance in a One Health context through multi-institution collaborations between data scientists and health professionals from the animal and human health sectors.

Pluridisciplinary collaborations can be challenging. During my different work experiences, I noticed that some experts take great pride in their work -with possible territoriality -and adopt a discipline-based vision. Specialized in a particular field, they have their own agenda and interests, and usually prefer to keep a certain degree of autonomy and independence in their work [START_REF] D'amour | A model and typology of collaboration between professionals in healthcare organizations[END_REF]. This can make them less prone to include other disciplines in their projects for a more global and integrative approach. In addition, the context of precariousness and unemployment can exacerbate a natural competition between experts and limit the willingness to share one's work and learn new principles and perspectives from other disciplines. This type of behavior can unfortunately hamper pluridisciplinary collaborations. Indeed, the recognition and value of the work of peers is crucial to establish mutual trust and respect within a project, and lead to successful pluridisciplinary partnerships [START_REF] D'amour | The conceptual basis for interprofessional collaboration: Core concepts and theoretical frameworks[END_REF]. In such cases, the output of the whole becomes greater than the sum of the outputs of its individual parts, which highlights the interdependency between disciplines in the context of complex health issues.

There is no right or wrong type of collaboration, whether multidisciplinary, interdisciplinary or transdisciplinary. It is important to choose a level of integration and collaboration adapted to the context and objective of the project. Transdisciplinary collaboration is not always necessary or adapted to the context of the project. Trying to impose a strong-level of collaboration in a project where conditions are not favorable (e.g. lack of willingness of the participants) can be counterproductive. Willingness to understand other disciplines and their underlying principles is the first step to pave the way for fruitful pluridisciplinary interactions.

Finally, beyond pluridisciplinary collaborations among scientific disciplines or with health professionals, research should also inform policy-makers. Their implication in tool developments -even just by including them in the final loops of co-conception or informing them of the final product depending on what they would prefer -ensures that they become aware of the usefulness of the tool's outputs, and facilitates their decision-making process. The next level would be to bring pluridisciplinary and participatory processes up to the decision-making level, to go beyond policy advising. This would mean involving, or at least informing, decision-makers in the surveillance process and in research efforts to translate scientific results into policies.

Chapter 4

A pluridisciplinary approach to data analysis in the context of risk characterization

Introduction

A pluridisciplinary approach can also be implemented in the framework of the analysis of EI data. These analyses, conducted by EI teams, aim to characterize a detected health event in order to improve disease knowledge, better inform risk analyses, and target EI systems if needed. Risk detection and characterization are closely linked to risk analysis.

First, it is important to provide definitions of terms that are sometimes used indistinctively: risk analysis, risk assessment, risk estimation and risk evaluation. I present two frameworks that define risk analysis:

-the Codex Alimentarius Commission (CAC), which is a joint WHO/FAO Food Standards Program, and -the OIE Terrestrial Animal Code. The Codex Alimentarius, or "Food Code", and the OIE Animal Health Code are both a collection of standards, guidelines and codes of practice. OIE provides information on risk analysis relating to the importation of live animals or animal products, while the CAC focuses on the context of microbiological food safety.

In both of these frameworks, the risk analysis process consists of several steps which include risk assessment, risk management and risk communication. In this section, I will focus on risk assessment and will not consider risk management and risk communication which, although they are essential, are not as closely linked with the detection and the characterization of a health event (EI).

The CAC framework (WHO/FAO)

According to the CAC, risk analysis is defined as "a process consisting of risk assessment, risk management and risk communication" (World Health Organization et al., 2011). It emphasizes the functional separation of the three components while recommending interaction and communication between each component.

Risk assessment provides a framework for the review of relevant information in order to evaluate the possible outcome of an exposure to a health threat. It generally includes both a statement of the nature of the health threat and an estimation of the probability assertion of the harm it may cause. The CAC defines risk assessment as the process including the following steps:

 hazard identification: the identification of pathogenic agents capable of causing adverse health effects  hazard characterization: the evaluation of the nature of the health threat  exposure assessment: the evaluation of the likely exposure to biological, chemical or physical agents

 risk characterization: the estimation of the uncertainty and the probability of occurrence and severity of adverse health effects based on hazard identification, hazard characterization and exposure assessment

The OIE framework

The OIE risk analysis framework consists of the following steps:

• hazard identification: the identification of the pathogenic agents that could potentially be introduced in the commodity considered for importation • risk assessment:

 release assessment: the evaluation of the likelihood of entry/introduction  exposure assessment: the evaluation of the likelihood exposure of the target population  consequence assessment: the description of consequences (adverse health effects) and the likelihood of their occurrence and magnitude  risk estimation: the integration of the results from the release, exposure and consequence assessments to determine appropriate risk management measures • risk management • risk communication Both frameworks have similar components but vary in terms of definitions and order of implementation (Figure 21). Although they share similar definitions, OIE separates hazard identification from risk assessment, while the CAC includes hazard identification in risk assessment. This separation leads to four components of risk analysis for the OIE, compared to three components for the CAC.

According to the CAC, hazard characterization is the first step of risk assessment and aims to evaluate the nature of the possible adverse health effects associated with the health threat. In the OIE framework, the term hazard characterization is not used as such, but it is linked to the consequence assessment, which is also part of risk assessment.

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The OIE's risk estimation corresponds to the CAC's risk characterization, and involves a description of the uncertainty and the estimation of the risk in the risk assessment process, linked to the hazard as well as the exposure and its potential impact. [START_REF] Henwood | A qualitative risk assessment of Salmonella enteritidis in the broiler production chain in the Western Cape[END_REF] Another term that is sometimes used as a substitute for risk analysis or risk assessment is risk evaluation. Although it is not used by the CAC, it is defined by OIE as "the process of comparing the risk estimated in the risk assessment with the reduction in risk expected from the proposed risk management measures," i.e. determine whether risk management measures are warranted. It is included as a step of risk analysis and more specifically risk management.

Figure 21. Comparison of the CAC and OIE frameworks for risk analysis

I helped mobilize EI data and spatio-temporal analyses and models to conduct risk characterization analyses in two case studies, one in human health and the other in animal health. The case studies, presented in this section, illustrate examples of the analysis of EI data led by EI teams in order to characterize health threats and improve knowledge on their spread dynamics. They illustrate the added-value of a pluridisciplinary approach and the knowledge that can be provided by the rapid and preliminary analyses of outbreak data conducted by EI teams, before more in-depth risk analyses. I participated in these case studies during my work experiences at SPC (Roth et al., 2014a) and CIRAD [START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF].

The analysis of the circulation of arboviruses in the Pacific (2012-2014)

This first case study presents the added-value of analyzing EI data in a pluridisciplinary context to better understand disease spread patterns through the spatio-temporal analysis of the circulation of arboviruses in the Pacific, from 2012 to 2014. Dengue is a severe flu-like illness caused by a flavivirus belonging to the Flaviviridae family. Dengue viruses (DENV) are categorized into four serotypes (DENV-1 to 4) based on antigenic characteristics and the immune response they induce in the host. Infection with one of the four serotypes leads to immunity for the specific serotype only (although temporary crossed immunity can be observed between two to nine months after infection), but can increase the probability of potentially lethal complications (called severe dengue or dengue hemorrhagic fever, DHF). Dengue is transmitted by mosquitoes of the Aedes genus, mainly Aedes aegypti and Aedes albopictus, which can transmit any of the four dengue serotypes. Dengue has severe health, social and economic impacts worldwide (World Health Organization, 2017b). With about 2.5 billion people living in endemic areas, dengue is estimated to cause approximately 25 000 deaths and 100 million cases each year, although recent estimates suggest an increase to nearly 390 million annual infections when considering asymptomatic cases (World Health Organization, 2009[START_REF]Western Pacific Regional Action Plan for Dengue Prevention and Control[END_REF][START_REF] Bhatt | The global distribution and burden of dengue[END_REF]. The global incidence of dengue has dramatically increased in recent decades (30-fold increase in the number of reported cases globally over the past 50 years), and the four serotypes have spread to over a hundred countries in tropical and subtropical regions of Africa, Asia, America and the Pacific [START_REF] Descloux | Climate-Based Models for Understanding and Forecasting Dengue Epidemics[END_REF]World Health Organization, 2017b). As vaccine development is ongoing, vector control, surveillance and education are crucial to mitigate dengue outbreaks.

Chikungunya is an arthropod-borne disease transmitted by Aedes mosquitoes, mainly Aedes aegypti and Aedes albopictus. It is characterized by acute febrile illness associated with severe polyarthralgias [START_REF] Staples | Chikungunya Fever: An Epidemiological Review of a Re-Emerging Infectious Disease[END_REF]. The clinical illness is often associated with prolonged morbidity and significant economic and social consequences [START_REF] Enserink | Chikungunya : No Longer a Third World Disease[END_REF]. The chikungunya virus (CHIKV) is an alphavirus belonging to the Togaviridae family. Phylogenetic analyses have identified three distinct lineages of CHIKV strains according to phylogenetic origin: West Africa, Asia and East/Central/South Africa (ECSA). In comparison to most other common arboviral infections such as dengue, the rate of asymptomatic cases is lower, and the percentage of infected patients requiring medical attention is higher. No licensed vaccine or specific treatment is currently available.

Zika virus (ZIKV) is a flavivirus belonging to the Flaviviridae family, just like the DENV. Zika is transmitted by Aedes mosquitoes and causes mild fever, joint pain, conjunctivitis and rash, although the majority of cases are asymptomatic [START_REF] Musso | Zika virus[END_REF]. The virus was first isolated from a monkey in the Zika forest of Uganda in 1947 [START_REF] Kirya | A yellow fever epizootic in Zika Forest, Uganda, during 1972: Part 1: Virus isolation and sentinel monkeys[END_REF]. Following this first report, rare sporadic human infections were reported in Africa and Asia (World Health Organization, 2018b). The disease then emerged causing large outbreaks in the Pacific and South America. In March 2015, Brazilian authorities reported a large outbreak of ZIKV infections which soon spread throughout South America and to other regions of the world (World Health Organization, 2018b). First thought to cause mild illness, these Zika outbreaks caused widespread concern due to newly observed neurological complications such as Guillain Barré syndrome and microcephaly [START_REF] Dejnirattisai | Dengue virus sero-crossreactivity drives antibody-dependent enhancement of infection with zika virus[END_REF]. To date, a total of 86 countries and territories have reported ZIKV infections (World Health Organization, 2018b).

Although complications may vary, all three arboviruses share similar clinical symptoms, mosquito vectors, and epidemiology, particularly in urban settings.

The Pacific context: health services and mosquito vectors

The Pacific region hosts potential vectors for many arboviruses to which local populations are mostly naïve. This makes the region an ideal setting for the emergence and spread of arboviral diseases, and greatly contributes to the globalization of dengue, chikungunya and Zika viruses as public health threats (Cao-Lormeau and Musso, 2014). Dengue, chikungunya, Zika are all transmitted by mosquitoes of the Aedes genus. The two main mosquito vectors of these three arboviruses worldwide are Aedes albopictus and Aedes aegypti¸ which both bite during the daytime and have adapted to urban and peri-urban settings. In the Pacific, in addition to these two species, the mosquito species identified as vectors of dengue are Aedes polynesiensis, Aedes scutellaris, Aedes marshallensis, Aedes cooki, Aedes hebrideus, Aedes hensilli, Aedes kesseli, Aedes pseudoscutellaris, Aedes rotumae and Aedes tabu [START_REF] Guillaumot | Arboviruses and their vectors in the Pacific -status report[END_REF]. Aedes polynesiensis and other local Aedes mosquito species are considered potential vectors for chikungunya and Zika [START_REF] Musso | Unexpected outbreaks of arbovirus infections: lessons learned from the Pacific and tropical America[END_REF]. [START_REF] Teurlai | Modélisation multi-échelle de la dynamique spatiale de la Dengue : application à la Nouvelle-Calédonie et à la région Pacifique[END_REF].

Aedes aegypti is present throughout most of the Pacific region including North Queensland. It is absent from the rest of Australia, New Zealand, Hawaii, Futuna and some other remote islands. It seems to be currently displaced by Aedes albopictus in some locations including Papua New Guinea and the Solomon Islands.

The evolution of arboviruses in the Pacific

Mosquito-borne viral diseases have a characteristic epidemiology in the Pacific due to a unique insular context. Small populations scattered over thousands of islands, in relative geographic isolation, and with significant mobility due to air travel, increase the risk of exposure to arboviruses (Roth et al., 2014a).

The risk of importation of DENV, ZIKV or CHIKV in Pacific islands can be substantial, depending on travel patterns and the number of airline passengers between countries and territories where these arboviruses are circulating. The risk is especially high for PICTs with many air travelers to and from islands with ongoing circulation. However, in comparison to the large CHIKV epidemic that occurred in the Indian Ocean in 2005, population density in the Pacific is lower, which may decrease the risk of spread of arboviral diseases.

From 2007 to 2014, the epidemiology of arboviruses in the Pacific progressively evolved and the situation gradually worsened. The circulation of a single dominant DENV serotype (DENV-1) changed to the concurrent co-circulation of several serotypes as well as the emergence of new mosquito-borne viruses in the region (Figure 23). In 2014, concomitant outbreaks of dengue, chikungunya and Zika were reported for the first time in the Pacific region.

Dengue outbreaks in the Pacific

In the Pacific region, the first dengue outbreaks were reported in Hawaii in 1844, in French Polynesia in 1852, and in Fiji and New Caledonia in 1883 [START_REF] Perry | The Mosquitoes and Mosquito-Borne Diseases on New Caledonia, an Historic Account: 1885-1946 1,2[END_REF][START_REF] Descloux | Dynamique moléculaire et épidémique du virus de la dengue dans différents écosystèmes[END_REF]. Following World War II, the numerous movements of soldiers between PICTs led to several waves of dengue epidemics, and the dengue virus was isolated and serotyped as DENV-1 for the first time [START_REF] Steel | Natural attenuation of dengue virus type-2 after a series of island outbreaks: A retrospective phylogenetic study of events in the South Pacific three decades ago[END_REF]. From 1950 to 1970, only two dengue epidemics were reported, both in French Polynesia, in 1964and 1969. In 1971, dengue re-emerged in the Pacific region (not considering the two epidemics in French Polynesia) causing many outbreaks, and cases of DHF were reported for the first time [START_REF] Singh | Dengue in the Pacific -an update of the current situation[END_REF]. Dengue viruses were once again introduced in the Pacific in the early 1970s following an absence of more than 25 years, and have caused many outbreaks since [START_REF] Gubler | Dengue/dengue hemorrhagic fever: the emergence of a global health problem[END_REF].

Today, dengue circulates in the form of epidemic waves. These epidemic waves are linked to the introduction and spread through the region of a new serotype (World Health Organization, 2017b). The spread of a new serotype through several PICTs usually lasts between three to six years, until a new serotype emerges and becomes the dominant serotype (Figure 24) [START_REF] Teurlai | Modélisation multi-échelle de la dynamique spatiale de la Dengue : application à la Nouvelle-Calédonie et à la région Pacifique[END_REF]. As illustrated in Figure 24, waves of dengue serotypes seem to alternate in a specific order: DENV-2, DENV-1, DENV-4 and DENV-3. However, this pattern was identified over the period 1971-2009 which is not long enough for this observation to be robust. An epidemic cycle of the same serotype occurs every 20 to 24 years [START_REF] Tesh | The Distribution and Prevalence of Group a Arbovirus Neutralizing Antibodies Among Human Populations in Southeast Asia and the Pacific Islands[END_REF][START_REF] Gubler | Dengue/dengue hemorrhagic fever: the emergence of a global health problem[END_REF]for DENV-2, 1978[START_REF] Souarès | Telehealth and outbreak prevention and control: the foundations and advances of the Pacific Public Health Surveillance Network[END_REF]for DENV-1 and 1984[START_REF] Darbellay | A vision of transdisciplinarity : laying foundations for a world knowledge dialogue[END_REF] for DENV-4). [START_REF] Teurlai | Modélisation multi-échelle de la dynamique spatiale de la Dengue : application à la Nouvelle-Calédonie et à la région Pacifique[END_REF].

Dengue epidemiology is heterogeneous among PICTs. Smaller islands usually sustain dengue transmission for only several months, while larger islands (e.g. New Caledonia) may experience active circulation of a serotype for several years until the emergence of a new serotype [START_REF] A-Nuegoonpipat | Sustained transmission of dengue virus type 1 in the Pacific due to repeated introductions of different Asian strains[END_REF][START_REF] Li | Rapid displacement of dengue virus type 1 by type 4, Pacific region, 2007-2009[END_REF].

The increasing co-circulation of more than one serotype of dengue in many countries of the Western Pacific Region, together with other factors, may be responsible for an increase in the number of DHF cases (World Health Organization, 2017b).

Chikungunya outbreaks in the Pacific

There is little documentation on previous CHIKV circulation in the Pacific, but studies from the 1970s in Papua New Guinea indicate a seroprevalence of CHIKV in the population of up to 30 % [START_REF] Tesh | The Distribution and Prevalence of Group a Arbovirus Neutralizing Antibodies Among Human Populations in Southeast Asia and the Pacific Islands[END_REF][START_REF] Kanamitsu | Geographic Distribution of Arbovirus Antibodies in Indigenous Human Populations in the Indo-Australian Archipelago *[END_REF]. These findings indicate that CHIKV may have circulated in the region and that there could be immunity among some populations (Roth et al., 2014b).

The first reports of autochthonous chikungunya infections in the Pacific were notified in 2011 in New Caledonia, with 33 confirmed cases [START_REF] Dupont-Rouzeyrol | Chikungunya Virus and the Mosquito Vector Aedes aegypti in New Caledonia (South Pacific Region)[END_REF]. Phylogenetic analysis showed that the CHIKV strain isolated in New Caledonia belonged to the Asian lineage, which is consistent with the Indonesian origin of the previously imported CHIKV cases. Subsequently, chikungunya outbreaks were reported in Papua New Guinea in 2012, in New Caledonia and Yap Island of the Federated States of Micronesia in 2013, and in Tonga, American Samoa, Samoa and Tokelau in 2014 [START_REF] Horwood | Outbreak of chikungunya virus infection, Vanimo, Papua New Guinea[END_REF]Cao-Lormeau and Musso, 2014;Roth et al., 2014b). The CHIKV strain detected in Papua New Guinea has been shown to be of the ECSA genotype, therefore carrying the E1-A226V mutation which was detected in the large CHIKV outbreak in la Réunion in 2005. This mutation has been shown to increase vector competence and epidemic potential [START_REF] Tsetsarkin | A Single Mutation in Chikungunya Virus Affects Vector Specificity and Epidemic Potential[END_REF][START_REF] Horwood | Outbreak of chikungunya virus infection, Vanimo, Papua New Guinea[END_REF]. Sporadic cases of CHIKV were reported in the region until 2015.

Zika outbreaks in the Pacific

In 2007, the Yap State of the Federated States of Micronesia reported the first outbreak of ZIKV outside of Africa and Asia [START_REF] Duffy | Zika Virus Outbreak on Yap Island, Federated States of Micronesia[END_REF]. No subsequent outbreak of Zika was reported in the Pacific until 2013, when the virus re-emerged in French Polynesia causing a large outbreak with an estimated 28 000 human cases (approximately 11% of the population), before spreading throughout the Pacific region (Cao-Lormeau et al., 2014;[START_REF] Musso | Rapid spread of emerging Zika virus in the Pacific area[END_REF]. Phylogenetic analyses demonstrated that the French Polynesia strain was closely related to the Cambodia 2010 and the Yap State 2007 strains, corroborating previous findings highlighting the expansion of the ZIKV Asian lineage (Cao-Lormeau et al., 2014). Zika continued to spread in the Pacific from 2013 to 2017, with sporadic cases reported in 13 PICTs [START_REF] Musso | Unexpected outbreaks of arbovirus infections: lessons learned from the Pacific and tropical America[END_REF].

Context of the study

As a public health surveillance officer at SPC from 2014 to 2016, I was in charge of updating the Pacific regional database of infectious disease outbreaks. This database contained detailed information on the circulation of arboviruses such as the date and location of occurrence, the serotype and the number of cases reported. This database provided valuable information to monitor the evolution of arboviral circulation in the Pacific, and a historical logging of detailed outbreak data spanning several decades at regional level. With my colleagues of the REI team, we decided it would be useful to analyze such historical data to identify trends in the spatial and temporal circulation of dengue, chikungunya and Zika in the Pacific region. We noticed, through our EI activities, that the number of arbovirus outbreaks seemed to be increasing in the past years.

We had also noticed an increasing co-circulation of different types and serotypes of arboviruses simultaneously in the region, sometimes even in the same country. In addition, arboviruses were a topic of interest in New Caledonia in 2014, when we decided to conduct the study. New Caledonia was hit by the largest ever recorded outbreak of dengue at the time with almost 11 000 cases of DENV-1 reported from September 2012 to September 2013. Dengue cases continued to be reported until September 2014, and DENV-3 emerged in February 2014, leading to the cocirculation of DENV-1 and DENV-3 in 2014. Cases of chikungunya and Zika were also recorded on the island in 2013 (for chikungunya) and 2014 (for chikungunya and Zika). This indicates significant and concomitant circulation of several arboviruses in New Caledonia at the time, which prompted concern of an increase in the risk of emergence of arboviruses in New Caledonia and the Pacific region.

We designed and implemented a descriptive study to confirm these hypotheses by analyzing the arbovirus outbreak data that had been gathered by EI over the past two years, 2012-2014. The aim of the study was to describe the circulation of arboviruses, namely dengue, chikungunya and Zika, in the Pacific from 2012 to 2014 in order to identify trends or changes in the epidemiology of these arboviral diseases. As second author, I co-lead the study's implementation. I contributed to the filtering, cleaning, standardization and verification of the collected data relating to outbreaks and vector distribution (e.g. detecting anomalies, filling missing information), as well as the analysis of the data to identify spatial and temporal trends. I also participated in the writing process.

Material and methods

An outbreak is defined by WHO as the occurrence of cases in excess compared to what is normally expected, whether the disease is endemic or not. If the disease is not endemic in a PICT, an outbreak can be just one or a few cases, excluding imported cases because the focus is on local transmission. The definition of an outbreak and its interpretation varies depending on the PICT. Therefore, in our study, we considered an outbreak when it was defined as such by the reporting health authorities in the data source, or when the first autochthonous cases were reported. When the term "outbreak" was not used by authorities, we used the term "circulation". When sporadic cases were continuously reported, we considered a new circulation (or a new outbreak) when no cases had been reported in at least one year.

Data relating to newly reported (not ongoing as of 1 st January 2012) outbreaks or circulation of dengue, chikungunya and Zika reported in the Pacific region from 1 st January 2012 to 17 th September 2014 was extracted from SPC's regional database. This data included case numbers, dates, locations (country, region and village/town if available), information sources, serotypes and other available information. We considered outbreaks reported in the Pacific region, which includes the 22 PICT members of the PPHSN network.

The paper contains a brief description of the regional epidemiological situation of dengue, chikungunya and Zika, as well as a table summary of the collected information relating to outbreaks of these three arboviruses.

The data was aggregated by semester in order to plot the temporal evolution of the number of mosquito-borne outbreaks. Semester 1 corresponds to January-June and semester 2 corresponds to July-December.

The map of dengue, chikungunya and Zika outbreaks was taken from the weekly map of disease alerts in the Pacific, computed by SPC's EI team and sent to PICTs via the PacNet network of the PPHSN. I was in charge of updating this map.

Information relating to the spatial distribution of mosquito vectors was provided by the Pasteur Institute of New Caledonia. The information combined personal knowledge and expertise of entomologists, as well as information from the literature.

Main results

Between 1 st January 2012 and 17 th September 2014, 12 PICTs reported dengue outbreaks or circulation. Co-circulation of two serotypes was reported in at least five PICTs, including New Caledonia. Chikungunya outbreaks were reported in seven PICTs and Zika in three PICTs (Figure 25). A total of 28 outbreaks or circulation of dengue, chikungunya and Zika were reported in the Pacific region during the study period. (Roth et al., 2014a).

The study identified an increase in the number of different arboviruses circulating in the Pacific from 2012 to 2014, and particularly during the first semester of 2014 (Figure 26). This is mainly due to the emergence of chikungunya in 2011, the re-emergence of Zika in 2013, and the increasing co-circulation of several arboviral diseases and/or serotypes in the same country, such as concurrent outbreaks of two distinct dengue viruses and Zika virus in New Caledonia in 2014.

82 (Roth et al., 2014a).

The study also provides a map of the distribution of Aedes mosquito vectors in the Pacific region (Figure 27).

Study discussion

The study provides an example of a rapid, simple and descriptive analysis of EI data, conducted by EI teams, using time series and mapping of outbreaks and vector distributions. The study presented the surveillance and described the epidemiology of dengue, chikungunya and Zika viruses in the Pacific region from 2012 to 2014. The results revealed an increase in the number and diversity of arboviral disease outbreaks and circulation in the Pacific during the study period. Significant mobility within the region and with other parts of the world affected by arboviruses increases the risk of exposure and introduction through the airline network of the Asia-Pacific region. This stresses the need for improved disease surveillance and response to limit the risk of spread at regional and global level, and mitigate the already-heavy burden on PICT health systems.

The epidemiology of arboviruses is distinct in the Pacific due to the insular context with geographically scattered populations. The epidemic pattern of dengue in the Pacific is sporadic with epidemic waves, marked by the introduction of a new and dominant serotype which sweeps through the region every three to five years, with varying durations depending on the size of the naïve population. During my experience, we noticed that these epidemic waves sweeping through the Pacific were also observed for other infectious diseases such as measles. This pattern is linked to travel trends and ethnic groups, and is characteristic of island epidemiology.

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However, the epidemiology of dengue seems to be changing with more frequent introductions due to increased mobility and co-circulation of several serotypes. Instead of having a dominant serotype replace the previous serotype, we can observe prolonged and uninterrupted circulation of DENV-1 in the region for over 10 years, as is the case in New Caledonia. The continued circulation of DENV-1 might be linked to a change in urbanization (leading to more cases in winter), more frequent introductions, and perhaps better diagnostic capacities as well. We can argue that dengue is becoming endemic in some regions of the Pacific and that the epidemiology is changing from sporadic epidemic waves to become more similar to the epidemiology of dengue in South-East Asia. This would mean that the epidemiology of dengue in the region is becoming less characteristic of insular epidemiology. Increased co-circulation of arboviruses could also mean increased risk of complications and severe forms including DHF, further increasing the burden on health systems.

In the paper, we consider all three arboviruses (dengue, chikungunya and Zika) because the regional diagnostic capacities for chikungunya and Zika are generally poor. This is the reason why regional surveillance focuses on a syndromic surveillance approach, and allows us to detect an increase in reports of high fever. This, however, means that we might have missed an emergence of chikungunya or Zika. Diagnostic capacities should therefore be improved at regional level through the PPHSN's LabNet service, with material support and training.

The study revealed the added value of analyzing historical data. In a broader sense, looking at 30 years of historical data on the circulation of arboviruses (available in SPC's regional database) is key to understanding the risk of large outbreaks in PICTs. For example, if the previous cases of a particular DENV serotype were reported more than a decade ago, a new introduction of this virus is more likely to cause a large outbreak due to low population immunity. If the previous cases were reported just a year or two beforehand, residual population immunity is likely to be greater (depending on the size of the previous outbreak), perhaps leading to less new cases. Therefore, historical data can be used as a tool to predict the potential size of outbreaks depending on the time of previous circulation in a PICT or at regional level. This is done by SPC and WHO which use data on past arboviral circulation in the Pacific to alert on the risk of large outbreaks following new introductions of arboviruses in the region.

The study also underlines the need for improved vector control capacity, which is often insufficient. The implementation and improvement of entomological surveillance targeting vectors of dengue and other arboviruses could help improve our knowledge on vector distributions and insecticide resistance. This could help improve the map of vector distributions provided in our study, which is mostly based on past entomological surveys dating back to the second half of the 20 th century.

In hindsight, it would have been interesting to include more data in our study. Beyond the reemergence of chikungunya (2011) and the emergence of Zika ( 2013), which greatly contributed to this unprecedented wave of arboviruses, I believe we should study the epidemiology of dengue in more detail. SPC's regional database contains 30 years of detailed data on the circulation of dengue serotypes in the Pacific, which could be analyzed to confirm the epidemic waves pattern and study more thoroughly this change in epidemiology with the continued circulation of DENV-1. Mathematical models including environmental and socio-economic drivers of dengue, as well as phylogenetic studies, could help improve our knowledge of this continued circulation of DENV-1 and why other introduced serotypes do not take over and become dominant.

Since the publication of the paper in 2014, dengue has continued to circulate in the Pacific with increased co-circulation of several serotypes. Since 2015, DENV-1, 2 and 3 have circulated in the region with mainly DENV-1 reported in the South and DENV-3 reported in the North of the Pacific region. In 2017, all three serotypes were reported in the region. The change in epidemiology from the circulation of a single dominant serotype to the concurrent co-circulation of several serotypes continued to be observed. DENV-1 circulated uninterruptedly over a decade (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018), with cases even reported in the cold season, in co-circulation with DENV-4 in 2009, DENV-3 in 2014, DENV-2 and 3 in 2017, and DENV-2 in 2018 [START_REF] Inizan | Dengue in New Caledonia: Knowledge and Gaps[END_REF]. Chikungunya and Zika viruses continued to circulate in the Pacific with several outbreaks reported in 2014 and 2015, and a few sporadic cases of Zika reported in 2016 and 2017. SPC's REI team is still actively involved in EI and monitors infectious disease outbreaks in the Pacific, including arboviral diseases. Their weekly map of disease alerts is published online through the PacNet network along with an interactive version of the map available online2 . SPC also continues to strengthen laboratory capacity, particularly relating to diagnostics, in Pacific islands through the LabNet service of the PPHSN, which improves the probability of detecting new arboviral emergence in the region.

The insular context of the Pacific has an impact on the epidemiology of arboviruses in the region, and several factors influence the risk of arbovirus emergence and spread. Isolated and scattered populations, high mobility and travel from dengue-affected regions (e.g. South-East Asia) and between Pacific islands, as well as limited population immunity, increase the risk of arbovirus introduction and spread in the Pacific region, with possible subsequent exportation to other unaffected regions like Europe. In addition, several competent vectors are present in the region, including Aedes albopictus and Aedes polynesiensis, which increase the risk of regional spread. Non-infectious diseases (e.g. diabetes, obesity) can also increase vulnerability to infectious diseases and should be considered when looking at the susceptibility of populations, particularly in the Pacific where non-communicable disease rates are among the highest in the world. A descriptive spatio-temporal study is the basis before conducting further detailed analyses to better understand the epidemiology of arboviruses. These include seroprevalence studies to clarify the level of population immunity in PICT populations, particularly for CHIKV and ZIKV, to evaluate the level of immunological susceptibility of these populations to new emergences of arboviruses.

Access to timely and reliable information is crucial to mitigate the risk and burden of arboviruses on Pacific health services, particularly given the limited-resources available in some islands. Networks such as the PPHSN are essential to facilitate the exchange of information between health professionals and the rapid implementation of prevention and control measures. Several different information sources are needed to have a more complete overview of the epidemiological circulation of diseases. These include official reports from national and international health authorities, unofficial communications between health professionals through PacNet, and media reports. The PPHSN also highlights the importance of cross-border surveillance. Surveillance by proxy is also used for EI in the Pacific region. It can provide information on the circulation of a disease in a specific country based on the number of imported cases from that country detected in other PICTs. Imported case data is often used as proxy for countries for which limited data is available, such as Papua New Guinea. Indeed, the level of exportation of a disease to other countries is an indicator of the level of disease incidence in the country: the higher the incidence rate, the higher the probability of infecting tourists who can import the disease into their home country upon their return.

Outbreaks of arboviruses in non-tropical regions such as Europe emphasize the need for preparedness and response to mitigate the risk of EIDs. This is particularly true in a context of globalization, with the expansion of air travel facilitating rapid spread of pathogens from endemic/epidemic areas to unaffected regions. Preparedness starts with being aware of ongoing disease circulation in affected regions. The information produced by SPC's EI team is accessible and disseminated at regional level but also beyond. In addition to the regular reports published on PacNet, SPC's online interactive map containing information on ongoing public health alerts is publicly available. It is used by European public health agencies, for example in the ArboFrance network which includes many different stakeholders (e.g. research centers, public health institutes, governmental agencies). This network uses SPC's regional map in their monthly meetings to summarize the epidemiological situation of infectious diseases in the Pacific region, as they do with other regions such as the Caribbean and the Indian Ocean. This allows public health authorities to launch preparedness measures if a risk is detected. Beyond Europe, it is of international interest to stay informed regarding the spread of arboviruses in the Pacific, as we have seen with the spread of Zika virus from French Polynesia to Brazil and its devastating consequences on public health in South America.

The study involved pluridisciplinary collaborations between epidemiologists, health authorities, EI experts, entomologists and microbiologists. It gathered pluridisciplinary and complementary perspectives to better understand the epidemiological dynamics relating both to the pathogen and the vector, and inform risk analyses. Through these data analyses, the EI team was able to confirm an increase in the number of outbreaks of arboviruses in the region, as well as a change in their epidemiology marked by increasing co-circulation. This new knowledge can trigger strengthened arboviral surveillance and inform health authorities on possible surveillance and control strategies, such as increasing serotyping during dengue outbreaks to monitor the possible emergence of a new serotype or increasing vector control measures.

The estimation of the spread rate of lumpy skin disease in the Balkans (2015-2016)

This second case study showcased another example of a rapid data analysis method, implemented by EI teams, to better characterize disease spread patterns by estimating the spread rate of lumpy skin disease through the Balkans in 2015-2016.

Lumpy skin disease

Lumpy skin disease (LSD) is caused by a poxvirus from the Capripoxvirus genus and the Poxviridae family. Capripoxviruses also include sheep and goat pox. It is transmitted by arthropod vectors, 87 but direct/indirect transmission may occur [START_REF] Babiuk | Capripoxviruses: An Emerging Worldwide Threat to Sheep, Goats and Cattle[END_REF]. Although no specific vector has been identified, mosquitoes, biting flies and ticks are believed to play a role in the transmission of the disease. LSD is a species-specific virus infecting only cattle, buffalo and closely related wildlife. It causes fever, lymphadenopathy and very characteristic cutaneous nodules 2-5 cm in diameter, particularly on sparsely haired areas such as the head, neck, limbs, udder, genitalia and perineum [START_REF] Beard | Lumpy skin disease: a direct threat to Europe[END_REF]. Clinical signs range from inapparent to deadly. The diagnosis of LSD is done by detecting viral DNA in blood or tissues of infected animals using polymerase chain reaction (PCR). LSD is included in the list of OIE diseases, and notification of outbreaks to the European Commission and its Member States is mandatory (Council Directive 82/894/EEC3 ).

The main prophylaxis measure is vaccination. In LSD-free countries, the importation of cattle, carcasses, leathers, skins and semen originating from infected areas is banned to avoid the introduction of the disease (World Organisation for Animal Health, 2019a). In infected countries, OIE recommends a strict quarantine to prevent the introduction of infected animals into diseasefree herds. In case of an outbreak, animal isolation, animal movement ban, culling of infected animals, incineration of dead animals, disinfection of premises and materials, and vector control measures should be implemented. Prophylaxis measures other than vaccination remain inefficient.

LSD is a transboundary viral disease that has traditionally been found in Southern Africa [START_REF] Beard | Lumpy skin disease: a direct threat to Europe[END_REF].

Outbreak in the Balkans in 2015-2016

The LSD virus was first introduced in Greece in August 2015, with two outbreaks reported in cattle farms in the Thrace and Eastern Macedonia regions. In 2015, the disease spread to the Eastern part of Greece with 117 LSD outbreaks reported (Figure 28). The number of outbreaks decreased by December 2015 due to the implementation of control measures (stamping out and mass vaccination in the Northeastern provinces) and winter conditions limiting vector activity [START_REF] Food | Lumpy skin disease: scientific and technical assistance on control and surveillance activities[END_REF].

In In 2018-2019, sporadic cases were reported in Turkey, Georgia and Russia, but no outbreak of LSD was reported in the Balkans. This confirms the efficiency of the vaccination campaigns and the coordinated control measures implemented in the region (European Food Safety Authority et al., 2019).

A regional vaccination campaign started in 2016 with a live attenuated vaccine based on a homologous strain (Neethling vaccine) in all affected European countries [START_REF] Kitching | Vaccines for lumpy skin disease, sheep pox and goat pox[END_REF][START_REF] Food | Lumpy skin disease: scientific and technical assistance on control and surveillance activities[END_REF]. In addition, two non-affected countries implemented LSD vaccination as a preventive measure, namely Croatia in 2016-2017 and Bosnia-Herzegovina in 2017. All of the Southeastern European countries (Albania, Bosnia-Herzegovina, Bulgaria, Greece, Kosovo, Montenegro, Serbia and North Macedonia) continued to vaccinate against LSD in 2018, except for Croatia which was recognized as free-from-LSD in 2018, following the implementation of a specific surveillance system as recommended by OIE. Over 2.5 million animals were vaccinated, reaching over 70% of vaccination coverage in the Balkans [START_REF] Food | Lumpy skin disease: scientific and technical assistance on control and surveillance activities[END_REF]. Mass vaccination of cattle against LSD using a live homologous vaccine has proven to be an effective control measure. However, persisting circulation of LSD in the region (Albania in 2017) proves that naïve cattle remain at risk of infection despite high vaccination coverage, and countries should remain vigilant.

Modelling the spread rate of infectious diseases

Pathogens rarely spread homogeneously through space and time, and the mechanisms behind disease spread are often uncertain and variable. Pathogens encounter different levels of resistance, which can be influenced by dispersal mode and other intrinsic factors, the presence of susceptible host or vector, and other environmental, socio-economic and ecological factors [START_REF] Taylor | Risk factors for human disease emergence[END_REF][START_REF] Patz | Unhealthy landscapes: Policy recommendations on land use change and infectious disease emergence[END_REF][START_REF] Jones | Global trends in emerging infectious diseases[END_REF]. Modelling the spread of pathogens provides valuable information for risk analysis, for example by estimating the duration of time before the disease affects a specific area depending on the distance to the current disease frontline and the spread velocity, which can in turn improve prevention and control measures.

Previous studies have analyzed and modelled the spread of diseases to identify spatial and temporal patterns. Few studies have looked at the spread rate of diseases, and they usually focused on the general mean estimate of the spread rate over a large area, instead of studying the variation of local spread rates in different areas within the infected perimeter [START_REF] Gilbert | Comparing methods for measuring the rate of spread of invading populations[END_REF][START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF].

In epidemiology, mechanistic models are generally based on complex equations which require a detailed understanding of disease life-history parameters (e.g. spread rate, incubation period). An example of a mechanistic model often used in epidemiology is the susceptible-infected-recovered (SIR) model which is used to study the spread of a pathogen through a population. When many parameters are involved, the volume of parameter space to test increases and the search for optimal combinations of parameters becomes difficult. In contrast, statistical models characterize the spatio-temporal patterns of invasion to quantify some of their key features (e.g. rate of spread, correlation between date of first invasion and external predictor variables). Although the epidemiological interpretation of their outputs might be more challenging, they are easier to fit to observed spatio-temporal data describing the invasion because they do not require specific parameters. The merits of mechanistic vs statistical models have long been debated in ecology and epidemiology. The mix of both models often represents a programmatic approach: the use of a statistical model to quickly characterize a diseases spread pattern, followed by the application of a mechanistic model to test and validate more specific hypotheses [START_REF] Dormann | Correlation and process in species distribution models: bridging a dichotomy[END_REF]. In this section, I will focus on statistical models.

Local spread rates can be estimated using two general types of spatio-temporal analysis approaches. First, boundary displacement (BD) methods are based on the division of the invasion into time steps corresponding to invasion front boundaries [START_REF] Morin | Anisotropic spread of hemlock woolly adelgid in the eastern United States[END_REF]. The local spread rate is estimated as the distance between two successive time steps or front boundaries. On the other hand, trend-surface analysis (TSA) calculates the date of first invasion at outbreak locations and uses a spatial interpolation model to predict this date across the study area. The slope of the surface corresponds to a friction index (units of time /space), and the spread rate is calculated as the inverse of the friction index [START_REF] Farnsworth | Identifying spatio-temporal patterns of transboundary disease spread: examples using avian influenza H5N1 outbreaks[END_REF]. BD is more appropriate when the temporal resolution of the data is coarse (e.g. weekly). The displacement of the invasion front can be evaluated using aggregated data per week. However, it would be problematic to do so with daily data as the error in the estimation of the boundary would be too high compared to the relatively small daily displacements.

Estimating the spatio-temporal distribution of the invasion front to derive the date of first invasion can be done using different interpolation methods: a generalized linear model (GLM), kriging and thin plate spline regression (TPSR). A GLM is used to predict a binomial response (presence/absence) at each time step for the BD approach, and assumes a Gaussian distribution for the dependent variable for the TSA approach. Kriging aims to fit a variogram to outbreak location data within a 300 cell radius using a generalized least squares (GLS) regression [START_REF] Firestone | The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis[END_REF]. TPSR is used to estimate the surface of the date of first invasion [START_REF] Wood | Thin plate regression splines[END_REF]. The slope of the surface corresponds to a friction index, and its inverse provides the magnitude of the spread rate while the gradient (angle or inclination) of the surface at each point provides to the direction of spread.

Three main methods exist to estimate spread rates (Figure 29) [START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF]. With the neighboring method, the spread rate corresponds to the inverse of the local slope of the date of first invasion surface. With the nearest distance method, the distance of each pixel to the nearest previous and following invasion fronts is estimated as the length of the line that connects two sequential invasion front lines passing through a given pixel. With the Delaunay method, the space between two sequential invasion front lines is divided into geometric shapes using a Delaunay triangulation. The median of the triangles is used as the estimate of the displacement. The nearest distance and Delaunay methods both require invasion fronts and are, therefore, more appropriately used with a BD approach which provides a discrete output (presence/absence). As it requires a continuous variable, the neighboring method is more appropriately used with a TSA approach. [START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF].

Figure 30 summarizes the three processes along with the different methods.

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Figure 30. Summary of the three main processes to interpolate and predict the date of first invasion, and to estimate the spread rate [START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF].

Context of the study

The FEIS closely monitored the evolution of the epidemiological situation of LSD in the Balkans, particularly after its rapid spread through Turkey and its introduction into Eastern Greece. The FEIS published 22 reports on the LSD outbreak in the Balkans and Turkey, between August 2015 and December 2016. These reports were published on the ESA Platform website.

LSD is a category 1 health hazard for France according to the French decree of 29 th July 2013, relating to the definition of category 1 and 2 health hazards for animal species. It causes significant economic loss in affected countries. Given its worrying introduction into Europe and its subsequent spread, the DGAL of the French Ministry of Agriculture, for whom the ESA Platform carries out EI activities, asked the FEIS to estimate how fast LSD was spreading through the Balkans in order to evaluate the risk of introduction of the disease into France.

CIRAD members of the FEIS team were involved in the previous development of a spread rate model, in collaboration with the University of Brussels (ULB) [START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF]. We decided to apply this spread rate model to the outbreak of LSD in the Balkans. As first author, I led the design of the study (methodology, choice of study period and area), participated in the adaptation of the tool to the LSD outbreak, and conducted the analysis of the data in close collaboration with modelling experts, epidemiologists from CIRAD and FEIS members.

Material and methods

The aim of the study was to estimate and analyze the spread rate of LSD outbreaks reported in the Balkans in 2015 and 2016.

LSD outbreaks were described using the geographical coordinates (rounded to the second decimal degree to ensure confidentiality) and the confirmation date of reported outbreaks. The study included LSD outbreaks that occurred in the Balkans (countries cited above) and in the Western part of Turkey (Edirne, Kirklareli and Tekirdag provinces), from 13 th May 2015 to 31 st December 2016. Outbreak data was extracted from the European Commission's ADNS and the FAO's Empres-I databases.

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To identify hotspots of disease occurrence, the density of LSD outbreaks was mapped using hexagonal binning [START_REF] Carr | Hexagon Mosaic Maps for Display of Univariate and Bivariate Geographical Data[END_REF]. The study area was divided into hexagons of 20 km, and outbreak data was spatially aggregated per hexagon.

Disease spread rates were calculated using a combination of TSA, TPSR and a neighboring spread rate estimator, which was identified by previous studies as providing the most robust estimates of local spread rates [START_REF] Farnsworth | Identifying spatio-temporal patterns of transboundary disease spread: examples using avian influenza H5N1 outbreaks[END_REF][START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF].

The week of first invasion was extracted from the date of occurrence at each outbreak location (Figure 31a), and interpolated across the study area using TPSR (Figure 31b). This generated a 1km resolution raster of the front wave of invasion across the study area. [START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF].

A friction index, which represents the landscape resistance to the spread of the disease, was calculated as the difference of week number between each raster cell and its surrounding cells (neighboring method). The friction index (units of time per space) corresponds to the slope of the TPSR surface (Figure 32), and represents the mean duration of time (in weeks) for the virus to spread from a given raster cell to its neighboring cells, i.e. to cross 1 km.

93 The spread rate (week/km) was calculated as the inverse of the friction index (km/week). A steep slope means that the disease needed a lot of time to pass from one pixel to the next (high friction, low spread rate). Conversely, a flat surface on a pixel means that the disease passed through the pixel almost instantaneously (low friction, high spread rate). The error linked to the interpolation tends to increase as the distance from the observed outbreak location increases. Such errors tend to artificially inflate spread rate values. Therefore, the values of the spread rates were extracted at outbreak locations only, in order to minimize the bias caused by interpolation and limit edge effects.

Assuming that the spread of LSD is the outcome of multiple underlying processes (e.g. vector transmission, cattle movements), the estimated values of spread rates at outbreak locations should follow a mixture of Gaussian distributions (multi-modal distribution). A mixture model with a bootstrap procedure of 500 replications was used to estimate the number of Gaussian components.

Finally, the mean spread rate and its evolution since the first LSD introduction (year 1 and year 2) and according to seasons were estimated using a GLS model.

All the analyses were performed using the R software (R Core Team, 2019). 94

Main results

The median spread rate of LSD through the Balkans was estimated at 7.3 km/week (95% CI: 4.4 -12.5 km/week). GLS estimates revealed an almost constant mean spread rate of 11 km/week across seasons, except during summer-fall 2015 when the spread rate estimate dropped to 6.5 km/week.

We identified hotspots of LSD transmission on the density map, which are mainly located along borders (e.g. Turkey-Greece, Greece-Bulgaria or Serbia-Bulgaria-FYROM) (Figure 33). We also highlighted the seasonality of LSD transmission, with an increased transmission from May to August. [START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF].

Interpolated spread rate values across the study area were highly heterogeneous, but showed a well-structured pattern. The spread rate was higher in the latest frontline areas (with respect to the date of first occurrence), and lower and patchy in the center of the infected regions (Figure 34a).

We identified two modal distributions corresponding to two diffusion processes (Figure 34b). The first diffusion process corresponds to low spread rates (< 15 km/week), while the other diffusion process is linked to high spread rates (> 15 km/week, with a maximum value of 543.6 km/week).

95

Study discussion

The median spread rate of 7.3 km/week is in line with similar results obtained by EFSA in a 2017 report on the LSD outbreak in the Balkans, with an estimated spread rate of 2 km/day (75% percentile) (European Food Safety Authority, 2017). The increased transmission of LSD from May to August is most likely linked to warmer temperatures and increased vector activity.

The hotspots of LSD transmission located at borders might be due to the European regulation at the time, which enabled vaccination only after a first detection in the country. This first detection often occurred along borders with infected countries, and the implementation of vaccine campaigns limited the spread of the disease to other parts of the country.

The results of the study identified two diffusion processes. The first diffusion process with low spread rates (< 15 km/week) corresponds to local-level transmission, linked to vector transmission or contact between infected and naïve animals. The other diffusion process corresponding to high spread rates (> 15 km/week, with a maximum value of 543.6 km/week) indicates "jumps" in the disease frontline, which might be linked to the transport of infected animals (e.g. cattle markets, trade, slaughterhouses). This jump leads to a new introduction of the disease in an unaffected area, followed by local-level diffusion unless effective control measures are implemented. These jumps correspond to hotspots on the density map.

96

The data needed as input for the model is limited (date and geographic location of outbreaks) and, therefore, easy and quick to collect. The data needs to be updated in real-time in order to accurately reflect the displacement of the disease frontline. Delayed or under-reporting can lead to bias by artificially creating jumps in the disease spread due to the absence of data. This can make the maximum estimated spread rate value unstable, and explains why we consider the median value. Spread rate estimates should, therefore, be regularly updated according to newly acquired data. The data sources used in the study were the European Commission's ADNS database and the FAO's Empres-I database. Combining several data sources is necessary to have a more complete dataset, but is a cumbersome task. To avoid multiple notifications by countries and improve inter-connection between notification systems, OIE and the European Commission are currently working on combining the WAHIS and ADNS systems into a single and common database of animal disease outbreaks. This should facilitate the reporting process for health authorities and the access to timely animal disease information online.

In hindsight, we could have improved our model by taking into account vaccination and other control measures that were set up in different Balkan countries as the disease progressed through the region. The implementation of vaccination campaigns increased host immunity and slowed disease spread to unaffected regions. Most countries implemented control measures following the first report of an outbreak in the country. This could explain why most of the observed hotspots are located along borders, which are areas at high risk of disease spread when the neighboring country is affected. Further spread in-land is usually mitigated through the timely implementation of control measures. Another important aspect is the fact that the Neethling vaccine used in the Balkans in 2015-2016 is not a 'Differentiating Infected animals versus Vaccinated Animals' (DIVA) vaccine. This means it is impossible to distinguish the immune response of a vaccinated animal and an infected animal. This incapacity to differentiate between vaccination and infection can impact the surveillance of the disease, the monitoring of its spread and proving disease-free status [START_REF] Beard | Lumpy skin disease: a direct threat to Europe[END_REF]. This is the reason why CIRAD, which is a national reference laboratory for poxviruses in France, is currently working on developing a capripoxvirus DIVA vaccine strain.

The effective control of the spread of LSD remains difficult due to gaps in our knowledge of the disease, particularly relating to transmission modes, efficient serological testing and the lack of a suitable vaccine [START_REF] Beard | Lumpy skin disease: a direct threat to Europe[END_REF]. We still need to understand the transmission dynamics between animals and between herds, and the impact of different environments on this transmission. This is particularly true regarding the role of vector transmission and which vectors are implicated.

Better knowledge on vector transmission would help target vector control measures. The availability of simple, affordable and sensitive serological tests would improve the detection of historically or sub-clinically infected animals, thus improving our knowledge of past and present disease circulation and informing disease-free status. There is, therefore, a need to target future research efforts on these gaps and set up coordinated regional response to limit the risk of reemergence of LSD in Europe, particularly in warmer months during increased vector activity.

The results of the spread rate model were used to inform risk analyses for France conducted by ANSES (ANSES, 2017). The estimate of the spread rate was used to determine the size of the area around a first potential outbreak to consider for vaccination, as well as the size of the vaccine bank necessary to ensure efficient vaccination around this first outbreak. The results of our study were also cited by EFSA to highlight the seasonality of LSD spread [START_REF] Food | Lumpy skin disease: scientific and technical assistance on control and surveillance activities[END_REF].

The spread rate model was designed to be generic and applicable to other infectious diseases that spread with a frontline of infection. It was applied to LSD in the Balkans as presented in the case study, but it was also used by the FEIS to analyze the spread of other diseases of concern such as ASF in Belgium from August 2018 to February 2019 [START_REF] Muñoz | Estimation de la vitesse de propagation de la PPA en Belgique[END_REF]. I was second author of that study, which was led by an expert in modelling.

An R package named "spreadrate" was created by CIRAD modelling experts to facilitate the use of the spread rate model by the FEIS and other health professionals (R, 2019). CIRAD is continuing methodological developments to improve the model, which include the integration of covariates to analyze the effect of environmental and socio-economic drivers, the estimation of the uncertainty linked to the model, and the modelling of the spread rates to establish predictions beyond the study area. Another way to improve the model would be to incorporate simulated outbreak data, as was done by other studies, instead of observed data which might suffer from under or delayed reporting [START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF]. Comparing the two methods (use of simulation data vs use of real data) would provide useful information for further disease spread modelling.

This case study presented an example of a pluridisciplinary collaboration between epidemiologists, EI experts and modelling experts in the aim of characterizing disease spread dynamics and improving prediction models. It illustrates the added-value of integrating end-users in the development and implementation of spatio-temporal models so they can better understand the underlying processes of the model, along with its advantages and limitations, which are useful to interpret the outputs. The FEIS used a tool that was already available through existing collaborations to better characterize the health hazard and improve the quality of the information on the LSD outbreak reported to French authorities and animal health professionals. Collaboration with modelling experts to create an R package with a user-friendly interface allows a better appropriation of the tool by health professionals for rapid analysis of outbreak data.

In hindsight, I would have strengthened the involvement of end-users, specifically EI experts in the implementation but also the design and development of the model. Epidemiologists were involved in its development, but not the leaders of the EI system, which are the main end-users. This implies that the EI leaders of the FEIS have to rely on the epidemiologist of the group that participated in the tool's design to run the model in order to obtain new knowledge and characterize health threats. Modelling is a complex process, but EI needs to ensure the continuity of activity and cannot depend on the availability of specific experts. Therefore, similarly to PADIweb improvement suggestions, a representative group of end-users, including EI team leaders, could have been more involved in the adaptation of the tool to LSD to understand the underlying processes of the modelling approach and be able to run the model using the user-friendly R package and interpret the outputs. However, EI team leaders might not always have available time to directly participate in the design and development process. In this case, the intermediary link between research developments and operational activities (i.e. the epidemiologist involved in the tool's development) should be more proactive in the transmission of knowledge about the tool's process and use. This can be done through training sessions that also include theoretical 98 explanations of the underlying processes to ensure clear and autonomous interpretation of the model's results.

Section discussion

This section is based on two case studies which both present examples of pluridisciplinary collaborations in the context of analysis of health data gathered by EI services to improve the characterization of health threats.

The case study on arboviruses in the Pacific aimed to characterize the change in the epidemiology of dengue, chikungunya and Zika relating to epidemic waves with alternating dominant serotypes, as well as the increase and diversification of outbreaks. Identifying spatial and temporal spread patterns allows a better understanding of the disease dynamics in an insular context like the Pacific region. It enables health authorities to better anticipate epidemic waves and target periods and countries at higher risk depending on the virus or the serotypes.

The case study on LSD in the Balkans provided an estimate of the spread rate of the disease to inform risk analyses of potential introduction into France. This modelling study provided insight about the different transmission modes of diseases linked to various external factors including mobility, and the impact of vaccination on the velocity of spread of the disease.

Both studies implemented a descriptive analysis with time series and maps of outbreaks, allowing the identification of hotspots of viral transmission and disease seasonality. The descriptive analysis of spread patterns of arboviruses in the Pacific was simple to conduct. It provided useful information on spread pattern in a limited amount of time, thus ensuring its compatibility with EI activities. The spread rate model is more complex but was developed ahead of time by researchers. They made sure the model was easy to use and created an R package to ensure rapid implementation and facilitate user appropriation. EI activities require a certain reactivity to minimize delays between the detection and the reporting of the health threats to allow the timely implementation of control measures. This short time span is not compatible with complex and long analyses that would require time and multiple expert inputs. The two case studies illustrate examples of analyses that are quick to implement. Providing timely knowledge on health threats (e.g. spread rate and pattern, seasonality, drivers) also allows experts to better understand the epidemiological phenomenon and target surveillance efforts depending on specific periods or areas at higher risk. This will, in turn, inform decision-makers who will be able to implement targeted control measures.

The arbovirus and LSD case studies provided examples of pluridisciplinary collaborations between epidemiologists, EI experts, entomologists, microbiologists and modelling experts in order to improve the characterization and prediction of health threats. These two case studies illustrate the added-value of integrating EI expertise in the analysis of health data to better inform risk analyses and disease control strategies. In the same sense that including disease experts in risk analyses for decision-making can improve knowledge of the disease system, the inclusion of EI experts can improve knowledge relating to the epidemiological situation of the disease and its context (e.g. past and ongoing circulation in the affected region to better describe seasonality or current immunity, or the epidemiological situation in surrounding regions or at global level to 99 analyze possible sources of introduction). EI experts also have access to the latest information and can update in near real-time the data used for risk analyses.

It is important to strengthen the links between the different domains of disease surveillance and control, from the detection of health threats and their characterization to the analysis of the risk they represent for specific populations and the recommendation of appropriate response measures. The different steps should be closely linked, either by involving the same teams or by promoting collaborations between dedicated experts. The involvement of EI teams in what is referred to as risk analysis varies depending on the country and the institution. Here are two examples of the scope of EI activities in human health at European level with ECDC, and in animal health at national level with the FEIS.

In France, before the ESA Platform and its centralized EI system, French authorities in charge of risk management (Ministry of Agriculture, DGAL) and risk analysis (ANSES) both conducted their own EI internally. This shows that risk detection and characterization are the common first steps to provide timely and complete information for further analysis and management purposes. The creation of the FEIS was meant to centralize EI activities, and strengthen, improve and formalize the system. By including members of DGAL and ANSES into the FEIS team, EI activities benefit from their expertise and perspectives as end-users of the validated and interpreted information generated by the FEIS. However, this collaboration is not bilateral as the FEIS is not involved in risk analyses or discussions relating to risk management.

At European level, the EI team at ECDC is in charge of risk detection and characterization. Contrary to the FEIS, they are also involved in risk analysis, notably the rapid risk assessments (RRA) regularly produced to provide epidemiological information and assess the risk for European Member States. During my EI training at ECDC, I participated in the RRA on dengue in Réunion Island (ECDC, 2018). With the EI team, I provided updated epidemiological information to characterize the outbreak, such as a description of the epidemiological evolution, an epicurve of the number of dengue cases over time and a map of cases worldwide. The RRA brings together complementary expertise, with disease experts from other units and the EI team, to compile a well-informed and rapid risk analysis of a public health situation. Providing real-time information on outbreak occurrence and spread is crucial to correctly reflect the current epidemiological situation and analyze its evolution.

In international organizations such as ECDC and WHO, EI teams take the lead for risk analyses and can request input from disease experts depending on the extent of the risk analysis. This is reflected in the name of the EI team in WHO: the "Detection, Verification and Risk Assessment" team. In contrast, disease experts at ANSES are in charge of risk analyses for animal health in France, with no interaction with the EI team.

Many agencies or institutions involved in human or veterinary health conduct their own targeted EI despite having a dedicated EI team or mandate. For example, disease experts monitor outbreak events of a disease at local, national or global scale to stay informed on the current epidemiological situation of their disease of expertise. By sharing specific expertise, tools and resources, centralizing EI expertise can improve the quality of the information produced, thus better informing decision-makers and authorities in charge of risk management and communication. It facilitates the work through mutualization of expertise, tools and resources, 100 and allows dedicated experts to conduct EI activities in a more efficient way (e.g. provide more detailed and timely information through the use of several information sources or use of automated tools). It does not, however, mean that it should be excluded from other processes such as risk analysis. Links should be established between the detection and characterization of a threat and the analysis of the risk it represents, for instance to ensure that up-to-date data on the epidemiological situation is used for risk analyses.

Pluridisciplinary collaborations with researchers to develop spatio-temporal models analyzing disease data (e.g. disease spread models) provides access of health professionals to innovative data analysis methods aiming at better understanding disease emergence and spread dynamics. This was the case for the spread rate model developed in collaboration with modelers and used in the FEIS' daily EI activities. Creating user-friendly applications, using the R and R Shiny software for example, can further facilitate the use of the tool by health professionals involved in EI on a daily basis. A few examples from my experience include the R package named "spreadrate" created by researchers to facilitate implementation of the spread rate model by health professionals, or the interactive map of disease outbreaks monitored by the FEIS. This interactive map, available on the ESA Platform website 4 , allows health professionals to visualize the evolution of disease outbreaks in Europe using interactive features on a map and time series. This map was developed with researchers from CIRAD which are also involved in the FEIS and understand user needs in terms of visualization of disease data.

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structured information. This unofficial information can complement official sources, such as syndromic surveillance (OE). This combination of sources can improve the quality of the health threat characterization (descriptive analysis with time series and mapping, as well as spread rate modelling) and the subsequent risk analyses. PADI-web has been integrated in the FEIS' activities on an ad hoc basis, while OE strengthened Pohnpei's syndromic surveillance system.

By illustrating a pluridisciplinary approach involving epidemiologists, microbiologists, entomologists and computer scientists, the case studies presented in Chapter 3 focused on the early detection and alerting of health threats, and the case studies from Chapter 4 analyzed these alerts to improve the characterization and prediction of health threats. Each case study mobilized different data, methods and stakeholders. The data collected and the alerts detected through the tools developed in Chapter 3 could feed into the analyses developed in Chapter 4. For instance, OE could go beyond syndromic surveillance and include a media monitoring component derived from PADI-web. The spatio-temporal analysis of arboviruses used EI data from SPC's regional database, which included a significant amount of information from online media sources. The outputs from the analyses presented in Chapter 4 can prompt EI experts to strengthen data collection or improve the sensitivity of the early alerting process depending on at-risk areas or periods for example.

Defining epidemic intelligence

Definition of EI

I agree with the definitions of EI provided by ECDC and WHO, and would define EI as the process of early identification, verification, assessment and reporting of potential health threats in order to timely alert and inform health authorities.

WHO often uses the term EBS instead of EI to refer to the rapid detection of outbreak events, as is the case for the COVID-19 surveillance strategy guidelines (World Health Organization, 2020a).

EBS is an increasingly important component of EI given the recent developments in online communication outlets. Therefore, some organizations will use the term EBS instead of EI, referring to the same process. However, setting up an EBS component requires additional resources. The proportion of EBS and IBS in EI varies from one system to another, and depends largely on the scope of the system, the available resources and the local context (e.g. surveillance capacity, infrastructures).

According to ECDC, EI activities include risk monitoring and risk assessment, but not risk management [START_REF] Paquet | Epidemic intelligence: a new framework for strengthening disease surveillance in Europe[END_REF]. On the contrary, the CAC and the OIE include risk management in the risk analysis process. I believe that the management of the risk should remain separate from its detection and analysis to ensure objectivity and avoid conflict of interests in the recommendation vs the implementation of control measures (Figure 35). Such is the case, for instance, with ECDC and the French animal health authorities.

In the OIE framework, the identification of the health hazard is separate from the risk assessment. I believe this step of detecting, defining and characterizing the health threat is part of EI because the early detection of a health threat requires a specific expertise that I consider distinct from the 103 disease, environment or policy-specific expertise required for risk assessment and management (Figure 35). The characterization is essential because it allows the interpretation of the event through the collection of pertinent information on the context of the outbreak such as affected hosts, detected serotype or past and present circulation. Although EI might not be able to provide pathogen-related information such as host, vector or genetic features, it can provide useful information on the past/present circulation of the disease in/around the affected area and details on the epidemiological situation (e.g. reported cases, mortality, countries and hosts affected, observed spread, changes in the epidemiological situation).

The identification of the hazard is the first step in analyzing the risk it represents. Therefore, we can consider that EI provides information for the first step of risk analysis. In my experience and perspective (which provides the EI point of view as opposed to CAC and OIE frameworks), risk analysis corresponds to risk assessment and comes between risk detection and risk management (Figure 35). If risk assessment includes the release, exposure and consequence assessments leading to a quantified risk estimation, it requires specific disease expertise that differs from the EI expertise (i.e. how to timely detect, verify and characterize health events). It is challenging to set a clearly-defined boundary between EI and risk analysis -nor is it adapted to a global and integrated approach -because the whole process of risk detection and analysis is continuous.

There needs to be at least a rapid and preliminary risk assessment to determine whether the detected health event represents a potential threat or not, without necessarily quantifying this risk. Therefore, the boundary is not clearly defined between EI and risk assessment. In France in the context of animal health, the two are separate and led by dedicated teams. At ECDC, however, the EI team leads the rapid risk assessments in close collaboration with disease-specific experts and teams. As different institutions have different mandates and provide different organizational structures, this does not exclude the possibility of integrating the EI expertise within a larger and pluridisciplinary team which can also include risk assessment and risk management experts. If both are not led by the same team, EI should be closely linked and collaborate with the risk assessment process, i.e. be involved and contribute with timely information on the epidemiological situation. Each step of the risk analysis process should be interlinked with strong interactions and collaborations between experts involved in each activity, as advised in the CAC by WHO and FAO.

104 EI provides real-time information to inform risk analyses (Chapter 3), which in turn generate new knowledge on disease emergence and spread dynamics (Chapter 4) that can help target surveillance activities (Figure 36). For example, EI can enhance its activities during at-risk periods or for at-risk areas for specific diseases identified by mathematical models, which use EI data as input.

Figure 36. Information flow between epidemic intelligence and risk analysis

Epidemic intelligence at national vs international level

Regional or international agencies have set up EI systems that monitor health risks worldwide using different biosurveillance tools such as ProMED or GPHIN. Some countries have set up their own EI system to monitor and detect health threats at both national and international levels to anticipate the possible introduction of emerging pathogens from other regions. National-level EI systems, such as the FEIS for animal health in France, monitor the same signals at international level as supra-national systems, thus creating some overlap with the international agencies. However, these nationally implemented systems also monitor other sources of information through their own networks. The FEIS, for example, aims to provide reports with interpreted and contextual information to better characterize the health threat and facilitate its interpretation by decision makers. The existence of the FEIS is justified by the extra layer of filter, which allows targeting animal health surveillance according to national context. Indeed, needs in terms of animal health surveillance may vary from one country to another depending on strategies and priorities (e.g. priority given to caterogy-1 health hazards or events with geographical proximity to France). In addition, the FEIS monitors several different types of information sources, from official sources to unofficial sources such as media reports, also gathering unofficial communications from its national and international pluridisciplinary network of experts. This allows the FEIS to contextualize the event when reporting and include contextual information (e.g. viral strain involved, previous occurrence and historical circulation of the virus in the area, vector and host distributions, animal mobility). Information from various fields, such as epidemiology, virology or entomology, is also useful to provide context information and a holistic approach considering several perspectives. This reinforces the need for a pluridisciplinary approach to EI, which is why the FEIS team and expert network include experts in epidemiology, veterinary health, informatics, entomology, virology, and many other specialties.

Identification of drivers

Understanding the causes of EID events has become part of a global strategy to address this health threat. When studying the underlying processes, key factors to take into account include human and animal mobility, climate change and vector biology for vector-borne diseases. Animal movements are essential to the economic health of livestock production, but also threaten it by facilitating disease spread. Animal movements are an important pathway of transmission from farm to farm, sometimes over great distances as seen with LSD in the Balkans [START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF].

After characterizing the spatial and temporal patterns of disease emergence and spread, the next step is use mathematical models to identify environmental and socio-economic drivers in order to better understand the underlying processes of disease emergence and spread, and how they are influenced by external factors. This helps improve expert knowledge of the disease dynamics in the aim of targeting efficient control measures and anticipating future outbreaks. The environmental and socio-economic drivers of disease emergence and transmission can become proxies to monitor as sentinels to facilitate disease surveillance. An example is the use of population movements, a key driver of disease spread, in the fight against the spread of the COVID-19 pandemic. Population mapping and modelling can help predict where the disease is more likely to spread and which control methods are more effective. These methods can help improve the timely detection of anomalies and disease emergence, and facilitate the monitoring of spatial and temporal trends.

Over the past several years, I have contributed to several projects aiming to identify drivers of disease emergence and spread. Future developments of the spread rate model, which was used by the FEIS to estimate the spread rate of LSD in the Balkans, include the identification of factors that influence the spread rate. With my colleagues at CIRAD, we have started to develop a second complementary model that would use the estimated spread rates from the first model as input to analyze the impact of various environmental and socio-economic factors such as temperatures, rainfall, mobility and animal density.

Dengue drivers paper:

The identification of drivers of dengue outbreaks was the subject of another study which aimed to identify socio-economic and environmental determinants of dengue outbreaks in Noumea, New Caledonia [START_REF] Zellweger | Socioeconomic and environmental determinants of dengue transmission in an urban setting: An ecological study in Nouméa, New Caledonia[END_REF]. I contributed to this study as part of a 6-months professional internship at IRD in 2014. My work focused on the extraction of environmental drivers from remote sensing images (e.g. vegetation index) and contributing to the principal component analysis (PCA) to identify drivers of dengue outbreaks in the urban setting of Noumea, the capital of New Caledonia. The study demonstrated a correlation between dengue incidence and socio-economic status across the different neighborhoods of Noumea. Higher dengue incidence rates were linked higher unemployment, lower revenue, higher percentage of population born in the Pacific. Identifying key drivers of dengue allows experts to identify areas or time periods at higher risk, i.e. where or during which several risk determinants are present. With this knowledge, health authorities can better target prevention and control measures in at-risk areas and during dengue season.

Another study I'm currently leading is looking into the role of air travel in the risk of importation of dengue into Europe. Given the advent of commercial air travel, modelling of global movements of populations is essential to understand the potential implications of global outbreaks of infectious diseases. Indeed, with over two billion air travelers each year, a pathogen can travel to the other side of the world in just 24 hours so diseases are spreading faster (World Health Organization, 2007). Following my participation in ECDC's rapid risk assessment of the dengue outbreak in Réunion in 2019 and given my previous work on dengue in the Pacific, I designed with ECDC experts a study to analyze the risk of introduction of dengue into Europe through infected air-travelers returning from dengue endemic areas. Indeed, two important factors played a key role in the risk of dengue importation: dengue incidence in the visited country and the volume of passengers travelling from the dengue infected area to Europe. We set up a project in collaboration with French public health authorities (Santé publique France, in Paris and Réunion) to analyze the travel-associated risk of dengue introduction into Europe through a case study of dengue importation from Réunion to metropolitan France following the large outbreak in 2019.

The aim of the study is to estimate the risk of introduction of dengue into France using data on air travelers and local dengue incidence in affected countries. This allows us to target surveillance and inform risk analyses by identifying countries or periods of higher risk of dengue importation depending on travel patterns and ongoing dengue circulation in destination countries. I am leading this study, which is currently ongoing, and will be first author of the upcoming manuscript. I coled the study design, the collection and the analysis of travel and dengue data with the R software.

The COVID-19 pandemic has generated delays in our study due to limited availability of the public health agencies involved, but we aim to publish the project results in 2021. Our project was presented at the 21 st Journées Nationales d'Infectiologie conference in Poitiers, France on 9 th September 2020.

The identification of key drivers of disease emergence and spread can improve prediction models and allows to better anticipate future outbreaks and global pandemics.

Integrating different disciplines and expertise

Society is increasingly calling for decompartmentalization of knowledge and expertise through pluridisciplinary collaborations. Human and animal health are based on complex processes that draw from many disciplines, including medicine, environmental sciences, biology, virology, epidemiology, ecology, farming, and more [START_REF] Harris | An integrative approach to health[END_REF]. Each field of expertise brings to the table a new perspective on cross-disciplinary issues such as disease surveillance. As increasing volumes of diverse health data are generated, disease surveillance needs technical support from data and computer sciences to implement new and innovative technologies aiming to improve health data analysis in the era of big data and data sciences.

To ensure an integrated approach, collaborations can be implemented at several levels:

-the pooling of information and data sources: collect data from diverse and complementary information sources, from both official and unofficial sources including personal communications through each stakeholder's contact network -the pooling of expertise: improve the verification, the characterization and the interpretation of health-related events for risk assessment -the pooling of resources: both human and financial to ensure common strategies, efficient capacity-building, continuous operation of EI activities, and to improve time and cost efficiency of disease surveillance systems

The centralization of pluridisciplinary data, expertise and resources can be done through a project, a platform or a network, to bring stakeholders together and facilitate discussions and data sharing.

Challenges of collaboration:

Although the scientific expertise, innovative technologies and diverse information sources needed to generate real-time EI exist, their integration is suboptimal. Integration of different types of stakeholders and experts with a common goal of early detection and control of disease threats can be challenging. Gathering several institutions with varying, and sometimes contradictory, motivations and interests can be a potential source of discord within a network or project. The level of collaboration usually depends on the willingness of actors involved to share their opinions and open up to other disciplines and perspectives on the issue at hand. It may also vary depending on the type of collaboration, e.g. between different geographic levels or between scientific disciplines.

Despite these challenges, there is a strong need for collaboration between scientific disciplines within research, the involvement of various types of stakeholders (e.g. public/private, animal/human sectors, local/national) through participatory methods, and the integration of different expertise and domains of expertise from disease detection and characterization to risk analyses to better inform risk management. Issues in managing outbreaks of EIDs may stem from the lack of understanding of these pathogens, particularly in the case of unknown pathogens.

Involving the research community, universities and reference laboratories can improve knowledge on the pathogen and its spread dynamics to help in the design and implementation of adequate surveillance and control strategies. The integration of different types of stakeholders can strengthen the surveillance and control process, as demonstrated by the French ESA Platform. For instance, including wildlife services can inform health professionals on diseases circulating in wild 108 animal populations thus accounting for the risk of spillover from wild animals to domestic animals, with the example of ASF in Europe.

Future efforts should focus on bridging the gap between complementary scientific disciplines and between the public and private sectors, embedding this collective knowledge in existing and new technologies, and integrating heterogeneous data while addressing legitimate concerns about data confidentiality [START_REF] Khan | Infectious disease surveillance and modelling across geographic frontiers and scientific specialties[END_REF]. These ethical considerations are essential in handling disease data, but will not be addressed in this manuscript.

Other levels of integration

Geographical integration

Infectious diseases can be detected and controlled at several geographical levels, as demonstrated with mass gathering surveillance. Mass gatherings can be seen as global-to-local-to-global events because they consist of a global-to-local convergence followed by a local-to-global divergence of populations from around the world. However, the implementation of disease surveillance systems at local, regional and global levels are not always well integrated or linked [START_REF] Khan | Infectious disease surveillance and modelling across geographic frontiers and scientific specialties[END_REF]. Successful disease surveillance requires the integration of stakeholders at all geographical scales in the process, and particularly efficient bilateral information flow between clinicians at the local level and health agencies in charge of disease control at regional, national and international levels.

Stakeholders can interact at different geographical levels, and they are different at each level. The local community, farmers and clinicians are key actors at the local level, while international health agencies and agricultural organizations decide on health guidelines and strategies to adopt at the international level. National health agencies and stakeholders are the intermediary link and interact with both the local and the international level. Integrating all these actors in the disease surveillance and control process requires the clear identification of each stakeholder's role and responsibility to enable rapid response. Priorities and the perception of risk can also vary depending on the geographical scale [START_REF] Barnett | Practising co-production and interdisciplinarity: Challenges and implications for one health research[END_REF]. For instance, emerging diseases such as avian influenza can be considered as a priority at international level by international stakeholders and health agencies, but national health agencies and local farmers may show more concern for the daily management of endemic diseases, which might not be considered as a priority but require the most efforts and resources to manage. Decision makers should aim to consider all perspectives, whether local or national, when establishing health priorities, strategies and guidelines.

One of the lessons learnt from the SARS epidemic in 2002 was that weaknesses in disease surveillance and control in one country can endanger countries all over the world, particularly in the current context of globalization and rapid air travel. Indeed, not all surveillance systems are mature enough to handle these large-scale pandemics of novel viruses. Also, recent outbreaks have shown that no country is able to ensure 100% security of its borders due for example to the illegal flow of animals and animal products, as well as migrant human populations [START_REF] Jebara | Surveillance, detection and response: managing emerging diseases at national and international levels[END_REF]. Cross-border collaborations with health authorities from other countries is crucial to facilitate the timely exchange of validated information regarding outbreaks of EIDs and improve regional 109 surveillance and response capacities. In the Pacific region, SPC aims to strengthen the links between public health agencies from different PICTs across the region, for instance by organizing regional training courses and workshops to promote in-person interactions and feedback on the implementation of disease control measures in affected PICTs. Although the focus of SPC's initiatives have been at regional and national level, there is a growing realization that efforts at the sub-national level should be strengthened to include local actors, such as clinicians, to improve rapid detection and containment of disease outbreaks. In general, the majority of investments and efforts to improve surveillance and response capacity tend to focus on regional and national level. It is important to also consider sub-national level which is the source of the information where patients enter the healthcare system and the first line of action to contain outbreaks [START_REF] Nelesone | Strengthening sub-national communicable disease surveillance in a remote Pacific Island country by adapting a successful African outbreak surveillance model[END_REF].

The geographical level of implementation of EI activities depends on the objective and scope of the system. The level of specificity and sensitivity of these objectives will define the granularity of the geographical scale required for the implemented activities.

Some international EI systems can rely on regional hubs to monitor the epidemiological situation at regional level and relay the information at international level. This is the case for the FEIS which relies on regional networks such as the CaribVet and SEGA networks to monitor infectious diseases in French overseas territories in the Caribbean and the Indian Ocean [START_REF] Gongora | The Caribbean Animal Health Network: New Tools for Harmonization and Reinforcement of Animal Disease Surveillance[END_REF][START_REF] Halm | Four years into the Indian ocean field epidemiology training programme[END_REF]. Cross-border collaborations may also help attenuate the geographical disparity in different parts of the world relating to accessibility to real-time sensitive and specific information, most likely linked to technological infrastructures, level of communication and awareness, internet access and available resources.

Multi-sectoral integration (One Health)

Cross-border surveillance can relate to geographical borders but also to sectoral borders. Disease surveillance should also aim at being cross-sectoral, and pool information and expertise from different domains, with the example of the One Health approach. In a One Health approach, the animal-human health interface is based on the principle that humans and animals occupy a continuous health space and are interconnected [START_REF] Barnett | Practising co-production and interdisciplinarity: Challenges and implications for one health research[END_REF]. The threat of zoonotic diseases is exacerbated by several factors including climate change and deforestation, which lead to increased contact between human and animal populations. This growing global threat stresses the need for collaborations between human and animal health authorities. Ideally, in a One Health approach, collaborations should also be established with the environmental sector (e.g. plant health and food safety) but that is outside of the scope of my experience which focuses mainly on human and animal health.

As I discovered during my experience working in both human and veterinary health, EI activities in both sectors face similar challenges and share common objectives and methodologies. The process itself is identical, only the scope changes, as well as the implications in terms of impact and implementation of control measures. Collaborations between human and veterinary health agencies can open access to data relating to both human and animal cases, which can provide a sentinel system as well as a global picture of the disease's epidemiological situation (in the case of zoonoses). They can be implemented at different stages, from the early detection of unusual events to the collaborative design and implementation of control measures.

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There are different degrees of inter-sectoral collaborations between human and veterinary health: sharing reports to sharing data, using common EI tools or databases, sharing resources for the detection, analysis and response to health threats, and defining common strategies for the control of zoonoses. Cross-fertilization and transfer-learning are terms used in informatics to describe the sharing of knowledge and methods between disciplines and sectors. The idea is to benefit from existing tools, knowledge and methods by adapting them to new systems, domains or scales.

Several examples of cross-sectoral collaborations can be cited. The FEIS has set up collaborations with ECDC to share EI methods, networks and information relating to zoonoses such as West Nile.

Animal case counts are now included in ECDC's West Nile reports following their recent One Health approach, and vice versa the FEIS includes human case reports in their West Nile reports.

The FEIS is also involved in cross-sectoral networks such as the ArboFrance network in France (focusing on arboviruses) and the French epidemic intelligence network involving stakeholders in both human and veterinary health. The H2020 MOOD project, coordinated by CIRAD which is a major stakeholder in animal health surveillance in France, stemmed from a call for tender in human health and is founded on a One Health approach. The global pandemic of COVID-19 has also shined a light on several cross-sectoral initiatives such as the support of veterinary laboratories and the provision of testing material and protective equipment.

Other levels of integration to consider when optimizing an EI system include strengthening interorganizational collaborations (e.g. between organizations such as WHO, ECDC, OIE or the European commission) as well as public-private partnerships, and improving inter-institutional communication and transparency through information flow between different hierarchical levels of an institution.

Strategies to facilitate integration

Co-creation of knowledge and shared decision-making are challenging processes which require time, role clarity, continuous attention to power imbalances and constant monitoring [START_REF] Oliver | The dark side of coproduction: Do the costs outweigh the benefits for health research?[END_REF]. Some determinants can facilitate integration initiatives in human and animal health: high motivation among stakeholders, involvement and support of local communities, and the setup of a centralized system to oversee actions [START_REF] Ingemann | Determinants of an integrated public health approach: the implementation process of Greenland's second public health program[END_REF]. On the other hand, examples of constraining determinants are ambiguous program objectives, high turnovers, siloed budgets and work environments, as well as a lack of centralization of information and activities [START_REF] Ingemann | Determinants of an integrated public health approach: the implementation process of Greenland's second public health program[END_REF].

When experts and researchers from multiple disciplines work together and share different opinions on priorities and methodologies, several methods and tools can be used to come to a holistic understanding of the disease system. Below are a few examples.

Organizing group discussions and workshops using consensus methods can lead to a common understanding, common objectives and a common strategy. Disagreements between researchers sharing a common objective can be overcome using consensus methods among experts like the Delphi method [START_REF] Rayens | Building Consensus Using the Policy Delphi Method[END_REF]. This method is used by the ESA Platform when designing 111 surveillance protocols for example, and by the PADI-web team to help health professionals come to a common understanding in regards to methodology and definitions (Arsevska et al., 2016).

D'Amour and his team developed a model of collaboration, which applies to interprofessional and interorganizational collaboration in healthcare organizations [START_REF] D'amour | A model and typology of collaboration between professionals in healthcare organizations[END_REF]. The model is based on four components (governance, shared goals and vision, formalization and internalization) and ten indicators (centrality, leadership, support for innovation, connectivity, goals, client-centered orientation, formalization tools, information exchange, mutual acquaintanceship and trust). It can be used to analyze the ways in which complex systems of actors collaborate, and is based on the concept of collective action in organizational sociology, a type of reasoning for the analysis of political, social and economic processes.

Once actors are willing to cooperate and co-construct a common strategy, the inclusion of social scientists can facilitate the integration process through the implementation of participatory methods [START_REF] Duboz | Systems Thinking in Practice: Participatory Modeling as a Foundation for Integrated Approaches to Health[END_REF]. These methods can enable the appropriate contextualization of research practices and ensure a balanced representation of the roles and relationships of medical, biological, mathematical, and social disciplines. Participatory methods refer to the involvement of communities in defining and prioritizing problems, and in the development of solutions [START_REF] Catley | Participatory epidemiology: approaches, methods, experiences[END_REF]. Participatory modelling is iterative and adaptive, and is necessary to deal with the complexity of issues and topics handled. Such methods are implemented in the H2020 MOOD project to ensure the co-conception of innovative tools based on user needs.

Participatory approaches can include the use of modelling techniques which encourage those involved to clarify which key variables affect the underlying system and the possible outcomes of different strategies. These integrated approaches consider the system as a whole, with interdependent and complex interactions between the human, animal and ecosystem components. The Companion Modelling (ComMod) approach promotes dialog among stakeholders and provides methodological support to integrate a plurality of viewpoints [START_REF] Buchheit | Companion Modelling for resilient water management: Stakeholders' perceptions of water dynamics and collective learning at catchment scale[END_REF]. In the ComMod approach, participatory modelling tools such as role-playing games and agent-based models are used in an iterative and evolving process. Stakeholders are involved in the design of the simulation tools, which are then used to collectively design, simulate and discuss scenarios of change (e.g. implementation of control measures). This approach is implemented through several iterative cycles and is based on participatory and capacity-building workshops which bring together the different stakeholders.

Chapter 6 Perspectives

Perspectives relating to the case studies

My experience has allowed me to take a step back and have a more global approach and understanding of challenges in EI. In hindsight, I have identified several ways of improving the implementation of an integrated pluridisciplinary approach to the design of innovative methods for the collection and analysis of EI data.

The PADI-web case study of COVID-19 could have been improved by replacing manual extraction of relevant terms with an automated software such as BioTex specialized in terminology extraction. Also, a complementary study focusing on adding the new "unknown" category of keywords to PADI-web and conducting a retrospective study of another emergence such as Ebola or SARS would validate our results concerning the vocabulary related to novel pathogen emergence. To facilitate the implementation of newly developed tools such as PADI-web in existing surveillance systems, I would suggest to start with a needs assessment with a representative group of end users in the design of the tool to identify key operational needs and ensure that the tool meets the needs of the users. I would suggest to organize stakeholder meetings to clearly identify all users' needs, validate the tool's design and set up feedback testing loops with users.

Relating to the development and use of electronic tools for the enhancement of mass gathering surveillance, the study could have benefited from an entirely-electronic data collection without relying on paper forms to collect the data at sentinel sites. We could set up an electronic data collection system at sentinel sites using computers (if internet access is sufficient) or mobile phones. Organizing regional workshops on mass gathering surveillance enhancement would improve awareness and sharing of experience among PICT health authorities. SPC's mass gathering enhancement team could benefit from the additional support of public health professionals from other PICTs with previous experience in mass gathering surveillance. This would further strengthen inter-PICT collaborations. Given the need for a sufficiently long pilot phase, SPC could adopt a more proactive approach and directly contact health authorities long before the event to offer assistance and assess the local context and available resources.

In our study on the circulation of arboviruses in the Pacific, we could have included more data spanning over a longer period of time to analyze whether the epidemic wave process was recent or had been ongoing for several decades. I believe we should study the epidemiology of dengue more carefully. SPC's regional database contains 30 years of detailed data on the circulation of dengue serotypes in the Pacific, which could be analyzed to confirm the epidemiology in epidemic waves and study more carefully this change in epidemiology with the continued circulation of DENV-1. Mathematical models that would include environmental and socio-economic drivers, along with phylogenetic studies, could help improve our knowledge of the circulation of dengue and other arboviruses in the Pacific.

Further improvements of the spread rate model include taking into account vaccination and other control measures as well as integrating environmental and socio-economic drivers of LSD spread to improve knowledge of the disease spread dynamics and inform prediction models. The study would also have benefited from a collaboration with local veterinary health agencies who could have provided more precise outbreak data, local expertise and feedback on the response to LSD outbreaks.

The common challenge in my case studies is not only to include end-users in the early stages of tool and method development but to make sure that end-users involved are representative of all future users of the tool or method. This allows to anticipate any future needs in terms of operational use of the tool or implementation of the method. However, involving too many stakeholders from the start of the design and development of the tool can be counter-productive.

Implementing an iterative process of feedback loops which would include an increasing number of stakeholders as the process advances would be more productive. And the support from social sciences can further facilitate the integration of user needs to ensure sustainable use of the tool or method.

Improvement of the FEIS: During my four years as team leader of the FEIS, I have identified several gaps which represent possible improvements of the EI system for animal health in France. The main challenge is the availability of sufficient and sustained resources. Assuring a permanent contract for the team leaders would allow them to invest fully into the system, benefit from the network of contacts that they have built over time and bring their full expertise to the system. Additional resources could allow the team leaders to implement the ideas of improvements they identified but have no spare time to set in motion due to the increasing amount of time needed to monitor daily signals and act when alerts are detected. These ideas, for my part, include more involvement in research projects such as the MOOD project which would allow the FEIS to benefit from tailored innovative methods and tools developed by the research community. The system needs to keep up with new technologies to facilitate the management of increasing amounts of heterogeneous data. I also believe the FEIS could benefit from the One Health collaborations I set up during my four years because, in my experience, EI in both human and animal health sectors have similar methods, tools and challenges. An example of collaboration is with the French national epidemic intelligence network with other agencies involved in EI (mainly human health) such as the Ministries of Health and Foreign Affairs, the Pasteur Institute, Santé publique France, and the epidemiology and public health center of the French armies (CESPA). The FEIS would also benefit from strengthening its network of national and international experts to improve access to timely local information on new health threats and improve the bilateral flow of information and feedback with experts.

It is important to note that the potential implementation of such suggested improvements will be conditioned by available resources and time, which are both determined by the health agencies' strategy and priorities in terms of EI.

Exploring new data streams

The rapid and global expansion of the internet and mobile devices has provided opportunities to increase communication between public health stakeholders and the general public, including people attending mass gathering events for instance, through informal sources such as blogs, phone applications or social media platforms [START_REF] Khan | Infectious disease surveillance and modelling across geographic frontiers and scientific specialties[END_REF]. Electronic media, health expert forums and blogs, as well as internet search queries offer easily-accessible sources of unstructured information, which can provide local and near real-time information relating to suspected or confirmed disease outbreaks.

Citizen data has become an area of focus to broaden the extraction of health data to include participatory platforms and social media (e.g. FluNearYou, Twitter). An example is the DIZIE project developed in Japan which monitors syndromes through Twitter [START_REF] Collier | Syndromic classification of twitter messages[END_REF]. The implication of citizens in the collection of information through the use of social media or mobile phone applications will increase the amount of timely and accessible raw data for the monitoring of health threats, which is particularly important in high-risk contexts such as mass gatherings. However, the lack of health-related knowledge of the general public might lead to non-specific or non-accurate data, requiring expert verification. Broadening the scope of surveillance through citizen involvement can also enhance disease surveillance, for instance during mass gatherings, and create an opportunity for a bilateral exchange of information to collect data and inform participants of the event [START_REF] Nsoesie | New digital technologies for the surveillance of infectious diseases at mass gathering events[END_REF].

Through EBS, these data sources can help overcome some of the limitations of traditional surveillance systems, including delays in reporting, inconsistent population coverage or poor sensitivity for new and emerging diseases. These sources can complement traditional disease surveillance systems by allowing health professionals to detect weak signals across borders and thereby create awareness at an early stage of EID risks.

EI experts are also turning to metadata in addition to data relating directly to health events. These metadata include for instance social media trends or Google search queries. For instance, ECDC launched early October 2020 a new application called "epitweetr", an automated R-based tool for the early detection of public health threats from Twitter data. This indirect way of detecting unusual events can improve early alerting and trigger further investigation by switching from metadata to direct health data (e.g. syndromic surveillance, unofficial communication with local health authorities). The monitoring of metadata allows EI authorities to amplify weak signals and improve the coverage of EBS beyond unofficial sources such as electronic media. This paves the way for a shift in the EI paradigm and approach, proof that the EI process is evolving to adapt to the context of big data and new technologies (Figure 37).

Integrating new technologies

Integrating new and innovative technologies can further improve EI activities, thus highlighting the added-value of the involvement of EI teams in research projects. Automated tools for data collection, analysis and reporting have been illustrated in this manuscript, with the example of OE and PADI-web. Automation of tools can considerably improve the timeliness of early detection and alerting, but human moderation remains essential to validate and interpret the signals to recommend efficient control or prevention measures. The use of biosurveillance tools can complement traditional disease surveillance by allowing health professionals to detect weak signals and create awareness at an early stage of EID risk. Recent efforts to centralize existing methods relating to biosurveillance tools in platforms, such as EIOS or the H2020 MOOD project, have further facilitated the process of integrating scientific results and innovations into EI processes. In the context of mass gatherings, easy to use point-of-care diagnostic tests will facilitate diagnosis of syndromic events. The use of mobile phones and portable wireless sensor networks to increase wireless coverage have been shown to be efficient by decreasing data entry time and reducing costs [START_REF] Yu | The development and evaluation of a PDA-based method for public health surveillance data collection in developing countries[END_REF].

As previously highlighted, EI is a complex and time-consuming process. Integrating additional data sources and new technologies will require additional resources to which the system should adapt to avoid increasing the burden on experts in charge of conducting daily EI activities. The integration of new sources and technologies should be adapted to the context and the available resources. For instance, it would be counterproductive to integrate an automated tool for the monitoring of Google search queries in developing countries where internet coverage might be limited. In such contexts, efforts should focus more on improving the performance of the existing surveillance systems than on adding new components to the system.

Anticipating the emergence of unknown viruses

In 2016, WHO's Research and Development Blueprint was set up to reduce the time for the development, assessment and authorization of medical countermeasures for a list of priority diseases with epidemic potential for which there are no or limited medical countermeasures available (World Health Organization, 2018a). In 2018, to respond to the growing threat of new viral emergence, the WHO acknowledged the possibility that a serious international epidemic could be caused by a currently unknown pathogen by adding "disease x" to its list of priority diseases (World Health Organization, 2018a).

The monitoring of weak signals, such as increased syndromic activity or an unusual syndromic event with an unknown etiology, is crucial to timely detect emergences of new and unknown viruses. This can be done by increasing the scope and sensitivity of biosurveillance tools such as PADI-web. The PADI-web case study illustrated that future efforts should focus on clearly defining keywords for the detection of "unknown" or "mysterious events". Given the current risk of emergence of new diseases, we should aim to integrate weak signals in EI systems to improve the early detection and monitoring of emergence of unknown pathogens. Human moderation is crucial to interpret the increasing number of relevant signals detected, particularly if we include weak signals relating to syndromes. Adequate resources should, therefore, be made available to implement this new integration. The integration of weak signals in existing EI systems and the interpretation of the detected signals both require pluridisciplinary efforts to improve the system's performance.

Monitoring viral mutations:

In addition to the early detection of emergence events, viral mutations should also be closely monitored as they can significantly worsen the impact of outbreaks by increasing the vector competence, the virulence of the virus or its resistance to treatments [START_REF] Tsetsarkin | A Single Mutation in Chikungunya Virus Affects Vector Specificity and Epidemic Potential[END_REF]. An example of unexpected genetic mutations in circulating viral strains is the E1-A226V mutation that occurred in the ECSA CHIKV strain, which increased vector competence and was responsible for a large outbreak in La Réunion in [2005][2006]. These genetic mutations are difficult to predict and highlight the need for strengthened pluridisciplinary collaborations in order to closely monitor the evolution of viral strains through phylogenetic studies.

From reactive to proactive

Public health care systems seem to focus more on treating the sick than on protecting public health by anticipating and setting up prevention measures. This is, in part, due to limited resources available. In contrast to human health, veterinary health seems to be much more focused on preventive veterinary medicine, with the health of the population being more important than the health of the individual. It seems control measures such as travel bans are implemented much more rapidly than in human health. Another difference lies in the harmonization and standardization of control measures. In animal health, there is a common set of measures to be applied in case of an animal disease by each Member State of the EU, most of which are harmonized across the world by OIE. In the fight against COVID-19, different countries implemented different movement restrictions at different levels of in-country disease circulation resulting in undesired movements and further possible spread of the disease. Also, governments perceiving socio-economic risks might be reluctant to quickly and fully disclose information about risks of infectious diseases of international concern.

Pluridisciplinary collaborations between scientific disciplines is one thing. The next level is interdisciplinarity between researchers and policy-makers to better translate scientific results into adapted policies and implement rapid and efficient response measures. Another challenge is the difference in timespan between research, funding and policy-making. Research funding is often quite short compared to research agendas [START_REF] Barnett | Practising co-production and interdisciplinarity: Challenges and implications for one health research[END_REF]. In addition, policy makers often require rapid input from researchers to design adapted strategies and implement efficient control measures. The success of researchers is often judged by how fast they can deliver.

Research projects rarely last longer than five years. However, interdisciplinarity and participatory research often require more time to be successfully implemented within research projects.

Integration of surveillance across geographical and sectoral borders in a One Health approach, involvement of stakeholders at both local, national and regional level, and modelling across scientific specialties could produce the first real-time risk detection and assessment platform that could strengthen awareness of global EID threats. This integrated platform could improve rapid detection, assessment and reporting of infectious disease threats of international concern, enable timely and coordinated implementation of response measures, provide mathematical modelling to predict the emergence and spread of infectious diseases and facilitate the surveillance through the use of proxies, simulate the impact of different scenarios of health interventions, serve as a foundation for scientific research and innovation, and strengthen engagement between the scientific community and decision-makers at local, national, and global levels [START_REF] Khan | Infectious disease surveillance and modelling across geographic frontiers and scientific specialties[END_REF].

Given the increasing threat of EIDs illustrated by three coronavirus emergences in the past two decades, disease surveillance and control strategies should dedicate more efforts and investments into anticipating future emergence of both known and unknown disease threats. This requires a switch from reactive to proactive, and a strengthening of prevention and preparedness measures.

Particularly in a time of global COVID-19 pandemic, it is important to draw lessons from the recent novel coronavirus emergence and the impact of response to better prepare for the next emergence. As mentioned, the timespan of research efforts to better understand and characterize the health threat is not compatible with the need for rapid response and the decision-makers' need for quick answers. To improve preparedness, an arsenal of existing generic tools can be consolidated and made available through regional or global platforms to provide timely knowledge and feedback to decision-makers. Future efforts should also focus on better anticipating the risks by identifying clear drivers of disease emergence, whether environmental, social or economic. Anticipating future risks, whether known or unknown, includes better prediction through modelling and other tools, but also purchase and stocking of vaccines (if available), medications and protective gear in preparation of future emergences.

Pandemics are on the rise and we should act at the root of the issue, go from predicting to preventing. Health experts have been warning about disease x for some time now. Instead of focusing on the response to individual disease emergence, we should aim to understand and contain the underlying processes that drive the emergence and global spread of new emerging pathogens in a One Health context, for instance by focusing on the spillover from wildlife and the human-animal interface. Also, awareness of health issues at stake and the potential consequences of delayed implementation of countermeasures can be raised by better informing decisionmakers and governments, and strengthening the link between them, researchers and EI experts. Goer et al., 2018)

  Mots clés : veille sanitaire, détection précoce, maladies infectieuses, intégration, pluridisciplinaire Annexes ........................................................................................................................................ Annex 1. The interface of the EpidVis tool (Fadloun et al., 2020)............................................ Annex 2. The interface of the EpidNews tool (Goer et al., 2020) ............................................ Annex 3. Article for case study n°1 -Valentin et al., 2020 ...................................................... Annex 4. Surveillance form for mass gathering enhanced surveillance at the 8 th Micronesian Games in Pohnpei, Federated States of Micronesia (White et al., 2018) ................................ Annex 5. Article for case study n°2 -White et al., 2018 .......................................................... Annex 6. Article for case study n°3 -Roth et al., 2014 ............................................................ Annex 7. Article for case study n°4 -Mercier et al. 2018 ........................................................

Figure 1 .

 1 Figure 1. Impact of early detection and rapid response measures on the epidemiological curve of an outbreak (source: ECDC) European Member States have long-established disease surveillance systems that provide traditional surveillance based on formal indicators. In May 2006, Member States committed to comply with WHO's 2005 revised IHR and have developed and/or strengthened their epidemic intelligence activities (World Health Organization, 2008b). Countries have had to improve their detection and response capacities to control the emergence and spread of infectious diseases. Some countries have strengthened existing surveillance systems by setting up epidemic intelligence activities.

Figure 2 .

 2 Figure 2. Indicator-based and event-based components of epidemic intelligence[START_REF] Barboza | Evaluation des systèmes d'intelligence épidemiologique appliqués à la détection précoce des maladies infectieuses au niveau mondial[END_REF]

  The ESA Platform counts six founding members, with four additional members added by 2018. These members represent different sectors of animal health: the General Directorate for Food (DGAL, part of the Ministry of Agriculture), farmers (La Coopération agricole, GDS France), veterinarians (French society for veterinarian technical groups -SNGTV), scientific support organizations (the Agency for Food, Environmental and Occupational Health & Safety -ANSES, CIRAD and the National Research Institute for Agriculture, Food and Environment -INRAe), laboratories (French association of public veterinary laboratories 1 https://www.plateforme-esa.fr/

Figure 4 .

 4 Figure 4. ESA Platform logo

Figure 5 .

 5 Figure 5. Members of the French animal health epidemiological surveillance platform (ESA Platform)

Figure 6 .

 6 Figure 6. Working groups of the ESA Platform

Figure 7 .

 7 Figure 7. Logo of the French epidemic intelligence system

Figure 8 .

 8 Figure8. Process of the French epidemic intelligence system[START_REF] Mercier | The French Epidemic Intelligence System: comparing disease surveillance at national and international level using data from the Program for Monitoring Emerging Diseases[END_REF]

Figure 10 .

 10 Figure 10. The logo of PADI-web

Figure 12 .

 12 Figure12. PADI-web interface viewing the news article content with identified extraction information and keywords; 1) basic information of the news article (title, publication date, link to the original content), 2) list of epidemiological entities, 3) class label predicted by the classifier, 4) tagged epidemiological entities and 5) details on the selected location(Valentin et al., 2020b)

Figure 13 .

 13 Figure 13. Frequency of the different categories used to describe the COVID-19 outbreak (above), and stepped curve of the daily number of COVID-19 news articles retrieved by PADI-web (below). The paper entitled "Monitoring online media reports for the early detection of unknown diseases: insights from a retrospective study of COVID-19 emergence" was submitted to Transboundary and Emerging Diseases in April 2020 and accepted in July 2020. It is currently in press. The latest version of the paper is available in Annex 3.

Figure 14 .

 14 Figure 14. (left) Crowds of people at the London 2012 Olympic Games Park in Stratford in August 2012; (right) Muslim pilgrims at the Ka'aba during the Hajj in Makkah, Saudia Arabia in October 2013[START_REF] Memish | Mass gatherings medicine: international cooperation and progress[END_REF] -© 2014 Corbis)

57

  Since 2012, SPC has provided support to Pacific health authorities for the enhancement of disease surveillance during the following events:  11 th Festival of Pacific Arts in the Solomon Islands (2012),  9 th Pacific Mini-Games in Wallis and Futuna (2013),  8 th Micronesian Games in Pohnpei State, Federated States of Micronesia (2014),  3 rd International Conference on Small Island Developing States (SIDS) in Apia, Samoa (2014),  Commonwealth Youth Games in Apia, Samoa (2015),  12 th Festival of the Pacific Arts in Guam (2016),  10 th Pacific Mini-Games in Vanuatu (2017), and  9 th Micronesian Games in Yap State, Federated States of Micronesia (2018).

Federated

  of Micronesia (FSM) is a Pacific Island country located north of Papua New Guinea. It is made up of four independent states: Yap, Chuuk, Kosrae and Pohnpei (Figure15). The capital of FSM is Palikir, located in Pohnpei State. FSM covers 701 square kilometers and the population was estimated at 103 000 inhabitants in 2010 (the Pacific Community, 2017; World Health Organization, 2017a). Located in the Eastern part of FSM, Pohnpei is the second largest 59 state after Chuuk. It is made up of the main island of Pohnpei, the largest island in FSM with 129 square miles, and eight smaller outer islands[START_REF] Ichiho | Cancer in Pohnpei State, Federated States of Micronesia[END_REF].

Figure 15 .

 15 Figure 15. Map of the Federated States of Micronesia (SPC's Member Map -the Pacific Community 2017)

Figure 16 .

 16 Figure 16. 8th Micronesian Games logo

Figure 17 .

 17 Figure 17. Supporting local health authorities with manual data entry from surveillance forms into the online OE application.

Figure 19 .

 19 Figure 19. Distribution of syndrome cases reported in rural and urban sentinel sites, Pohnpei State, 17 th July to 6 thAugust 2014[START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF].

Figure 20

 20 Figure 20 presents the distribution of syndrome cases (in blue) and laboratory samples (in green).

Figure 20 .

 20 Figure 20. Comparison of daily syndrome counts and laboratory sample distributions, Pohnpei State, 17 th July to 6 thAugust 2014 (the grey time period corresponds to the Games)[START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF].

  Arboviruses are viruses transmitted by arthropods, mainly mosquitoes. The three most important arthropod-borne viral (or arboviral) diseases affecting the Pacific region are dengue, chikungunya and Zika.

Figure 22

 22 Figure 22 presents the spatial distribution of Aedes mosquitoes (subgenus Stegomyia), dengue vectors and possible vectors of chikungunya and Zika, in the Pacific region as provided by the Pasteur Institute of New Caledonia in 2014.

Figure 22 .

 22 Figure22. Distribution of dengue vectors in the Pacific (Ae: Aedes; colors correspond to the three main mosquito species Aedes aegypti, Aedes albopictus and Aedes polynesiensis; the presence of another species is noted with a letter)[START_REF] Teurlai | Modélisation multi-échelle de la dynamique spatiale de la Dengue : application à la Nouvelle-Calédonie et à la région Pacifique[END_REF].

Figure 23 .

 23 Figure 23. Spread of dengue, chikungunya and Zika viruses in PICTs from 2007 to 2014 (Isl = island; plot marks represent outbreak occurrences) (Cao-Lormeau et al., 2014).

Figure 24 .

 24 Figure 24. Distribution of dengue cases by country and serotype, from 1917 to 2009 (size of squaresproportional to the number of reported cases)[START_REF] Teurlai | Modélisation multi-échelle de la dynamique spatiale de la Dengue : application à la Nouvelle-Calédonie et à la région Pacifique[END_REF].

Figure 25 .

 25 Figure 25. Map of newly reported dengue, chikungunya and Zika virus outbreaks or circulation in the Pacific regionfrom 1 st January 2012 to 17 th September 2014(Roth et al., 2014a).

Figure 26 .

 26 Figure 26. Incidence and etiology of newly reported mosquito-borne virus outbreaks and circulation by semester in the Pacific region from 1 st January 2012 to 17 th September 2014 (S: semester, semester 2 in 2014 is not complete and onlyincludes reports for 2 out of 6 months)(Roth et al., 2014a).

Figure 27 .

 27 Figure 27. Map of the known distribution of Aedes (Stegomyia) mosquitoes, vectors of dengue and possible vectors of chikungunya and Zika viruses in the Pacific region as of early October 2014 (Roth et al., 2014a). The paper, entitled "Concurrent outbreaks of dengue, chikungunya and Zika virus infections -an unprecedented epidemic wave of mosquito-borne viruses in the Pacific 2012-2014" was published in 2014 in the journal Eurosurveillance. The full paper of Roth et al., 2014a is available in Annex 6.

  The disease was limited to Sub-Saharan Africa until 1986 when it spread to the Middle East, Jordan, Egypt and Iraq in 2012-2013. LSD reached Turkey for the first time in 2013 and spread to all parts of the country in 2014. LSD then followed two routes of transmission, North and West. The first route of transmission expanded North spreading to Dagestan, Russia in 2015 and Armenia in 2016. The second route spread West to the Eastern part of Greece in 2015 and to other Balkans countries.

Figure 28 .

 28 Figure 28. Map of LSD outbreaks reported in Europe and Turkey from April 2015 to November 2017 (data sources: European Commission ADNS & FAO Empres-I; figure source: ESA Platform)

Figure 29 .

 29 Figure 29. Neighboring (a), nearest distance (b) and Delaunay (c) methods for spread rate quantification[START_REF] Tisseuil | Evaluating methods to quantify spatial variation in the velocity of biological invasions[END_REF].

Figure 31 .

 31 Figure 31. Map of LSD outbreaks according to the week of occurrence (a) and map of the interpolated week of first invasion (b)[START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF].

Figure 32 .

 32 Figure 32. The estimated surface of the first date of invasion using TPSR. The X and Y axes represent space (km) and the z axis represents time (FA: first arrival or number of days since the first outbreak). Each point represents a reported outbreak.

Figure 33 .

 33 Figure 33. Time series of the number of reported outbreaks per month (a) and the density map of reported outbreaks (b) of LSD in the Balkans from May 2015 to August 2016 (FYROM = Former Yugoslav Republic of Macedonia)[START_REF] Mercier | Spread rate of lumpy skin disease in the Balkans, 2015-2016[END_REF].

Figure 34 .

 34 Figure 34. Lumpy skin disease spread rate in the Balkans from May 2015 to August 2016. (a) Map of estimated spread rates: the colored region represents the convex hull encompassing the data points, the small arrow heads represent the direction of spread; (b) probability distribution: the grey dots on the x axis represent the observed data (FYROM = Former Yugoslav Republic of Macedonia) (Mercier et al., 2018). The paper, entitled "Spread rate of lumpy skin disease in the Balkans, 2015-2016", was published as a short communication in the journal Transboundary and Emerging Diseases in 2018. The full paper Mercier et al., 2018 is available in Annex 7.

Figure 35 .

 35 Figure 35. My proposed framework of epidemic intelligence and risk analysis, compared to the CAC and OIE frameworks

Figure 37 .

 37 Figure 37. New paradigm of the epidemic intelligence process The context of the COVID-19 pandemic has promoted open sciences, with open access to COVID-19 related data provided by numerous institutions and agencies, and at different geographic levels. This open access to data has provided a more complete dataset available for health professionals worldwide to analyze and interpret. However, it has also led to the possibility of information flows containing unverified and unvalidated data.

Annexes Annex 1 .

 1 Figure 38 presents the web interface of the EpidVis tool: a) a KEYWORD MANAGER allows domain experts to express their knowledge, b) a QUERY BUILDER helps domain experts to build and launch queries, c) results are shown in the QUERY RESULT VIEW, d) a SUGGESTION VIEW helps to integrate external knowledge to the KEYWORD MANAGER (e). A toolbar enables interaction with the KEYWORD MANAGER. Buttons are available to launch the QUERY BUILDER (f) and the SUGGESTION VIEW (g). Files are created, saved, and loaded with specific buttons (h).

Figure 38 .Annex 2 .

 382 Figure38. The interface of the EpidVis tool(Fadloun et al., 2019)

Figure 39 .

 39 Figure39. The interface of the EpiNews tool(Goer et al., 2018)

  Figure 1: Impact of early detection and rapid response measures on the epicurve of an outbreak ......................................................................................................................................... Figure 2. Indicator-based and event-based components of epidemic intelligence ....................... Figure 3. Epidemic intelligence framework (EWRS: early warning and response system) ............Figure 4. ESA Platform logo ............................................................................................................ Figure 5. Members of the French animal health epidemiological surveillance platform .............. Figure 6. Working groups of the ESA Platform ............................................................................... Figure 7. Logo of the French epidemic intelligence system ........................................................... Figure 8. Epidemic intelligence process of the French epidemic intelligence system ................... Figure 9. Comparative table of existing biosurveillance tools including animal health threats, according to their scope and data processing approaches. ........................................................... Figure 25. Map of newly reported dengue, chikungunya and Zika virus outbreaks or circulation in the Pacific region from 1 st January 2012 to 17th September 2014 ................................................ Figure 26. Incidence and etiology of newly reported mosquito-borne virus outbreaks and circulation by semester in the Pacific region from 1 st January 2012 to 17 th September 2014 ...... Figure 27. Map of known distribution of Aedes (Stegomyia) mosquitoes, vectors of dengue and possible vectors of chikungunya and Zika viruses in the Pacific region as of early October 2014 Figure 29. Neighboring (a), nearest distance (b) and Delaunay (c) methods for spread rate quantification .................................................................................................................................. Figure 30. Summary of the three main processes to interpolate and predict the date of first invasion, and to estimate the spread rate ...................................................................................... Figure 31. Map of LSD outbreaks according to week of occurrence (a) and map of interpolated week of first invasion (b) ................................................................................................................. Figure 32. The estimated surface of the first date of invasion using TPSR. .................................... Figure 33. Time series of the number of reported outbreaks per month (a) and the density map of reported outbreaks (b) of LSD in the Balkans from May 2015 to August 2016 ......................... Figure 34. Lumpy skin disease spread rate in the Balkans from May 2015 to August 2016 .......... Figure 35. My framework of epidemic intelligence and risk analysis, compared to the CAC and OIE frameworks ............................................................................................................................. Figure 36. Information flow between epidemic intelligence and risk analysis ............................ Figure 37. New paradigm of the epidemic intelligence process ...................................................

Figure 10. PADI-web logo ............................................................................................................... Figure 11. PADI-web process .......................................................................................................... Figure 12. PADI-web interface viewing the news article content with identified extraction information and keywords ............................................................................................................. Figure 13. Frequency of the different categories used to describe the COVID-19 outbreak and stepped curve of the daily number of COVID-19 news articles retrieved by PADI-web. ............... Figure 14. Crowds of people at the London 2012 Olympic Games Park in Stratford in August 2012 and Muslim pilgrims at the Ka'aba during the Hajj in Makkah, Saudia Arabia in October 2013 ... Figure 15. Map of the Federated States of Micronesia .................................................................. Figure 16. 8th Micronesian Games logo ......................................................................................... Figure 17. Supporting local health authorities with manual data entry from surveillance forms into the online OE application. ....................................................................................................... Figure 18. SPC's process map for the enhancement of disease surveillance in the framework of mass gatherings .............................................................................................................................. Figure 19. Distribution of syndrome cases reported in rural and urban point-of-care sentinel sites, Pohnpei State, 17 July to 6 August 2014 ............................................................................... Figure 20. Comparison of daily syndrome counts and laboratory sample distributions, Pohnpei State, 17 July to 6 August 2014. ..................................................................................................... Figure 21. Comparison of the CAC and OIE frameworks for risk analysis ...................................... Figure 22. Distribution of dengue vectors in the Pacific ................................................................ Figure 23. Spread of dengue, Chikungunya and Zika viruses in PICTs from 2007 to 2014 ............ Figure 24. Distribution of dengue cases by country and serotype, from 1917 to 2009................. Figure 28. Map of LSD outbreaks reported in Europe and Turkey from April 2015 to November 2017 ............................................................................................................................................... Figure 38. The interface of the EpidVis tool.................................................................................. Figure 39. The interface of the EpiNews tool ...............................................................................

Table 1

 1

. Terms used to describe SARS-CoV-2 and COVID-19 in the corpus and their corresponding category after manual classification. .............................................................................................. Table 2. Case definitions for the four identified syndromes of the Pacific Syndromic Surveillance System ............................................................................................................................................. Table 3. List of original and new syndromes and corresponding suspected diseases for the 8th Micronesian Games enhanced surveillance system .......................................................................

  There have been three emergences of new coronaviruses causing large outbreaks in the past two decades. In 2002-2003, an outbreak of

	Severe Acute Respiratory Syndrome (SARS) affected 29 countries and resulted in more than 8 000
	cases and 750 deaths reported (World Health Organization, 2003). Since its emergence in
	September 2012, the Middle East Respiratory Syndrome coronavirus (MERS-CoV) has caused over
	2 494 cases and over 800 deaths in 27 countries (World Health Organization, 2019). The recent
	emergence of the SARS-CoV-2 virus responsible for coronavirus disease 2019 (COVID-19)

pandemic is the latest example. Following the detection of its emergence in Wuhan, China, the virus rapidly spread worldwide with 31 091 469 cases and 961 352 deaths reported as of 21 st September 2020 (European Centre for Disease Prevention and Control, 2020).

Table 1 .

 1 Terms used to describe SARS-CoV-2 and COVID-19 in the corpus and their corresponding category after manual classification.

	Category	Terms
	coronavirus coronavirus, betacoronavirus, "coronovirus"
	mystery	mystery, mysterious, unidentified, undocumented, disease x, unknown, abnormal, unexplained

pneumonia pneumonia, respiratory outbreak, lung disease, respiratory tract illness, respiratory illness, respiratory infection, pneumonia-like disease, upperrespiratory illness, respiratory condition, lung infection, pneumonia-like cases, pneumonia-like illness, respiratory virus, lung virus, pneumonia-like virus, pneumonia-causing virus, pneumonia-like virus technical 2019-ncov, 2019 novel coronavirus, cov2019, cov19, covid-19, n-cov2019, ncov, ncov2019, novel coronavirus 2019

Table 3 .

 3 List of original and new syndromes and corresponding suspected diseases for the enhanced surveillance system of the 8th Micronesian Games.

	Original syndromes		Addition syndromes for enhanced surveillance
	 Acute fever and rash (AFR) (measles,	 Foodborne disease outbreak (bacterial or
	dengue,	rubella,	meningitis,	viral gastro-enteritis)
	leptospirosis)				 Fever and jaundice (hepatitis A infection)
	 Influenza-like illness (ILI) (influenza	 Heat-related illness (heat cramps, heat
	and other viral of bacterial respiratory	exhaustion and heat stroke)
	diseases)
	 Prolonged fever (typhoid fever,
	dengue, leptospirosis, malaria)
	 Watery diarrhea (cholera)
	 Non-watery diarrhea (viral or bacterial
	gastroenteritis	including	food
	poisoning and ciguatera)

  [START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF]. Enhancing surveillance systems and providing daily situation reports with close monitoring of syndrome trends provided reassurance to public health officials and event organizers.The paper, entitled "Mass gathering enhanced syndromic surveillance for the 8 th Micronesia Games in 2014, Pohnpei State, Federated States of Micronesia" was published in 2018 in the Western Pacific surveillance and response journal. The full paper of[START_REF] White | Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia[END_REF] is available in Annex 5.

Hoy et al., 65 2015;

  April 2016, LSD reappeared in Greece and spread to Bulgaria and North Macedonia. It then spread to Serbia, Kosovo and Albania in June 2016, and in Montenegro in July 2016. A total of 7 483 outbreaks of LSD were reported in 2016 in seven Eastern European countries (European Food Safety Authority et al., 2019). The disease continued to circulate in North Macedonia, Montenegro and Albania in 2017 with 385 reported outbreaks. The last outbreak of LSD in Europe was reported in Montenegro in October 2017. 88 Over 12 000 animals were reported affected by the disease, and most outbreaks were reported between May and August (European Food Safety Authority, 2017).

PADI-web detected COVID-2019-related information in Chinese and English news articles on 31 st December 2019, the same day as the first official notification by Chinese health authorities of pneumonia-like cases. HealthMap also detected the first COVID-19-related news article on the 31 st December 2019. However, ProMED detected news relating to the emergence on 30 th December 2019, one day before official notification.

https://www.spc.int/phd/epidemics/

https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:31982L0894&from=FR

https://www.plateforme-esa.fr/page/vsi-carte-interactive-multi-maladies

Remerciements

Chapter 5 Discussion

Summary of my contributions

In this manuscript, I have illustrated examples of pluridisciplinary collaborations at different steps of the EI process, from data collection to data analysis and reporting. I presented four case studies from my seven years of experience working in EI in research institutes and intergovernmental organizations, in human and animal health, in the Pacific and Europe. Two of these case studies (Chapter 3) involved the development of automated tools to facilitate EI activities in two different contexts: routine EI for animal health and enhanced surveillance for human mass gatherings. The two other case studies (Chapter 4) focused on the analysis of EI data to characterize the health threat, by providing information on spatio-temporal trends or estimated spread rates.

In these case studies, I contributed on two levels: a methodological level and a meta-level centered on the integration of expert knowledge.

First, I took part in the development of innovative methodologies, their translation into a userfriendly tool and the integration of this tool in existing systems. By representing end-users, I ensured the tool fit their needs and helped translate these needs into technical requirements. This contributed to the development of user-friendly and functional features for a sustainable use of the tool by end-users and an efficient implementation in existing surveillance systems.

Second, I promoted the integration of expert knowledge and the inclusion of end-users in the tool development process. I was also instrumental in the global consolidation of the different methodological innovations by sharing the developed tools and methods in a One Health context through networks, in the context of research projects, or at scientific meetings and conferences. This allows the tools and methods to be used by other stakeholders and adapted to other contexts such as human health or plant health. For instance, the PADI-web tool developments were shared with the French plant health surveillance platform so that they may benefit from developments in automated monitoring of information sources on the Internet. Another example is the H2020 MOOD project which provides a platform to share existing tools, including PADI-web and the spread rate model, and co-constructing new tools with health professionals from other disciplines.

These developments can also be integrated in existing EI systems. PADI-web provides timely information from unofficial sources, that require filtering and validation by experts to become