Sandfort Mirco
email: mirco.sandfort@outlook.com

Mirco Sandfort
email: msandfor@pasteur.fr

Amélie Vantaux

Saorin Kim

Thomas Obadia

Anaïs Pepey

Soazic Gardais

Nimol Khim

Dysoley Lek

Michael White

Leanne J Robinson

Benoit Witkowski

Ivo Mueller

M Niang

Forest malaria in Cambodia: the occupational and spatial clustering of Plasmodium vivax and Plasmodium falciparum infection risk in a cross-sectional survey in Mondulkiri province, Cambodia

Keywords: Malaria, Plasmodium vivax, Plasmodium falciparum, spatial epidemiology, spatial clustering, spatiotemporal clustering, spatial signature

Without the enormous support of my partner Isabelle this thesis would not have been possible. Isa, thank you for the load you took off my back. I apologize to you and Nea

Résumé

Après plusieurs décennies marquées par un déclin global du paludisme, les pays de la sous-région du Grand Mékong se sont engagés à l'éliminer d'ici 2030. Avec l'intensification des efforts d'élimination, les zones de forte incidence du paludisme se restreignent aux régions reculées. Certaines populations sont particulièrement exposées, notamment lors de déplacements en zones forestières. Le risque d'infection doit être bien décrit afin d'adapter localement les stratégies d'intervention. Les retards par rapport aux objectifs prévus d'élimination confirment la nécessité d'améliorer les programmes de contrôle.

Cette thèse a eu pour but de décrire et comprendre l'hétérogénéité du risque d'infection dans une région reculée du Cambodge. Nous avons stratifié le risque, en exploitant des covariables démographiques et géographiques, et avons appliqué des outils existants pour la détection de clusters. Nous avons introduit la notion de signature spatiale de la transmission pour l'épidémiologie du paludisme. A partir de cas index confirmés par PCR, nous avons décrit l'évolution de la prévalence en fonction de la distance aux cas, et avons étendu cette analyse aux études menées au Cambodge, au Brésil, en Thaïlande, aux Îles Salomon et au Sénégal.

Au Cambodge, nous avons démontré que des poches de prévalence élevées d'infections à Plasmodium spp. subsistent, majoritairement causées par des infections de P. vivax, asymptomatiques et difficiles à détecter en routine. La zone d'influence de ces poches ne dépasse pas quelques kilomètres en bordure des zones forestières. Le travail en forêt est le facteur de risque le plus important à l'échelle de la population, et le risque s'étend à l'ensemble des habitants des villages forestiers. Au Cambodge, au Brésil et en Thaïlande, les cas index et secondaires sont très proches, géographiquement et dans le temps.

Le Cambodge a intensifié son programme de lutte à l'échelle locale et introduit le traitement « radical » des infections récurrentes à P. vivax. Cependant, la détection passive des cas continuera à manquer la plupart des infections. Le risque homogène dans les villages forestiers pourrait justifier des campagnes de distribution massive d'antipaludéens. Le faible rayonnement géographique des clusters plaide en faveur d'approches réactives restreintes aux foyers avoisinants. En résumé, cette thèse a amélioré la compréhension du paludisme résiduel et propose de nouveaux outils qui aideront à son élimination.

SANDFORT Mirco -Thèse de doctorat -2022

Table of contents

In contrast to the other species, P. vivax and P. ovale sporozoites form dormant stages in hepatocytes called hypnozoites (Mueller et al., 2009). These re-activate and cause relapses, i.e. bloodstream infections weeks, months, or years after the initial infection by a mosquito [START_REF] White | Variation in relapse frequency and the transmission potential of Plasmodium vivax malaria[END_REF]. Contrary to P. falciparum, P. vivax merozoites prefer reticulocytes, potentially causing chronic anaemia. Also, P. vivax gametocytes can develop prior to symptom onset [START_REF] Adams | The Biology of Plasmodium vivax[END_REF]Mueller et al., 2009).

Artemisinin-based combination therapies (ACTs) are the most effective treatment against uncomplicated malaria. These drugs treat the bloodstream parasitaemia of all human malaria pathogens and can cure P. falciparum, P. malariae, and P. knowlesi. However, they are ineffective against hypnozoites. To prevent relapses from P. vivax or P. ovale and thus achieve 'radical cure', 8-aminoquinolines such as primaquine are needed. However, primaquine can induce haemolysis in patients with glucose-6-phosphate dehydrogenase (G6PD) deficiency, one of the most common enzyme deficiencies worldwide (World Health Organization, 2022).

Malaria epidemiology worldwide

Malaria occurs across many countries in Latin America, Africa, Asia, and Oceania, particularly in tropical and subtropical areas. It thus poses a risk of illness and death to billions of people and puts an additional economic strain on many resource-poor countries (World Health Organization, 2015a). The latest World Malaria Report 2021 from the World Health Organization (WHO) estimated 241 million malaria cases and 627,000 deaths from malaria worldwide in 2020. Ninety-five per cent of these cases and 96% of malaria deaths occurred in the WHO African region, and 77% of all malaria deaths happened in children under five years (World Health Organization, 2021a).

Malaria case incidence varied across endemic areas from 233 per 1,000 population at risk in the WHO African region to 2 cases per 1,000 in the Western Pacific region in 2020. Likewise, mortality rates ranged from 62 deaths per 100,000 population at risk to 0.4 per 100,000, respectively. However, low-incidence regions can enclose high-incidence countries, e.g. Papua New Guinea in the Western Pacific region, with 164 cases per 1,000 (World Health Organization, 2021a). Generally, the heterogeneity of malaria risk is 'fractal', i.e. low-incidence world regions include high-incidence countries, low-incidence countries contain highincidence provinces, and low-incidence provinces comprise high-incidence villages (Lana et al., 2021). Risk diversity extends further, e.g. to the levels of households and individuals within villages [START_REF] Smith | Revisiting the basic reproductive number for malaria and its implications for malaria control[END_REF][START_REF] Woolhouse | Heterogeneities in the transmission of infectious agents: implications for the design of control programs[END_REF].

Cases in sub-Saharan Africa are almost exclusively caused by P. falciparum. In contrast, P. vivax causes a considerable share of cases in the other malaria-endemic WHO regions, e.g. an estimated 30% of all cases were P. vivax mono-infections in the Western Pacific in 2020. In the Americas, even 75% of cases were attributed to P. vivax mono-infections (World Health Organization, 2021a). In particular, in countries that are co-endemic with P. falciparum and P. vivax, it has frequently been observed that as malaria case incidence decreased with intensified control interventions, the proportion of cases attributable to P. vivax increases (Price, Commons, Battle, Thriemer, & Mendis, 2020), see also Figure 2 on page 13.

The goal of malaria elimination

Since the United Nations included the halt of the spread of malaria in the Millennium Development Goals in 2000, the global incidence of malaria cases has been reduced by a quarter, i.e. from 81 cases per 1,000 population at risk in 2000 to 59 cases per 1,000 in 2020, see Figure 1. Malaria mortality has halved from an estimated 30 deaths per 100,000 population at risk in 2000 to 15 deaths per 100,000 in 2020 worldwide (World Health Organization, 2000). Figure 1 Global malaria case incidence (cases per 1,000 population at risk) from 2000 until 2020 as estimated by the WHO (World Health Organization, 2021a).

A massive scale-up of the major malaria control tools made this success in burden reduction possible. It mainly comprised insecticide-treated bed nets (ITNs), including longlasting insecticide-treated nets (LLINs), indoor residual spraying (IRS), rapid diagnostic tests (RDTs), and ACTs. Given this momentum, the Bill & Melinda Gates Foundation and the WHO called for malaria elimination in 2007 (World Health Organization, 2020). Until 2020, 23 countries had reached the WHO category 'prevention of re-introduction', i.e. had reported zero indigenous cases in a year (World Health Organization, 2007). After reporting no locally acquired cases for three years, a country is eligible for malaria-free certification by the WHO.

12 countries had achieved this status by 2020 (World Health Organization, 2021a), mainly from the Middle East or Central Asia.

The milestone of malaria elimination in 8-10 countries until 2015 was surpassed (The Roll Back Malaria Partnership, 2008), and the malaria-free certification of China and El Salvador in 2021 was a great success (World Health Organization, 2021a). However, the Global Technical Strategy for malaria 2016-2030 (GTS) aimed to eliminate malaria from 10, 20, and 35 countries in which malaria was endemic in 2015 until 2020, 2025, and 2030, respectively (World Health Organization, 2015a). Six countries have reached the malaria-free criterion since 2016, so the intermediate 2020 target was missed. Overall, progress in reducing the malaria burden has levelled off globally (World Health Organization, 2021a).

South East Asia and especially the Greater Mekong Subregion1 (GMS) has attracted particular attention since 2011 regarding malaria elimination due to the detection of resistance to artemisinin derivatives in P. falciparum at the Cambodia-Thailand border (Carrara et

Challenges to elimination and the need to accelerate

Several recent developments threaten global elimination efforts. They include the spread of drug-resistant parasites, particularly in P. falciparum, and strains that evade common RDT target antigens or more efficient and resilient vector species (Faulde, Rueda, & Khaireh, 2014; [START_REF] Gamboa | A large proportion of P. falciparum isolates in the Amazon region of Peru lack pfhrp2 and pfhrp3: implications for malaria rapid diagnostic tests[END_REF]World Health Organization, 2015b, 2021a).

In addition, many countries, particularly those from the GMS, face challenges due to inherent features of malaria epidemiology. First, a large share of asymptomatic infections which are not detected during routine, curative case management. Second, the predominance of P. vivax infections entailing transmission prior to symptoms, difficult cure, and relapses.

Third, the overall geographic fragmentation of malaria transmission with persistently high infection levels in remote areas. While the emerging threats render malaria elimination a race against time, the inherent challenges of malaria epidemiology hinder the progress of (inter-)national control programmes. Those roadblocks must be overcome if the ambitious elimination timeline is to be met. This thesis is embedded in the Asia-Pacific International Center of Excellence in Malaria Research (ICEMR) project. It applies state-of-the-art and novel molecular, immunological, entomological, and modelling techniques to better understand malaria transmission in Papua New Guinea and Cambodia [START_REF] Mueller | Asia-Pacific ICEMR: Understanding Malaria Transmission to Accelerate Malaria Elimination in the Asia Pacific Region[END_REF]. Regarding Cambodia, it especially examines the dynamics of forest-based transmission and P. vivax malaria and their implications for malaria elimination [START_REF] Robinson | Asia-Pacific ICEMR: Maximising impact on malaria control policy and public health in Cambodia and Papua New Guinea[END_REF]. This thesis assesses the high levels of asymptomatic and P. vivax infections and spatial and social heterogeneity in infection risk based on a cross-sectional survey in a remote area in Cambodia. The work introduces a novel approach to describe spatial and temporal clustering of infections and applies it to data from Cambodia, Thailand, Solomon Islands, Brazil, and Senegal. The thesis compares it to established spatial statistical tools and reviews the findings in light of their utility to help accelerate towards malaria elimination.

Chapter 2: State of the art

Chapter 2 recaps the current knowledge of the progress towards malaria elimination in South East Asia. It lists the presently recommended public health interventions. A focus lies on the challenge of fragmented malaria burden and statistical tools for spatial risk patterns.

Progress towards malaria elimination in South East Asia

Over the past two decades, countries in the GMS have made enormous progress in reducing the malaria burden. Annual numbers of indigenous cases of P. falciparum malaria have fallen by 93% from 2000 until 2020 and overall malaria case numbers by 78%, see Figure 2, despite a drastic increase in case numbers between 2006-2012. Coordinated efforts against artemisinin-resistant P. falciparum helped to change the course and initiate a decade of even more marked reductions in case numbers (World Health Organization, 2021a). Reductions of 60% or more in malaria incidence and mortality were achieved in all GMS countries. The 2020 GTS milestone of a 40% reduction was thus met (World Health Organization, 2015a, 2021a). In Cambodia, reported case numbers per year have declined drastically over the last 20 years, from 129,167 cases and 608 deaths in 2000 to 4,279 cases and zero deaths (for four sequential years) in 2021, see Figure 3. Laos and Vietnam also reported zero malaria deaths in 2020. Cambodia reported 10% of cases caused by P. falciparum in 2020 and thus missed the target of P. falciparum elimination by that year (World Health Organization, 2015b).

Considering unreported cases, the WHO estimates 69,136 malaria cases in Cambodia in 2020, which sets malaria elimination far from reach.

Toolbox for malaria elimination

The tools that the WHO particularly recommends for the prevention of infection include: 1) distribution of ITNs/LLINs free of charge, 2) IRS if vectors feed and people sleep mainly indoors, 3) preventive chemotherapies irrespective of diagnosis, either targeting risk groups or population-wide via mass drug administration (MDA), and 4) RTS,S/AS01 vaccination against P. falciparum malaria for children (World Health Organization, 2022).

Given the unspecific symptoms, treatment against malaria should follow confirmation via light microscopy (LM) or RDTs. Uncomplicated P. falciparum malaria should be treated with ACT daily over three days. In low-transmission settings, a combination with a single primaquine dose is recommended without needing G6PD testing to lower the risk of onward transmission by killing gametocytes. Uncomplicated malaria by any of the other Plasmodium spp. should be treated with ACT (or chloroquine unless the area is known for resistance). P. vivax and P. ovale should be treated with primaquine daily for 7-14 days to prevent relapses if they are not G6PD deficient (World Health Organization, 2022).

The geographic heterogeneity of malaria poses strategic and logistical challenges to national malaria programmes (NMPs). Thus, the WHO recommends regionally stratified In delimited areas with transmission across the general population, 'mass' interventions may be considered. When transmission is (largely) confined to certain high-risk groups, 'targeted' interventions could be applied to group members before, during, or following the exposure. 'Reactive' interventions can be initiated upon the (usually passive) detection of cases: cases are followed-up, and interventions are also targeted to co-exposed contacts or people residing with or close to the index case. In all three categories, interventions could be presumptive drug administration or testing and treatment.

The Cambodian NMP has mainly tackled malaria through the cost-free distribution of ITNs/LLINs and effective diagnosis by LM or RDTs and treatment with ACT in health centres and more remote health posts (Maude et al., 2014). Access to diagnosis and treatment for remote populations was further improved by the VMW programme, launched in 2004 [START_REF] Canavati | Village malaria worker performance key to the elimination of artemisininresistant malaria: a Western Cambodia health system assessment[END_REF]. These community health workers provide cost-free malaria diagnosis by RDTs, ACT, and behaviour change communication to the general population in remote villages. In addition, mobile malaria workers (MMWs) were established to target migrant populations, e.g. mobile groups that stay in the forest for extended periods for occupational activities. IRS and vaccination are not adopted in Cambodia. Until 2019, all RDTs or LMconfirmed infections had been treated with ACT. Primaquine treatment for P. vivax infections and prior G6PD testing was piloted in 2019-2020 and has been scaled up countrywide since then [START_REF] Sovannaroth | Accelerating malaria elimination in Cambodia: an intensified approach for targeting at-risk populations[END_REF]; World Health Organization, 2021a, 2021b).

Regional challenges to malaria elimination

Predominance of P. vivax

In the past, P. vivax was reported to cause a minority of cases in countries across the GMS (World Health Organization, 2021a). To date, the share of cases with P. vivax infections has increased in all countries, and in most, P. vivax now accounts for most of the cases: 42.4% of cases in Vietnam, 54.9% in Laos, 74.1% in Myanmar, 89.5% in Cambodia, and 93.5% in Thailand in 2020 (World Health Organization, 2021a).

Case management with ACT after RDT or LM diagnosis (in combination with prevention measures through ITNs and IRS) has proven very successful in reducing case incidence. However, this success has been more pronounced against P. falciparum than against P. vivax (Price et al., 2020; World Health Organization, 2021a). P. vivax infections lead mostly to lowdensity, asymptomatic infections that can be hard to detect by RDTs or even LM. Several relapses per primary P. vivax infection lead to partial immunity more rapidly and increase the likelihood of a milder or asymptomatic course upon relapse or new primary infection (Mueller et al., 2009). Thus, clinical case management that relies on patients' self-reporting to health facilities targets patients with acute P. falciparum infections more effectively than those with a P. vivax infection. Furthermore, primaquine treatment of P. vivax cases is often not established in endemic countries because of prevalent G6PD deficiency in the population, the risk of drug-induced haemolysis, and because G6PD testing prior to treatment is costly. In addition, the daily dosing over 14 days reduces adherence compared to the 3-day ACT-only regimens. A further challenge to P. vivax control is the early production of gametocytes prior to symptom onset [START_REF] Adams | The Biology of Plasmodium vivax[END_REF]Mueller et al., 2009), whereas P. falciparum gametocytes are more easily hit by gametocidal treatment as they appear only days to weeks after clinical manifestation (Lover, Baird, Gosling, & Price, 2018).

High prevalence of asymptomatic infections

The malaria incidence reported by NMPs is mainly based on clinical cases who consult health facilities. The measure thus misses patients who do not attend such facilities, be it due to mild symptoms or because they frequent traditional healers [START_REF] Chatterjee | Cambodia's fight against malaria[END_REF]. Patients from remote parts of a country, far from the nearest health facility, may also be missed.

VMWs, MMWs, or health posts in the periphery of Cambodia may lower but not abolish such barriers. Furthermore, the detection threshold of LM and RDTs does not allow the detection of sub-patent parasitaemia in mildly symptomatic patients. Lastly, asymptomatic individuals will not consult health facilities in the first place. Sub-patent or asymptomatic infections will be particularly prevalent in areas or populations with high transmission levels (due to immunity). Rates of infections will thus exceed incidence, even more so in remote areas with high transmission levels, including the north-eastern provinces in Cambodia [START_REF] Stresman | Association between the proportion of Plasmodium falciparum and Plasmodium vivax infections detected by passive surveillance and the magnitude of the asymptomatic reservoir in the community: a pooled analysis of paired health facility and community data[END_REF].

The disproportionately stronger divergence of reported incidence and actual infection rates in high-transmission areas make field studies in these areas instrumental, as they actively sample asymptomatic people. The first survey determining infection levels in Cambodia's general population was conducted by Incardona et al. in three high-incidence provinces in peak transmission periods in 2001-2003 (Incardona et al., 2007). Comparing LM-derived prevalence to reported case incidence, the discrepancy was between 5-fold in Sampovloun and higher than 100-fold in Koh Kong. That infection prevalence in surveys exceeded NMP records of clinical incidence was equivalently observed in other GMS countries, e.g. at the Thailand-Myanmar border in 2012 [START_REF] Baum | Submicroscopic and asymptomatic Plasmodium falciparum and Plasmodium vivax infections are common in western Thailand -molecular and serological evidence[END_REF] or in Myanmar (O'Flaherty et al., 2021).

Asymptomatic and sub-patent infections often harbour parasitaemia levels that allow the transmission to mosquitoes and thus contribute to the persistence of malaria despite malaria control interventions (Kiattibutr et al., 2017;Tadesse et al., 2018). It is thus crucial to identify and treat this silent reservoir of infections if malaria elimination is to be achieved.

Geographic heterogeneity in malaria burden

The countries in the GMS show a highly heterogeneous profile of malaria burden. At the lower end, China has had no locally acquired cases since 2017. In terms of incidence of indigenous malaria cases in 2020 in the other countries, the WHO estimates range from <0.1 per 1,000 population at risk in Vietnam and 0.2 per 1,000 in Thailand, and up to 5.8 per 1,000 in Cambodia. The malaria mortality estimates range from <0.1 per 100,000 population at risk in 2020 in Vietnam and Thailand, and up to 0.4 per 100,000 in Cambodia.

Within the countries, the malaria burden is also distributed highly unevenly, see Figure 5. In Thailand, large parts of the country are malaria-free. Areas with ongoing transmission are mainly restricted to the borders with regions in Myanmar, Malaysia, Laos, and Cambodia that have a relatively high incidence (World Health Organization, 2021e). Also, in Vietnam, lowlevel transmission mainly remains along the border with Cambodia and Laos. By contrast, in Laos, Myanmar, and Cambodia, parts of the country with high or very high transmission remain. While the mountainous northern half of Laos is mostly free of malaria, high transmission levels occur throughout the country's south and southeast along the border with Thailand with its plains along the Mekong. In Myanmar, provinces with no or low-level transmission can be found in the central lowlands, but the largest part of the country is covered with high transmission areas (World Health Organization, 2021c, 2021d, 2021f). In Cambodia, districts with no transmission can only be found in and around the capital Phnom Penh. Apart from these, transmission occurs throughout the country in an area inhabited by 71% of the population. High-incidence provinces were mainly restricted to the west, the area most afflicted by drug-resistant P. falciparum, mainly in Pursat province, and five north-eastern provinces, Preah Vihear, Stung Treng, Ratanakiri, Kratie, and Mondulkiri, see Figure 5 [START_REF] Dondorp | Artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Noedl | Evidence of artemisinin-resistant malaria in western Cambodia[END_REF]World Health Organization, 2021b). Until 2020, the incidence had been reduced in almost all provinces, and high-incidence provinces withdrew further into the countries' periphery. For example, in north-eastern Cambodia, only three provinces remained with elevated incidence and the highest incidence of 10-20 cases per 1,000 population was only found in Mondulkiri.

High-burden areas in the GMS are usually found in the foothills and along the coast but, above all, in forested regions. The major vectors in the GMS, An. dirus and An. minimus, prefer this shaded and humid environment. They tend to exophily and exophagy, i.e. to rest and bite outdoors, and to bite early, limiting the effects of IRS and ITNs. Agricultural activity close to the forest (often at the forest fringe on deforested land) with frequent or long overnight stays or wood logging in the forest ensures frequent and prolonged vector-host contact (Dysoley et al., 2008;[START_REF] Hii | Malaria vectors in the Greater Mekong Subregion: overview of malaria vectors and remaining challenges[END_REF][START_REF] Singhanetra-Renard | Malaria and mobility in Thailand[END_REF]World Health Organization, 2015b). The country's northeast is densely forested, subject to illegal wood logging, and populated by disproportionately high proportions of marginalized ethnic minorities (Asian Development Bank, 2002;Moul & Seng, 2012). The recurring concept of malaria risk in the GMS and Cambodia, in particular, is that it is confined to the high-risk groups of people exposing themselves to forested areas temporarily, e.g. slash and burn farmers and loggers, but also miners, security forces, and dam construction workers. (Dysoley et al., 2008;[START_REF] Hii | Malaria vectors in the Greater Mekong Subregion: overview of malaria vectors and remaining challenges[END_REF][START_REF] Singhanetra-Renard | Malaria and mobility in Thailand[END_REF]; World Health Organization, 2015b). Various methods are at hand for detecting spatial clustering of observations, e.g. malaria cases or infections. Abundant options are statistical tests for spatial autocorrelation, e.g. Moran's I or Geary's C, or space-time statistics. Based on geo-located households of reported malaria cases in Haiti in a low-transmission setting in 2014-2015, a study detected several areas of 1 km 2 in size with significant case clustering via a spatial autocorrelation statistic for cluster detection, called Getis-Ord Gi*, and overlapping space-time clusters via space-time permutation modelling [START_REF] Dismer | Detecting Malaria Hotspots in Haiti, a Low-Transmission Setting[END_REF]. Three cross-sectional surveys in northern Guatemala at low malaria endemicity in 2000-2001 found clusters of malaria cases by Moran's I and Getis-Ord Gi*, however highly variable in location over time [START_REF] Malvisi | Analysis of the spatial and temporal distribution of malaria in an area of Northern Guatemala with seasonal malaria transmission[END_REF]). In the 1990s, the Trinidad and Tobago Ministry of Health was able to confirm P. vivax and P. malariae outbreaks and calculate their spatial extents via space-time statistics to adjust national outbreak management guidelines (Chadee & Kitron, 1999). These days, the most popular method to locate malaria clusters is spatial scanning, particularly the SaTScan™ implementation (Martin Kulldorff, 1997; M. Kulldorff & Nagarwalla, 1995). This method divides the study area (and for spatiotemporal clustering, potentially the study period in case of multiple time points of observations) into overlapping windows, e.g. circular areas of the area under study and time intervals during the study period, and varies the size of these windows. Within the windows, the number of observed vs. expected positive observations are compared. The window with the maximum likelihood is considered the most likely cluster, often referred to as a 'hotspot'. The output of the calculation then is the location and size of that window, e.g. the circle on the map of the study area.

Statistical tools for low-scale spatial risk clustering

A cross-sectional survey in three villages in Cambodia's northeast, in Ratanakiri province in 2016, found clusters of P. vivax, P. falciparum, and P. malariae in sub-village areas (Durnez et al., 2018). In a cross-sectional survey along the border between French Guiana and Brazil at low prevalence of P. falciparum and P. vivax in 2017, hotspots could be located in remote neighbourhoods on village outskirts and small intra-village clusters [START_REF] Mosnier | Prevalence of Plasmodium spp. in the Amazonian Border Context (French Guiana-Brazil): Associated Factors and Spatial Distribution[END_REF]. During its malaria elimination phase, China used SaTScan analyses on retrospectively collected and geo-coded malaria cases to identify remaining transmission clusters in Shandong province. The study identified several distinct clusters for indigenous and imported cases, which was helpful in guiding elimination interventions [START_REF] Kong | Malaria control and prevention towards elimination: data from an eleven-year surveillance in Shandong Province, China[END_REF] Often when applied to observation time series, the SaTScan method finds hotspots, but these are not stable in time. For example, a cross-sectional survey with repeated data collection and sampling time points over two years in three villages in eastern Cambodia, Ratanakiri province, in 2016-2017 found hotspots in 2016. However, having lived in a hotspot in 2016 did not associate with a risk of Plasmodium spp. infection in 2017, whereas having lived in a household with a malaria case did associate with risk in the following year (Bannister-Tyrrell et al., 2019). Based on repeated cross-sectional surveys three months apart from 2013-2014 in three villages in western Cambodia, hotspots of P. vivax and P. falciparum infections were found, with clusters of subclinical infections close to clinical case clusters. However, hotspots were transient and not predictive of stable spatial clusters over time (Parker et al., 2017).

Based on the household locations of dengue fever patients in Bangkok over five years, Salje et al. developed a method to generalize the spatial structure of the underlying dengue transmission dynamics (Salje et al., 2012). The authors also applied it to data on HIV and measles cases (Lessler, Salje, Grabowski, & Cummings, 2016). It made use of the knowledge that the viruses of some case pairs were more closely related than others, e.g. viruses of one of the four serotypes of the dengue virus. The method then infers spatial clustering by dividing the probability of case pairs being of the same serotype in a given distance interval by the probability of any other case pair being of the same serotype, regardless of distance, across moving distance intervals. An increasing relative probability of more closely related case pairs at shorter distances indicated spatial clustering, e.g. see Figure 6. Whereas SaTScan-based analyses aim to identify the locations of the clusters of infections (which may change over time), the spatial signature provides a description of the degree of clustering aggregated across a geographic area.

Cambodia

Background

In the last two decades, Cambodia has seen a marked decrease in numbers of reported malaria cases from approximately 140,000 cases in 1999 to 60,000 in 2018 [1]. Likewise, mortality by malaria steadily reduced, with zero deaths reported for the first time in 2018. Similarly remarkable reductions have been observed in the other countries of the Greater Mekong Subregion [2]. Since 2014, these countries have even pursued the goal of elimination of all malaria by 2030 [3].

This ambitious aim however faces several obstacles. After an all-time low of approximately 20,000 reported cases in 2016, numbers have increased again in 2017-2018 [1,2]. While the annual case numbers increased for both Plasmodium falciparum and Plasmodium vivax in 2017, they declined for P. falciparum in 2018 but increased further for P. vivax [1,3]. Consequently, P. vivax now accounts for almost three-quarters of all malaria cases in Cambodia [2,4].

The national malaria control programme (NMCP) is based on mass distribution campaigns of long-lasting insecticidal nets (LLIN), diagnosis by light microscopy (LM) and rapid diagnostic tests (RDT) in the public health sector, RDT-based testing by village/mobile malaria workers (VMW/MMW), and treatment of blood stage infections with artemisinin-based combination therapy (ACT) [5]. The spread of multi-drug resistant P. falciparum malaria in Western Cambodia [6] might explain the resurgence of P. falciparum cases in 2017 and the change of first-line treatment from dihydroartemisinin-piperaquine to artesunate-mefloquine their subsequent decline in 2018. For P. vivax, such resistance is not reported. However, this species has dormant liverstages called hypnozoites that escape acute blood stage treatment and cause relapsing infections. Therefore, although LLINs do reduce P. vivax transmission and ACT is effective against blood stage P. vivax infection, neither intervention targets the hypnozoite reservoir. Hence, there is a less marked effect of these interventions on P. vivax than P. falciparum.

The only licensed drug with hypnozoiticidal effect is primaquine, typically administered at 0.25 mg/kg daily over 14 days in Cambodia. The roll-out of P. vivax radical cure is part of the national treatment guidelines but has not yet been implemented because of concerns over the drug's potential haemolytic effect in patients with G6PD deficiency [7,8]. In summary, the control and elimination efforts of the NMCP are successfully targeting clinical P. falciparum malaria (understandably in the context of drug-resistant P. falciparum [6] in the country) but to a lesser extent to P. vivax.

Another obstacle to elimination is the high prevalence of asymptomatic infections [9][10][11][12]. Neither health facilities nor VMWs reach these asymptomatic carriers as they do not seek diagnosis or treatment. Although of lower infectivity [13,14], they remain however a potential source of onward transmission.

Alongside the sustained reduction in countrywide incidence, malaria has also become increasingly fragmented, affecting fewer, usually remote provinces. The highest incidence of malaria in Cambodia and across the Greater Mekong Subregion is now particularly found in the North-Eastern provinces such as Ratanakiri and Mondulkiri [1,3]. These settings are characterized by a high proportion of ethnic minorities and agricultural or wood-logging activities as the main income sources. Exposure to Anopheline mosquitoes and Plasmodium in Cambodia is understood to primarily occur in the forest rather than peri-domestically [15][16][17].

Together with the Western province Pursat, the highest incidence per province in 2018 was reported for Mondulkiri [1]. However, previous studies on population-level burden and risk of malaria in Cambodia focused on the country's West and North [12,[16][17][18][19][20][21] or the Eastern province of Ratanakiri [10,11,15,20,22,23]. Here, using a cross-sectional survey conducted in a rural area of Mondulkiri in the dry season in 2018, estimates of the prevalence of Plasmodium infection are shown and key risk factors identified.

Methods

Study area and census

Seventeen villages were selected in the Kaev Seima district, Mondulkiri province, in the Kingdom of Cambodia. The rainy season in Cambodia normally runs from June to October with the high transmission period from June through December [1,4,24]. Approx. two-thirds of the population in Mondulkiri is comprised of national ethnic minorities, with the Phnong ethnic group comprising the largest proportion [25,26]. In November-December 2017, a census was conducted by visiting each household in the 17 villages and collecting basic demographics of household participants such as age and gender. A person's household was defined as location of main residence in exposure was indicated in addition to risk due to forest activities. Village-level stratification of targeted interventions based on forest proximity could render the elimination efforts more cost-effective and successful. Keywords: Forest, Occupational risk, Spatial, Vivax, Hotspots, Cambodia, Greater Mekong Subregion the village according to adult members of the household or the village head. GPS coordinates were collected using Garmin ® GPSMAP ® 64s devices. When no adult household member could be found, demographic information was obtained from the village head's registry book.

Cross-sectional survey

Based on the census, a random selection of households was drawn oversampling small villages to ensure sufficient coverage (Additional file 1: Table ST1). Selected households were visited from mid-December 2017 until mid-April 2018 and all household members aged 2-80 years who had resided in the study area for at least 3 months were invited to participate in the survey. Upon informed consent, a questionnaire on household variables was administered to the head of household or another adult household member by trained interviewers. A questionnaire on individual-level variables was administered to each consenting household member. Children were interviewed assisted by a parent (or rarely another caregiver in the absence of the parents). Data were collected on tablets and run through automated data quality checks within days after the interview. In case of missing data or discrepancies, the field team was informed for immediate resolution if possible. Fingerprick blood samples were collected as thick and thin film slides and in K + EDTA-microtainers. Participants were screened for symptoms, i.e. feeling sick or feverish on the day of interview, having felt feverish over the preceding two days, or having an axillary body temperature of at least 37.5 °C. Upon any indication, the participant was administered a standard malaria RDT (Malaria Ag P.f/P.v, Standard Diagnostics Inc., South Korea) and referred to a local health care provider for treatment if positive.

Detection of infection

The microtainer blood samples collected at the interview site were stored in 4 °C ice boxes. At a field laboratory, they were separated into plasma and cell pellet and frozen at -20 °C. Following transport to the main laboratory at Institut Pasteur of Cambodia in Phnom Penh, cell pellets were stored at -20 °C and plasma at -80 °C. Infections with any of the four human Plasmodium parasites were determined by real-time PCR [27]. In case of a positive genus-specific result, qPCR specific for P. falciparum, P. vivax, Plasmodium malariae, and Plasmodium ovale followed. All positive and a random selection of 10% negative samples were assessed by independent double LM readings of asexual and sexual stages and parasite densities calculated. No discrepancy in parasite densities of above 30% occurred. Parasites were counted per approximately 500 leukocytes and densities were inferred assuming 8000 leukocytes per microlitre blood.

Descriptive analyses

Villages were classified based on the forest cover in a 750 m radius around the households, computed from the land cover analysis in [28] (Additional file 1: Figure S1). Villages with ≥ 50% of households with ≥ 10% forest cover in their vicinity were considered "inside the forest", with ≥ 30% of households with ≥ 5% forest cover as "at the forest fringe", or "outside the forest" otherwise. Because of very low sample sizes, the two small, neighbouring villages Beng (11 individuals) and Gaty (95 individuals) were analysed as one. Population prevalence was estimated based on post-sampling weights assigned to each participant according to their representation by village, gender, and 10-year age bins compared to the census population (raw numbers of positive survey samples accompany the estimates in brackets). Categorical covariates were compared using the chi-squared test or the Fisher's exact test when low strata sizes required it.

Risk factor analyses

The association of covariates with infection by P. vivax, P. falciparum, or all four species was assessed by mixedeffects logistic regression with random intercepts per household and village. Those covariates that were statistically significantly associated with Plasmodium infection at two-tailed α = 5% in univariate regressions were included in the multivariate model. The villages' proximity to the forest, work-unrelated overnight travels (incl. to forest sites), and work in the deep forest were considered part of the multivariate model a priori in order to assess the association of forest exposure with risk of infection. Having slept outdoors or under a bed net last night were kept as part of the model based on causal reasoning or prior knowledge due to their association with infection risk in previous publications, e.g. [11]. Gender and age were retained in the model as proxies for riskrelated behaviour and thus potential confounders. The other covariates comprised potential proxies for socioeconomic status or further exposure variables and their subset significantly associated with infection risk was assessed by backwards variable selection. Akaike Information Criterion (AIC) was used to assess model fit. If collinearities and interactions were identified, covariates were retained comparing fit of the respective models by AIC. Statistical significance in the multivariate regressions was calculated per covariate by likelihood ratio tests. Spatial hotspots were identified by a purely spatial scan statistic via a discrete Poisson model with maximally a third of the population in a scanning window, adjusted for all covariates of the final multivariate model except for the villages' forest proximity [29].

Software

All questionnaires were applied on tablets using the REDCap (Research Electronic Data Capture) software hosted at Institut Pasteur in Paris [30,31]. Data quality control as well as all descriptive and analytical statistics were performed in R 3.6.3 [32]. The SaTScan ™ software version 9.6 was used for the spatial scan statistic [33].

Results

Census, survey representativeness and survey population

From among the 10,053 individuals in 2351 households identified in the census, the survey recruited 4200 participants from 1147 households and oversampled smaller villages on average (Additional file 1: Table ST1). A map of the survey households and the categorization of villages by proximity to the forest is shown in Fig. 1a. Mean age was 26 years in both the census and the survey and with 51% (5135/10,053) and 53% (2231/4200) women, respectively, women were slightly oversampled in the survey. In men, ages of 16 to 40 years were mildly underrepresented (Fig. 1b). The main income source for the survey households was by large majority farming (89.8%, 1030/1147). In terms of mobility, three-quarters (75.9%, 3188/4200) of the survey participants reported work-unrelated trips to forest or field sites in the last month (8.5%, 358/4200) or any work trip in the last two months (75.8%, 3184/4200). By vast majority, these trips were short and frequent: In only 4.4% (140/3188) of the instances, stays for longer than a week were reported and among those who reported any work trip, 89.4% (2847/3184) went for work at least once a week.

Plasmodium vivax predominated over other human malaria parasites

Infection by Plasmodium parasite was detected by PCR in 8.3% (349/4200) of participants. The proportion of samples positive for P. vivax was 6.4% (268/4200) compared to 3.0% (125/4200) for P. falciparum (p < 0.001, Table 1). Plasmodium vivax was found in 77% (268/349) of all infections compared to 36% (125/349) for P. falciparum. Four samples were positive for P. malariae monoinfections and none for P. ovale. Speciation by PCR was unsuccessful in 4 samples which were analysed as negative. Extrapolation to the entire Kaev Seima population yielded an estimated prevalence of 8.9% for Plasmodium infection, 6.8% for P. vivax, and 3.3% for P. falciparum. Estimated prevalence was highly heterogeneous across the villages, ranging from 0.6% (3/594) to 36.3% (54/152) and from 0% (0/594) to 25.1% (27/106) for P. vivax and P. falciparum, respectively (both p < 0.001, Table 1). Village-level prevalence of P. vivax infection was on average higher in villages inside the forest compared with those at the forest fringe and outside the forest (medians 23.2, 7.2, 5.6% respectively, Fig. 2; similarly for P. falciparum in Additional file 1: Fig. S2).

Plasmodium vivax infections were least detectable by the health care system

Of all PCR-detected infections, 83.2% (223/268) were sub-microscopic for P. vivax compared to 74.4% (93/125) for P. falciparum (p≈0.056, Table 2). Plasmodium vivax infections coincided less often with reported or measured symptoms at the interview than those by P. falciparum, with 6.7% (18/268) and 16.8% (21/125), respectively (p < 0.01). Only 2.2% (6/268) of all P. vivax infections were symptomatic and also positive by RDT or LM (i.e. detectable through the Cambodian health care system), less often than for P. falciparum (7.2%, 9/125, p < 0.05). Asexual parasite stages were detected in all LM + samples for P. vivax (37/37) and in most (81.5%, 22/27) for P. falciparum (Table 3). In LM + samples, the geometric mean parasite densities were 149.7 parasites/μL and 431.3 parasites/μL (t-test: p≈0.07), respectively. While gametocytes were found in only one (2.7%, 1/37) P. vivax LM + sample, more than a third (37.0%, 10/27) of LM + samples for P. falciparum were gametocyte-positive with a geometric mean of 51.1 gametocytes/μL.

Prevalence was highest in men of working age

Estimated prevalence was more than twice as high in men as in women, i.e. 10.4% (188/1969) vs. 3.6% (80/2231) for P. vivax (p < 0.001), 4.8% (83/1969) vs. 1.9% (42/2231) for P. falciparum (p < 0.001), and 13.3% (239/1969) vs. 5.0% (110/2231) regardless of species (p < 0.001). The patterns across age were similarly heterogeneous for both species (Additional file 1: Figure S3). While there was no difference in genus-wide prevalence across age in women, men showed an elevated risk at working age (p < 0.001), regardless of the proximity of the village to the forest (Fig. 3). A fitted interaction term of gender and age was significant in the strata of villages outside the forest (p < 0.001), but not across those villages at the forest fringe or inside the forest.

Risk profiles for P. vivax and P. falciparum infections were similar

Risk of infection was associated with individual covariates for both P. vivax and P. falciparum in a highly similar

Table 1 Prevalence of PCR-detected infections

As proportions of positive blood samples or as census population-level estimates by extrapolation via post-sampling weights (raw numbers of positive samples in brackets). 4 fashion (Table 4 for behavioural variables, full list of variables with odds ratios in Additional file 1: Table ST2). For both species, prevalence was associated with workunrelated overnight travels and highest if those occurred to forest sites (22.4%, 60/268, and 14.9%, 40/268, for P. vivax and P. falciparum, respectively) and lowest for urban destinations (1.7%, 1/59, and 0%, 0/59, for both p < 0.001). Infections of both species were also more prevalent among those who reported work trips to sites in the nearby and deep forest (assessed separately). In particular, a significantly higher prevalence was observed in participants who reported work trips into the deep forest (25.5%, 37/145, and 18.6%, 27/145) compared to those who did not (5.7%, 231/4055, and 2.4%, 98/4055, for P. vivax and P. falciparum, respectively, p < 0.001).

Once clustering at household and village-level was taken into account in univariate mixed-effects logistic regression, work in the nearby forest was no longer significantly associated with risk of infection though (Additional file 1: Table ST2). There were fewer infections among those who reported the use of standard protection measures such as sleeping under a bed net (6.1%, 236/3880, and 2.7%, 104/3880, vs. 10.0%, 32/320, and 6.6%, 21/320, if no net was used, p < 0.01 and p < 0.001 for P. vivax and P. falciparum, respectively). A higher risk in men, at working age, in villages at the forest fringe or inside the forest, and with indicators of lower socio-economic status was also found similarly for both species (Additional file 1: Table ST2).

Working in and travelling to the forest were strong risk factors of infection

Behavioural covariates related to activities in the forest were statistically significant in the multivariate model of Plasmodium infection (Table 5). Having travelled to forest sites (nearby or deep forest) or having worked in the deep forest independently increased the odds of infection two to three-fold (adjusted odds ratio, aOR, 2.17, p < 0.01 and aOR 2.88, p < 0.001, respectively). Risk of infection was not significantly attributed to the other behavioural covariates that were included in the model a priori, namely having slept outdoors (aOR 1.99, p≈0.08) and under a bed net (aOR 0.99, p≈0.96). Risk of infection was increased in males (aOR 3.06, p < 0.001) and working age (aOR 7.84 in 21-25 years old compared to children, p < 0.001). A fitted interaction term of gender and age improved the model (AIC 1786 vs. 1802 without interaction, p < 0.001, Additional file 1: Table ST3). Other covariates linked higher socio-economic status with lower odds of infection, such as living in a house with a roof built of relatively high-quality material (aOR 0.50, p < 0.01). The covariate on recent information on malaria via TV (aOR 0.37, p < 0.01) most likely also acts as a proxy for higher socio-economic status, i.e. being able to afford a TV in the first place.

Descriptive characterization of the risk factors forest work and forest travels

Work-unrelated overnight travels to forest sites was a predominantly male domain (reported by 10.7%, 211/1969, of the men vs.

Residing in a village inside the forest was an independent spatial risk factor

Living in a village inside the forest remained associated with risk of infection when adjusting for the significant demographic, socio-economic, and behavioural covariates (aOR 12.47, p < 0.001, Table 5). Households from all four villages inside the forest also formed hotspots of Plasmodium infection, i.e. purely spatial clusters of elevated risk of infection that cannot be explained by the other covariates (Fig. 4, Additional file 1: Tables ST4-ST5).

Discussion

This is the first detailed study on the prevalence of Plasmodium infection and associated risk factors on population-level in Mondulkiri province in Cambodia. The overall prevalence of Plasmodium infection is consistent with that observed in the neighbouring province of Ratanakiri [10,11,22] or other endemic provinces in the West of the country [16].

Plasmodium falciparum has long been the predominant species countrywide [9,[START_REF] Gomez-Barroso | Spatial clustering and risk factors of malaria infections in Bata district, Equatorial Guinea[END_REF]. However, following a scale-up in the VMW programmes, a steady decrease in reported P. falciparum cases was observed in 2009-2011, while numbers of reported P. vivax cases increased [24]. In recent years, cases of both species were reported at an almost equal share [4] and in 2018 P. vivax accounted for approximately three quarters of reported cases [2]. That P. vivax started to predominate over P. falciparum in Cambodia even earlier is suggested by other studies in high-incidence provinces where molecular and serological diagnosis identified a higher prevalence of P. vivax than P. falciparum infections [11,12,[15][16][17][18][19], consistent with the presented study in Mondulkiri.

7.2% of the P. falciparum infections could be identified by symptoms and a positive result by LM or RDTs, i.e. the diagnostics used by the health system, compared to only 2.2% for P. vivax. Low sensitivity of RDTs for P. vivax is a commonly reported problem [12,[35][36][37][38]. In addition, higher proportions of asymptomatic infections for P. vivax are regularly observed as the relapses can lead to higher levels of immunity and thus overall lower parasite densities [39,40]. Data from studies using membrane feeding assays suggest that the densities of asexual stages and gametocytes in the asymptomatic, LM-positive samples in this survey are infectious to mosquitoes [14,[41][42][43][44][45]. It can thus not be ruled out that these subclinical infections contribute to the ongoing transmission in this area.

More asymptomatic infections, lower diagnostic sensitivity for P. vivax, high transmissibility, and the lack of radical cure by primaquine all might add up as possible explanations why the control efforts have been less successful against P. vivax than against P. falciparum. In order to address this high burden of P. vivax, Cambodia is now piloting the use of primaquine against P. vivax infections in four provinces. While an essential step, the effect of radical cure will be limited without point-of-care diagnostics that are more sensitive for P. vivax infections and ideally a test allowing the detection of hypnozoite carriage (irrespective of blood stage parasitaemia). New advances in the use of multiple antigens to detect antibodies as serological markers for recent exposure and thus for potential hypnozoite carriers are promising in this respect [46].

With as many as 40% of the inhabitants infected in the highest-prevalence village in this survey, it may be necessary to complement the current malaria control efforts by active or reactive case detection schemes [47,48] to reduce the infected (and potentially infectious) reservoir and eventually reach the goal of malaria elimination. Targeting such resource-intensive interventions to high-risk groups will render them significantly more cost-efficient. The similarity of the risk profiles for both P. falciparum and P. vivax presented here is encouraging as it suggests that such targeted interventions would appropriately address both of the two species. This study emphasizes the predominant role of forest malaria transmission in explaining elevated risk of infection in remote rural areas of Southeast Asia. Previous cross-sectional surveys have also identified an association of infection risk with either work in the forest [15,17,49] or time spent therein [16,50]. However, this study is unique in retaining them both as statistically significant risk factors in one multivariate model while also adjusting for gender, age, socio-economic proxies, and other behavioural covariates. While work in and travels into the forest are most frequent in adolescent and adult men who are usually considered the forest-related risk group, this behaviour also occurred in women and from children to participants in their sixties and thus actually extends to a much broader range of the population.

On the relatively small spatial scale of 17 villages in an area of 22 × 26 km 2 , the prevalence ranged from less than 1% to above 40% in the dry season. The trend towards higher prevalence in villages at the forest fringe and inside the forest compared to those outside the forest is in line with the widely accepted notion for forest-related mosquito abundance and exposure in Cambodia. Other studies have also identified a higher prevalence in villages at close proximity to the forest [21,49,50]. It is apparent that the association of risk with living near or inside the forest was retained in this study even if adjusting for demographic, socio-economic, occupational, and other behavioural differences.

Not more than 60% of infections could be attributed to the strong behavioural, forest-related risk factors. In villages outside the forest or at the forest fringe, most infections indeed occurred in working age men. This predominance of this occupational risk group was much lower in forest fringe villages and entirely lost in villages situated inside the forest. Taking gender and age as proxies for risk-related behaviour and occupational activities, this could indicate that in villages outside the forest, risk can be explained almost completely by such occupational exposure. By contrast, for villages within the forest, the more homogeneous presence of infection across all age groups in both males and females indicates additional exposure inside the village. This is in line with other studies in which household-level risk factors in forested areas were important to explain infection risk [10] or infections clustered in households over 2 years [START_REF] Bannister-Tyrrell | Households or hotspots? Defining intervention targets for malaria elimination in Ratanakiri province, Eastern Cambodia[END_REF] in Ratanakiri, the other Eastern province of Cambodia.

The current WHO report on malaria eradication calls for subnational stratification of intervention programmes [START_REF]Malaria eradication: benefits, future scenarios & feasibility[END_REF][START_REF] Stresman | Malaria hotspots: is there epidemiological evidence for fine-scale spatial targeting of interventions?[END_REF]. This study suggests that such a stratification of interventions could happen along a gradient of villages inside or outside the forest. Population-oriented interventions such as targeted mass drug administration or test and treat programmes with sensitive molecular or serological diagnostics [46,[START_REF] Lek | Tools to accelerate falciparum malaria elimination in Cambodia: a meeting report[END_REF] (together with their costs and risk of overtreatment) could be justified in forest villages. By contrast, approaches targeted at risk groups only may be effective at lower costs for the NMCP and the local population in residential settings outside of the forest.

Conclusion

Despite a substantial reduction in malaria burden in Cambodia over the last decades, this study demonstrates that pockets of high malaria prevalence persist in the country. Given that the vast majority of infections were asymptomatic, the study strengthens the argument to enhance malaria elimination efforts by measures of (re-)active detection of also asymptomatic infections. Plasmodium vivax infections were detected at a higher prevalence than P. falciparum infections, more often asymptomatic, and less detectable by RDT and LM. Consequently, novel tools for the identification of hypnozoite carriers could play a key role in the presented setting as well as the roll-out of routine radical cure treatment. The study corroborates the notion of forest malaria in the Greater Mekong Subregion by demonstrating the independent association of travels to the forest, of forest work, and of living in close proximity to forest with malaria infection risk. However, the study also demonstrates that infection risk is less confined to forest-goers in sub-populations that already live in forested areas and suggests within-village transmission therein. A focus of interventions solely on forest-goers could thus be insufficient to reach the goal of nation-wide malaria elimination in due time. In the light of the presented results, interventions could be targeted at whole villages inside the forest, and targeted at stratified risk groups for villages outside the forest.

Background

Globally, enormous successes have been achieved over the last decades in reducing the malaria burden (1). The main instruments were the distribution of long-lasting insecticide-treated nets and clinical case management after passive case detection. The strategies deployed so far have proven particularly successful in battling P. falciparum transmission. However, P. vivax causes relapses (2) and to a higher degree asymptomatic low-parasitaemia infections that escape passive case detection but contribute to transmission (3,4). The effectiveness against P. vivax malaria has thus been less pronounced so that P. vivax has predominance over P. falciparum in many areas (1).

In Latin America, South East Asia, and the Western Pacific, the overall reduction of malaria burden in many countries has paved the way for the ambitious goal of malaria elimination by 2030 (1,5). The necessity to incorporate active finding of infections in order to tackle the silent reservoir of on-going transmission is increasingly acknowledged (3,4,(6)(7)(8)(9). One strategy considered as part of current recommendations for malaria elimination are reactive interventions, i.e. active finding, testing, and treatment of other infections in a delimited area around passively detected malaria cases (10).

However, the radius of the area in which to optimally implement interventions remains unclear.

Infection with human Plasmodium spp. through an Anopheles spp. mosquito bite depends on a complex interplay of factors of the pathogen, host, vector, and environment. Each of these factors, such as proximity to the vector's breeding grounds, bed net usage, Anopheles species composition, and host immunity, can vary geographically (11,12). Geographical heterogeneity in malaria infections appears across multiple spatial scales varying from between households (13)(14)(15)(16) to within and between villages (9,11). At low incidence, the possibility to identify single malaria transmission clusters has long been reported (17).

Reducing malaria transmission country-wide usually leads to a geographical fragmentation of areas where malaria risk remains high, further increasing spatial heterogeneity (18). Tailoring interventions to high-risk areas within these countries is at the core of recommended strategies for malaria elimination (10,19). Obtaining a good understanding of the spatial dependence between malaria infections will help underpin spatially targeted control efforts.

To date, most efforts to characterise the spatial clustering of malaria have relied on comparing infection prevalence at community or household scales (11,13). In addition, some studies have used space-time clustering statistics such as the k-nearest neighbour method for binary testing whether spatiotemporal clustering of cases occurred (17). The introduction of spatial scanning (20,21) to malaria epidemiology further stimulated studies on spatial heterogeneity in disease or infection risk. This method scans the study area for (size-variable) circular areas with statistically significant clustering of infections ('hotspots'). Such hotspots were identified in diverse study areas in South America (22)(23)(24)(25)(26), Africa (27)(28)(29)(30)(31)(32)(33)[START_REF] Gomez-Barroso | Spatial clustering and risk factors of malaria infections in Bata district, Equatorial Guinea[END_REF](35)(36)(37)(38)(39), and South East Asia (9,(40)(41)(42)(43)(44), for P. falciparum (25,32,40,41), P. vivax (25,26,32,40,41), and P. malariae (40,41). However, the method's effectiveness in forecasting hotspots remains unclear and it does not generalize spatial patterns between infections.

In this study, we introduce a global clustering statistic to malaria epidemiology, borrowing strongly from earlier development in the epidemiology of dengue and other pathogens (45,46). The spatial signature quantifies both the magnitude of clustering of infections and the radius within which it is statistically significant, potentially informing reactive interventions as part of malaria elimination endeavours. We reveal spatial signatures of PCR-confirmed P. vivax and P. falciparum infections by assessing the pooled prevalence of infections around confirmed index infections in widening spatial and temporal windows. We demonstrate spatial and temporal clustering of infections in both cross-sectional surveys and cohort studies from a diverse spectrum of study sites in Brazil (6,47), Thailand (7,8), Cambodia (9), and Solomon Islands (48).

Methods

Cross-sectional malaria prevalence surveys

We used data from 5 cross-sectional surveys from 4 countries, i.e. Brazil, Thailand, Cambodia, and Solomon Islands. The study design and basic descriptions are summarized in Table 1.

We included a cross-sectional survey in 17 villages in the high-incidence, rural province Mondulkiri, in North-Eastern Cambodia, in the dry season in December 2017 until April 2018 (as described in ( 9)).

The 4,200 study participants were 2-79 years old and from randomly sampled households based on a census prior to the survey. We classified villages of residence into categories of proximity to the forest based on a forest cover remote sensing analysis (49). In this and all following included studies, we tested finger-prick blood samples for P. vivax and P. falciparum infections by real-time PCR and collected GPS locations of households. * Range of monthly prevalence. † Some individuals had reported ages greater than 100 years. In these cases, record keeping was considered not sufficiently robust to provide an accurate estimate of age.

From Kanchanaburi and Ratchaburi provinces in western Thailand, we consider data from a crosssectional survey in September-October 2012 (7). We enrolled 4,309 study participants aged 0-92 years.

We added data from two cross-sectional surveys in a peri-urban part of Manaus, Amazonas State, Brazil from November 2012 until January 2013 (rainy season) and from August until September 2013 (dry season), respectively (6). After all inhabitants had been invited to the study, 2,010 and 2,073 participated in the two surveys, respectively (963 in both surveys).

From Solomon Islands, we used data from a cross-sectional survey in several villages across Ngella, Central Islands province from May until June 2012 (48). From a representative household-based sample, 3,501 individuals agreed to participate.

Longitudinal cohort studies

In addition to the cross-sectional surveys, we used data from two longitudinal cohort studies with similar design: Monthly active follow-up with finger-prick blood-sampling for P. vivax and P.

falciparum real-time PCR testing and collection of household locations by GPS at baseline. One study was conducted in two villages in Kanchanaburi and Ratchaburi provinces, Thailand, respectively, in May 2013 until June 2014, enrolling 999 participants (Table 1) (8). The other study took place in the peri-urban part of Manaus, Brazil, from April 2013 until March 2014 with 1,274 enrolled participants (47).

Spatial signature of malaria infection

In order to characterise the spatial dependence between infections in both the cross-sectional surveys and for each follow-up period in the longitudinal studies, we calculated the average prevalence 𝜋(𝑑) of PCR-positive infections within a distance 𝑑 of PCR-positive "index" infections. To do this, we identified the study participants that lived within distance 𝑑 of each PCR-positive participant, and calculated the proportion that were also PCR-positive. We varied 𝑑 in incremental steps by 1 km for 𝜋 across the entire study region; and by 50 m for 𝜋 within 1 km.

We compared the observed prevalence with that expected under a null distribution where no spatial dependence exists. We generated a bootstrap null distribution of 𝜋 after 10,000 random reallocations of infection locations and define statistical significance outside of the null's 95 th percentile interval.

For the cohort studies, we calculated the spatial signature for P. vivax infections as above, pooling across the monthly study visits, i.e. considering only index-neighbour pairs in the same study month.

If multiple monthly PCR results were positive for an individual, we consider only the first, "incident" positive result.

We provide the underlying distribution of pairwise distances between the study participants per study site as well as the underlying distribution of pairwise distances between infections and all study participants in the Supplementary Figures (S.1.1a-S1.7).

Spatiotemporal signature of malaria infection

In the two longitudinal cohort studies, we also explored the extent of spatiotemporal dependence.

We calculated the period prevalence 𝜌(𝑑, 𝑡) of PCR-positive neighbouring infections within a distance 𝑑 and within a time window 𝑡 of all incident PCR-positive index infections for P. vivax as the ratio of the number of pairs of index and neighbouring incident infections within 𝑑 and 𝑡 and the number of pairs of study participants within 𝑑 (steps by 1 km for 𝜌 across the entire study region; by 50 m for 𝜌 within 1 km). A study participant was considered an incident infection at the first study visit with a PCR-positive blood sample. We generated a bootstrap null distribution of 𝜌 after 1,000 random re-allocations of locations of infection time series.

From the cohort study in Thailand, PCR-positive blood samples for P. vivax were genotyped as described in (8). We calculated spatiotemporal signatures as described above but respecting only P.

vivax infection pairs with a matching genotype.

Comparative analysis of distance to 50% reduction in prevalence

From each spatial signature across the cross-sectional surveys and cohort studies, we drew the distance within which the prevalence around index infection has fallen by 50% (between the prevalence at 0 m and the study's global prevalence). The distance was approximated as the mean of the pair of 50 m-increments of distance 𝑑 within which the spatial signature falls below the 50% of prevalence.

Software

We described, analysed, and visualised the data in R 4.1.2 (50). We created the maps with the ggmaps package in R and background Landsat-8 image courtesy of the U.S. Geological Survey.

Results

Spatial signatures from cross-sectional surveys

In the Cambodian cross-sectional survey, P. vivax and P. falciparum infections were found in all villages across the study region (Figure 1A). The prevalence of P. vivax around index infections decreased threefold from 21.3% at 0 km to the global study prevalence of 6.4% at maximum distance (Figure 1B). The estimate at 0 km may include household members and inhabitants of the same building. For P. falciparum, prevalence decreased from 12.9% to 3.0%. Within 1 km, it decreased to 13.3% for P. vivax infections and to 7.9% for P. falciparum infections. Amongst the villages inside the forest, P. vivax prevalence decreased from 38.4% at 0 km to 28.2% within 1 km (Figure 1C). For P.

falciparum, prevalence fell from 23.9% to 17.3%. For both species, prevalence remains spatially clustered beyond the null distribution within 1 km amongst the villages inside and outside the forest but to a lesser degree, if at all, amongst the villages at the forest fringe. In the cross-sectional survey across three sites in Western Thailand (Figure 2A), prevalence of P. vivax infections around index infections decayed from 6.6% at 0 km to 3.6% at 1 km and for P. falciparum from 3.1% to 1.0% (Figure 2B). Significant spatial clustering for P. vivax infections was found only within a radius of 75 m. In the peri-urban part of Manaus, P. vivax and P. falciparum infections were detected in the first of the two cross-sectional surveys (Figure 2C) and P. vivax infections in the second survey (maps in the Supplementary Figure S3). Only one P. falciparum infection was found in the second survey, precluding any signature analysis. P. vivax prevalence dropped from 11.4% at 0 km around index infections to 5.3% within 1 km in the first survey and from 9.6% to 3.7% in the second survey (Figure 2D). Prevalence around P. vivax index infections remained higher than the null distribution within 1 km in the first survey and up to 70 m in the second survey. Prevalence of P. falciparum infections in the first survey did not follow a decaying pattern.

In the cross-sectional survey in Solomon Islands, P. vivax prevalence was 23.7% at 0 km around index infections and hovered between 19.3% and 21.8% until a distance of 1 km (a decaying pattern is more apparent from the signature across the entire study region in the Supplementary Figure S5).

For P. falciparum, prevalence decreased from 11.1% at 0 km around index infections until 1.8% within 1 km. Prevalence was elevated from the null distribution for both species across the distance of 1 km.

Spatial and spatiotemporal signatures from cohort studies

Pooling across the study visits of the longitudinal cohort studies, P. vivax prevalence around index infections per study visit decreased from 9.5% at 0 km to 4.1% at 1 km in the study in Thailand (Figure 3B) and from 13.1% to 6.4% in the Brazilian study (Figure 3E). The signature remains above the null distribution until 250 m in the Thai study and entirely within 1 km in the study in Brazil. In the cohort study in Thailand, P. vivax period prevalence decreased from 4.5% at 0 km around incident infections to 2.3% within 1 km and the same study month, from 4.3% to 2.3% within 2 months, and from 3.1% to 1.9% across the entire study period of 14 months (Figure 3C). Period prevalence was above the null distribution until 150 m within the same study month and within 2 months, and 25 m for infections across the study period.

In the Brazilian cohort study, P. vivax period prevalence fell from 7.3% at 0 km to 3.5% within 1 km and the same study month, from 5.7% to 3.1% within 2 months, and from 4.7% to 2.9% across the entire study period of 13 months (Figure 3F). Period prevalence was above the null distribution until 150m within the same study month, until 250 m for infections within 2 months, and 100 m for infections across the study period.

Considering only infection pairs with a matching genotype in the cohort study from Thailand, P. vivax period prevalence decreased from 1.4% at 0 km to 0.4% within 1 km and the same study month, from 0.9% to 0.3% for infections up to 2 months apart, and from 0.7% to 0.2% across the entire study period of 14 months (Figure 4). Period prevalence was above the null distribution until 200 m within the same study month, until 250 m for infections within 2 months, and 250 m for infections across the study period. 2, Figure 5). Overall, the distance to 50% reduction tends to be shorter with lower prevalence. Legend: From the spatial signatures (for cross-sectional surveys) or spatiotemporal signatures at time window of '0 months', i.e. the same month (for cohort studies) for P. vivax and P. falciparum.

Discussion

Across multiple studies in diverse malaria-endemic regions, we demonstrate clustering of P. vivax and P. falciparum infections in close proximity around index infections, decreasing with distance.

Comparing the signature to a null distribution after random re-allocation of infection locations shows statistically significant clustering in most settings. In a village in Senegal, we have previously shown spatial clustering of P. falciparum infections with this method (14). The finding of spatial/spatiotemporal clustering is in line with previous studies close to the study sites (23,24,40,41) or in other malaria-endemic regions (22, 25-28, 30-39, 42-44), deploying the SaTScan scanning method.

We did not find clustering for P. falciparum in the first Brazilian cross-sectional survey. Given very low global P. falciparum prevalence in both surveys (6), this could be due to a lack of statistical power. Considering the distribution of infection locations on the map, almost all infections surround a forested, sparsely inhabited area in which no samples were collected. The resulting spatial signature is possibly an artefact due to a peculiar distribution of household locations in an insufficiently sampled transmission hotspot.

The signature of P. falciparum infections in the cross-sectional survey in Thailand also lacks statistical significance, however follows a decaying form. This lack of significance most likely stems from low prevalence and insufficient statistical power. A significant spatial signature was also not observed in the Cambodian cross-sectional survey among the villages at the forest fringe. This is most likely because exposure of inhabitants of villages outside the forest is mainly driven by forest-going behaviour (9), and vectors in this region are mainly found in the forests (12).

P. vivax infection distribution in Solomon

Islands' cross-sectional survey showed significant clustering in a flat signature within 1 km distance around index infections, with clustering decreasing at greater distances. There was substantial variation in malaria prevalence between villages (48). By calculating a spatial signature pooled across all villages that were sampled in the survey, we potentially miss significant within-village clustering in high transmission areas.

We also show spatial clustering in the cohort studies, particularly within shorter time windows. The amplitude of the signatures flattens with increasing time windows, demonstrating temporal clustering. Considering only infection pairs of a matching genotype, the P. vivax spatiotemporal signature in the Thai cohort becomes even more pronounced. While the genotyping methods used here lack the resolution to define separate transmission chains with certainty, the finding that infections with a matching genotype cluster in space and time corroborates the principle of the spatial signature method.

The study is subject to certain limitations. First, all included studies were based on household locations. In an area with peri-domestic vector exposure, spatial clustering and a decrease with distance based on household locations can be readily interpreted, mainly by the vectors' hostseeking. The interpretation in areas with mainly behavioural exposure is less straightforward, e.g. in villages outside the forest in the Cambodian site (9). Significant clustering of infections based on household location could still occur though, e.g. resulting from forest-goers tending to live closer to each other in the villages or more general clustering of household locations based on socio-economic factors. The fact that even in these settings clustering might be found is useful from a programmatic perspective.

Another limitation is that the form of the signatures depends on the locations of the households and villages that were sampled, i.e. the underlying distribution of pairwise distances in the data. Pooling across villages attempts to generalize the prevalence dynamics. Studies that sampled across adjacent villages (e.g. the Cambodian survey) are easily accessible for this approach. However, if sampled villages are far apart, it can complicate the interpretation (e.g. the survey in Solomon Islands). We controlled for this limitation by ensuring that no gaps occur in the pairwise distance distributions.

However, the P. falciparum signature in the first Brazilian survey may be an example where these distributions cause artefacts in the signature.

It is generally acknowledged that malaria elimination efforts need to be spatially tailored towards areas where (the highest) transmission risk persists (10,19). The scanning method has proven effective for detecting and locating clusters of infections. However, the method detects a single or multiple hotspots, ordered by their likelihood. The localization of hotspots with the strongest clustering can distract from spatial clustering that also exists outside the statistically most significant cluster. While the assessment of village-level prevalence and scanning methods are very valuable for identifying local spatial structure (e.g. identifying hotspots), these tools provide limited information on the more general properties of the spatial nature of malaria infection. Also, while some studies found 'stable hotspots', i.e. recurring hotspots in the same area from one year to another (36-38), others did not find that temporal predictive value (32). Repeating resource-intensive observational studies in order to regularly update hotspot mapping is not feasible for malaria elimination programs.

Reactive interventions are part of the recommended toolbox for malaria elimination (10). Unless they choose levels such as households or villages, malaria control programmes need to be informed on the radius around index infections in which to operate. Scanning methods report the spatial extent of the detected hotspots which is very specific to the investigated area and only partially generalizable. The spatial signature method allows to detect clustering, to quantify its magnitude, and shows its dynamics across increasing distance. For that purpose, it generalizes the spatial clustering across the entire study area and data points. From a programmatic perspective, it can inform a cost-benefit approach for the optimal selection of a radius around detected index infections, balancing the trade-off between the total number of households included, and the expected proportion of infections found. That clustering on the basis of household locations was found even in areas with occupationally driven exposure is encouraging. From a reactive intervention perspective, such socio-economic clustering may still allow effective targeting, regardless of the underlying dynamics.

We consider the distance to 50% reduction in prevalence as a suitable measure for comparing the signatures across study sites. For both P. vivax and P. falciparum, we observed a trend towards stronger spatial clustering at lower global study prevalence. This suggests that reactive infection detection strategies could become increasingly effective in low transmission settings approaching malaria elimination.

Conclusion

We Background. A detailed understanding of the contribution of the asymptomatic Plasmodium reservoir to the occurrence of clinical malaria at individual and community levels is needed to guide effective elimination interventions. This study investigated the relationship between asymptomatic Plasmodium falciparum carriage and subsequent clinical malaria episodes in the Dielmo and Ndiop villages in Senegal.

List of abbreviatons

Methods. The study used a total of 2792 venous and capillary blood samples obtained from asymptomatic individuals and clinical malaria datasets collected from 2013 to 2016. Mapping, spatial clustering of infections, and risk analysis were performed using georeferenced households.

Results. High incidences of clinical malaria episodes were observed to occur predominantly in households of asymptomatic P falciparum carriers. A statistically significant association was found between asymptomatic carriage in a household and subsequent episode of clinical malaria occurring in that household for each individual year (P values were 0.0017, 6 × 10 -5 , 0.005, and 0.008 for the years 2013, 2014, 2015, and 2016 respectively) and the combined years (P = 8.5 × 10 -8 ), which was not found at the individual level. In both villages, no significant patterns of spatial clustering of P falciparum clinical cases were found, but there was a higher risk of clinical episodes <25 m from asymptomatic individuals in Ndiop attributable to clustering within households.

Conclusion.

The findings provide strong epidemiological evidence linking the asymptomatic P falciparum reservoir to clinical malaria episodes at household scale in Dielmo and Ndiop villagers. This argues for a likely success of a mass testing and treatment intervention to move towards the elimination of malaria in the villages of Dielmo and Ndiop.

Keywords. malaria; asymptomatic; clinical malaria; clustering; interventions.

The World Health Organization (WHO) African Region was home to 93% of the 228 million malaria cases and 94% of the ~405 000 deaths attributed to malaria in 2018 [1], despite the dramatic decline of malaria incidence recorded in past decades in many countries [1], which has led in some places to the belief that elimination could be attained. The move from control to elimination entails the need to shift the primary focus of early detection and rapid case management of clinical malaria cases to the interruption of malaria transmission including the clearance of asymptomatic infections. In areas where repeated exposure to Plasmodium falciparum infections is commonplace, individuals develop nonsterile immunity to clinical malaria [2] that often is associated with a long-term carriage of asymptomatic infections [3][4][5]. In low transmission areas, asymptomatic P falciparum infections have been reported to contribute to 20%-50% of human-to-mosquito infections [6], and parasites from asymptomatic individuals have been reported to be even more infectious to vectors than those from symptomatic cases [7,8].

In Dielmo and Ndiop, 2 villages situated in the center of Senegal (West Africa) [9,10], the disease epidemiology has substantially changed to the point that the elimination of the disease is being considered [11][12][13]. The progress has unfortunately been somewhat hampered by resurgences of malaria attacks [12,13], and a low-level residual malaria transmission attributed to persistent asymptomatic infections is regularly documented in these areas [11,14]. Findings from 3 cross-sectional surveys performed before the malaria transmission season from 2013 to 2015 identified substantial asymptomatic infections in the communities with average parasite carriage of 7.5% and 9.7% in Dielmo and Ndiop, respectively [11].

The microgeographical analysis of such asymptomatic Plasmodium infections in relation with subsequent clinical malaria cases could serve as a rationale to trigger the application of targeted interventions toward the elimination of the disease. In fact, microgeographical analyses can inform how, when, and where clustering occurs and how it relates to transmission patterns [15]. Major elimination strategies such as mass or focused treatment with or without screening and reactive case detection (RCD) rely on being able to identify clusters of infections that can be targeted with appropriate interventions [16].

A recent study conducted at 4 field sites in The Gambia, Mali, and Senegal found separate spatial, temporal, and spatiotemporal clustering of P falciparum infections [17]. However, the authors visited participating households only once in The Gambia (rainy season in 2012) and Mali (dry season in 2013), detected asymptomatic P falciparum infections by using microscopy only, and did not account for submicroscopic and clinical P falciparum infections in the clustering analysis.

The current study capitalizes on cross-sectional-based asymptomatic P falciparum infections documented over 4 consecutive years and clinical P falciparum episodes of the Dielmo and Ndiop cohorts to explore the relationship between asymptomatic P falciparum carriage and subsequent clinical malaria episodes at the individual and household (HH) levels. The study also explored the spatiotemporal clustering and spatial signature of P falciparum infections in the communities in both villages.

MATERIALS AND METHODS

Study Sites and Samples Collection

The study was conducted in Dielmo and Ndiop, 2 Senegalese villages where malaria has been closely monitored since 1990s [9,10]. Asymptomatic individuals were recruited before the malaria transmission season through communitybased cross-sectional surveys in both villages in July 2013, June 2014, June 2015, and June 2016. No formal sample size calculation was performed because the objective was to sample the largest possible fraction of the project's participants who were present in the villages during the sampling period. A total of 2792 venous or capillary blood samples were provided from asymptomatic individuals during the 4 sampling periods. Each participant was identified with a unique code, and HH in both villages were geo-referenced (Figure S1) using a handheld global positioning system device (GPS 76, Garmin International Inc.). The study falls within the ongoing Dielmo and Ndiop project and was examined and approved by the Senegalese National Health Research Ethics Committee.

Detection and Characterization of Plasmodium Species by Microscopy and Molecular Methods

The detection of Plasmodium spp was carried out using both light microscopy (LM) examination of blood films and quantitative real-time polymerase chain reaction (qPCR) as previously described [18] and detailed in Supplementary Material 1. For the qPCR, DNAs from confirmed P falciparum-, Plasmodium malariae-, Plasmodium ovale-, and Plasmodium vivax-infected patients were used as positive controls in all amplifications, whereas sterile water and genomic DNA from uninfected blood samples served as negative controls.

Survey of Clinical Malaria Episodes

Clinical malaria episodes were surveyed among febrile patients (axiliary temperature ≥37.5°C or history of fever in the last 24 hours) who presented at the health posts for consultation after the cross-sectional sampling until December of the same year. Patients positive for malaria by rapid diagnostic test and/ or LM diagnosis were treated with artemether-lumefantrine as recommended by the National Malaria Control Program. Blood samples were collected from patients for follow-up, and all patients' clinical and epidemiological data were recorded in the Dielmo/Ndiop database at Institut Pasteur, where they were consulted for the purpose of this study.

Risk Analysis and Spatial Mapping

All data were analyzed using the R statistical software program. Differences in proportions were assessed using Fisher's exact test. Logistic regression was used to estimate the odds ratio for the associations between PCR positivity in a survey and the risk of experiencing an episode of clinical malaria during the rest of the year. Individual-level analyses were adjusted for age, sex, village, and year of cross-section. Household-level analyses were adjusted for household size, village, and year of cross-section.

The previously described concept of spatial signature was used to assess the degree of spatial clustering of P falciparum infections and clinical episodes of malaria [19]. The relative risk τ of a PCR-positive (or a clinical) case, occurring within a distance d, of a "neighboring" PCR-positive individual (denoted as the "central" case) in the same year is expressed as τ(d) = π(d)/π(d max ). π() is the ratio of the number of pairs of central and neighboring cases within distance d in the same year as the numerator and the number of pairs of central and neighboring cases within distance d in the same year as the denominator. d max is the maximum distance between points in the dataset. Ninety-five percent uncertainty intervals were generated from a null distribution of τ after randomly reassigning the clinical and PCR status across the HH locations and time points 1000 times.

RESULTS

Characteristics of the Study Population

The 2792 blood samples investigated in this study for the presence of Plasmodium parasites were collected in 2013 (N = 611), 2014 (N = 713), 2015 (N = 741), and 2016 (N = 727) (Table 1). The mean and standard deviation of ages of participants were comparable over the 4 periods, and were 21.3 years (19.3) in 2013, 22.1 years (18.6) in 2014, 22.3 years (18.9) in 2015, and 22.3 years (18.8) in 2016 (Table 1). Participants' ages ranged from 0.2 to 93.4 years (Table 1).

Submicroscopic and Clinical P falciparum Infections

The proportions of submicroscopic Plasmodium infections and species composition for the 2013, 2014, and 2015 samples were previously reported, and unique P falciparum infections accounted for 3.26% (21/611), 11.1% (90/713), and 5.8% (44/741) in 2013, 2014, and 2015, respectively [11]. In 2016, all samples were negative by LM, whereas qPCR detected 11.8% (86/727) positive samples, all of which were due to P falciparum (Table 2).

In both villages, the carriage of asymptomatic P falciparum parasites was similarly distributed and was age-dependent, with frequencies being higher in older individuals (>15 years) from 2013 to 2015 [11] and in 2016 (data not shown). The number of clinical malaria episodes recorded during the follow-up period was 126 in 2013 (45 in 2). The frequencies of clinical malaria cases were greater in individuals aged 5-15 years (Table 2).

Mapping of Asymptomatic and Clinical Plasmodium Infections

To generate a high-resolution picture of the localization of P falciparum infections within HHs in both Dielmo and Ndiop villages, asymptomatic P falciparum infections (green spots) and clinical P falciparum episodes (red spots) were positioned within their respective HHs (Figure 1).

In Dielmo, the mapping analysis revealed that 85.71% (6/7), 77.3% (17/22), 21.4% (3/14), and 13.3% (2/15) of HHs with asymptomatic P falciparum infections during the cross-sectional sampling had clinical malaria episodes in 2013, 2014, 2015, and 2016, respectively (Figure 1). Two and 4 clinical malaria episodes occurred among previous asymptomatic carriers (orange spots) in 2013 and 2014, respectively (Figure 1).

In Ndiop, all HHs with asymptomatic P falciparum carriers recorded clinical malaria cases documented on individuals different from asymptomatic carriers in 2013 (Figure 2). The 37 clinical malaria cases of 2014 occurred in 11 HHs, of which only 2 had no previous asymptomatic carriers, whereas 9 occurred in previous asymptomatic carriers (Figure 2). In 2015, 10 clinical malaria cases occurred in 4 households, all of which had previous asymptomatic carriers. In 2016, 8 clinical malaria cases occurred in 6 HHs, 5 of which had previous asymptomatic carriers (Figure 2).

Together, the spatiotemporal mapping of P falciparum infections revealed that 68% (160/236) of clinical malaria episodes occurred in HHs where a P falciparum asymptomatic case existed.

Individual Versus Household Risk Analysis in Dielmo and Ndiop

The individual datasets from Dielmo and Ndiop villages collected throughout the 4-year sampling period were pooled and used to investigate the relationship between asymptomatic P falciparum carriage and subsequent clinical malaria episode at the individual and HH levels by applying Pearson's χ 2 test for statistical difference between proportions. There was no association at the individual level between PCR positivity and the risk of clinical malaria, except for 2014 when PCR-positive individuals had a higher risk of clinical malaria, although this was marginally significant (P = .048) (Table 3). At the HH level, a statistically significant association was observed between asymptomatic carriage in a HH and subsequent episode of clinical malaria occurring in that HH (Table 3) for each individual year (P values were 0.0017, 6.0 × 10 -5 , 0.006, and 0.005 for 2013, 2014, 2015, and 2016, respectively) as well as for the combined years (P = 8.5 × 10 -8 ). The individual analysis of the datasets from each setting showed no significant association at the individual level, whereas a statistically significant association was observed at the HH level in Dielmo (Table S1) and Ndiop (Table S2).

The individual and HH risk analysis was adjusted for confounders and the association between P falciparum prevalence by PCR; the subsequent incidence of clinical malaria was reanalyzed using logistic regression (Table 4). If an individual was PCR-positive during a survey, he or she was 1.77 (95% confidence interval [CI]: 1.06-2.93; P = .028) times more likely to experience a clinical episode of malaria during the rest of the year. If an individual was PCR-negative during a survey, but lived in the same household as a PCR-positive individual, he or she was 1.58 (95% CI: 1.12-2.24; P = .0097) times more likely to experience a clinical episode of malaria during the rest of the year. If an infection was detected by PCR in a HH, that HH was 2.44 (95% CI: 1.00-5.98; P = .0499) times more likely to report a clinical episode of malaria in at least 1 member of the HH (Table 4).

Spatial Clustering of P falciparum Infections

The spatial clustering of P falciparum infections can inform the likelihood of infection occurring in nearby people around an individual with asymptomatic or clinical P falciparum infection compared with other people from the same village. The spatial clustering of P falciparum infections was analyzed by determining the proportion of asymptomatic or clinical cases around a PCR-positive individual according to the distance of the infected individual's HH. No significant patterns of spatial clustering of P falciparum infections were found in Dielmo (Figure 3A). This meant that if we identify an individual with an asymptomatic P falciparum infection, nearby people are not more likely to be infected than other people from the same village. By contrast, there was evidence of significant patterns of spatial clustering of P falciparum infections in Ndiop (Figure 3B) (ie, if we identify an individual with P falciparum infection, nearby people are more likely to be infected than other people from the same village). The spatial clustering of P falciparum clinical cases revealed no significant patterns of spatial clustering of P falciparum clinical cases in Dielmo (Figure 4A) and Ndiop (Figure 4B) (ie, if we identify an individual with asymptomatic P falciparum infection, nearby people are not more likely to experience clinical cases than other people from the same village). The higher risk of clinical episodes < 25 m from PCR-positive individuals in Ndiop is attributable to clustering within HHs.

DISCUSSION

The National Malaria Control Program reported a significant decrease of the incidence of malaria in Senegal [20], allowing the country to embark on elimination programs in parts of the country [21,22]. The dramatic decline observed in Dielmo and Ndiop has questioned the feasibility of eliminating the disease which would requires a reorientation away from the classical control program toward targeted approaches aimed at detecting and curing all Plasmodium infections, as highlighted by WHO [23]. It is largely acknowledged that the elimination strategies in each setting should be based on a detailed understanding of the local epidemiology to guide the application of specific interventions.

In this study, the high prevalence of 11.8% submicroscopic asymptomatic P falciparum infections observed in 2016 is in line with the level of residual parasite transmission reported in the previous years in the areas [11]. The high number of clinical P falciparum malaria attacks recorded in 2013 was attributed to a rebound following the successful implementation of Artemisinin-based combination therapies in June 2006 and long-lasting insecticidal nets in July 2008 [14], and was associated with the fact that people were exposed to infectious mosquito bites while they stayed late outdoors watching television [24]. Throughout the 4-year survey period, clinical malaria attacks were highest in older children 5-15 years old in both villages; a pattern attributed to the shift in age susceptibility [12,28] following long-lasting insecticidal net implementation. Importantly, though the number of asymptomatic and clinical malaria cases varied from 2013 to 2016, the mapping analysis revealed a similar pattern with clinical malaria predominantly occurring in HH of asymptomatic P falciparum carriers.

We identified in Dielmo (2013 and 2014) and in Ndiop in 2014 individuals who were asymptomatic carriers of P falciparum parasites during the cross-sectional survey and who subsequently experienced a clinical malaria episode during the follow-up period. This could be either the result of newly infecting P falciparum clones or the recrudescence of the carried-asymptomatic parasites that have evolved from a subclinical to a clinical threshold. The use of parasite genetic data to track infections within and between HHs has been shown to be critical in the understanding of the extent of within-and between-household transmission dynamics [25][26]. The preliminary genetic relatedness study conducted in Dielmo using msp-1 genotyping revealed similar genetic profiles between asymptomatic and clinical P falciparum parasites from the same individuals and within a HH (Niang et al, unpublished). This suggests that the same parasite populations are circulating within the community in Dielmo, with the asymptomatic reservoir contributing to the occurrence of clinical malaria episodes.

The findings from the risk analysis imply the existence of within-HH transmission with a significant association between the existence of an asymptomatic P falciparum infection within a HH and the subsequent occurrence of clinical malaria episodes in HH members of the asymptomatic P falciparum carrier, and this was also true at both the Dielmo and Ndiop sites when the datasets from each village were analyzed individually or compiled. This is of critical importance because it would suggest that a surveillance system that could detect asymptomatic Plasmodium infections during the dry season could also trigger a response to treat all Plasmodium-infected individuals in the HH and prevent the occurrence of clinical malaria in the HH.

The efficiency of an RCD strategy for malaria elimination in a given setting likely depends on the optimal screening radius and the number of HHs to investigate following the identification of an index P falciparum clinical case. RCD is a detection strategy that takes advantage of the spatial clustering of asymptomatic and/or clinical P falciparum-infected individuals by using passively detected cases as triggers to initiate screening and treatment of individuals living in proximity to those cases. The degree of spatial clustering was assessed using the spatial signature of the likely occurrence of asymptomatic and clinical P falciparum cases in relation to a previously documented P falciparum infection. Beyond the household level, we found significant spatial clustering of P falciparum infections in Ndiop but not in Dielmo. Though significant, the degree of spatial clustering in Ndiop was not strong: individuals living with 20 m of another individual with PCR-detectable P falciparum infection were 1.35 times more likely to also be infected. Such short-range effects are likely driven by household factors. The effect remained significant at greater distances, with individuals living 50 m of another individual with PCR-detectable P falciparum infection being 1.21 times more likely to also be infected (Figure S2). In both villages, there were no significant spatial interactions between PCR positivity in the cross-sectional survey and subsequent risk of episodes of clinical malaria.

An individual's level of immunity may have played a role in maintaining parasites at a subclinical level and contributed to the lack of association found at the individual level. In fact, the asymptomatic carriage has been described as a very complex process associated with low parasite proliferation and an absence of clinical signs related to a specific immunity to soluble antigens and a strain-specific or not antiparasite immunity [27].

Approaches for more efficient malaria elimination interventions have shifted to incorporating spatial dynamics of transmission and to identifying lingering foci. The Framework for Malaria Elimination contained in the WHO Global Technical Strategy for Malaria 2016-2030 included enhanced surveillance, mass drug administration, and active community-based case detection [29]. However, many strategies have encountered variable success in low/moderate transmission settings [30,31] resulting in either operational challenges of such settings and/or the limited understanding of the heterogeneity in malaria risk and transmission. Subscript "ind" denotes the individual level and subscript "house" denotes the household level. Individual-level regressions were adjusted for cross-section, age, sex, and village. Household level regressions were adjusted for cross-section, village, and household size.

Notes

Abbreviations: CI, confidence interval; OR, odds ratio; Pfclin, P. falciparum clinical malaria; PfPCR, P. falciparum PCR positive. the laboratory experiments. M. W., M. S., C. T., J. F., and M. N. curated the data and performed the statistical analyses. M. N. and M. W. drafted the manuscript. All authors contributed to the review and editing of the draft manuscript and approved the final manuscript. All authors read and approved the final manuscript and agree to its submission. All relevant data are within the paper. Specific datasets are available from the corresponding author on reasonable request.

Chapter 6: Discussion, conclusion, and perspectives

Chapter 6 contextualises the findings from the cross-sectional survey and the spatial signature application within the body of knowledge at the time of the studies and since then.

It highlights gaps of knowledge addressed and the expected prospects of the work.

Synthesis

Persisting pockets of high malaria risk on the province and district level

Using PCR with high sensitivity for infections (Canier et The data collected in our survey contributed to further studies by our partners, e.g. a mosquito collection study in the same region that investigated the Anopheles spp. behaviour, species composition, the prevalence of insecticide resistance and pathogen carriage (Vantaux et al., 2021). The social, behavioural, and spatial risk patterns also informed a study of GPStracked mobility in forest-goers in the same study region [START_REF] Pepey | Mobility evaluation by GPS tracking in a rural, low-income population in Cambodia[END_REF].

The high prevalence of Plasmodium spp. infection in a remote area in Mondulkiri in 2017-2018 was alarming. Together with other surveys, these findings corroborated that malaria persisted in remote provinces in Cambodia. With targets of country-wide elimination in the near distance, these point estimates should aid NMPs with informed milestone evaluations (Cambodian Ministry of Health, 2011; World Health Organization, 2015b).

Heterogeneity of infection rates on the village level

The prevalence of infection is highly heterogeneous below the province level. In the survey in Mondulkiri, we found P. vivax and P. falciparum infections at a village-level prevalence that ranged between 0.6% and 36.3% for P. vivax and between 0% and 25.1% for P. falciparum. Variation of prevalence between villages in close proximity has been found in other cross-sectional surveys; however, not at an equally wide range. In the western Battambang province in the dry season in 2015, a survey of 20 villages found no infections in some villages (sampling only 50 people per village). If infections were found, the prevalence of P. falciparum infection varied between 2% and 6% and P. vivax prevalence varied between 2% and 26%. Infection prevalence was found at lower extremes, although an ultra-sensitive PCR was used in the study (Tripura et Our survey in Mondulkiri added a unique observation to the literature on infection prevalence patterns in Cambodia as it sampled from all villages in a study area, with relatively high sample sizes, and with villages along a gradient of proximity to the forest. We categorized them along this gradient based on forest cover as deduced from remote sensing analysis of satellite imagery, an objective approach no other surveys in this area have used. The trend of increasing village-level prevalence with closer proximity to the forest was evident and statistically significant. In the survey by Since the publication of our survey, no further surveys assessing infection risk in the general population have been published for Cambodia. Our approach offers a methodological example for future studies so that the contribution of residential baseline risk vs. behavioural exposure to infection risk in these persistent pockets of malaria transmission in overall elimination settings can be further distinguished.

Spatial clustering of infections beyond the resolution of villages

In a survey of 117 villages over an area of approximately 80 km in Ratanakiri in 2012, [START_REF] Parker | Microgeography and molecular epidemiology of malaria at the Thailand-Myanmar border in the malaria pre-elimination phase[END_REF]. This survey was, interestingly, the only one from other GMS countries that used the SaTScan method to assess spatial clustering based on household locations.

In our study, household-level GPS coordinates also allowed for the detection of clustering below the village level and yielded two clusters of Plasmodium spp. infection. These included households from all study villages located inside the forest. In contrast to the surveys discussed above, detecting artificial spatial clusters due to social clustering was avoided as the analysis was adjusted for non-spatial, behavioural risk factors. That the hotspots remained significant despite the adjustment could indicate additional peri-domestic risk of infection in these villages.

Further studies are needed to underpin the hypothesis of peri-domestic infections in these areas, e.g. assessments of mosquito biting activity throughout the day, similar to the study by Vantaux et al. (Vantaux et al., 2021). Future studies are also needed to quantify the differential contribution of behavioural vs. peri-domestic exposure in more detail. Under the assumption of peri-domestic exposure, it remains unclear whether in-village host-vector contact patterns are dense enough to sustain peri-domestic transmission. Alternatively, the transmission system could depend on parasite 'importation' by individuals infected due to behavioural exposure, e.g. forest goers. More comprehensive infection control in these risk groups would then also drain the transmission system in the villages. This would probably require genotyping or sequencing studies, which are resource-intense but have suggested near-clonal circulating parasite lineages elsewhere (Salla et al., 2020).

The use(fulness) of spatial clusters to guide malaria elimination

The increase in country-wide malaria incidence in Cambodia 2016-2018, see section falciparum hotspot in 2010 was predictive for PCR-detected infection in the second survey (Mosha et al., 2014). In a study from Sudan, consecutive surveys from 1999 until 2008 among the general population in one state found significant hotspots in six of the ten yearly surveys (Nourein et al., 2011). In another study from Sudan, children from 88 villages in a low transmission area were tested for P. falciparum infection by LM in one survey per year each January 1999-2009. Hotspots were detected in all eleven yearly surveys, and the hotspots from ten years overlapped with a spatiotemporal cluster based on the entire study period (Mirghani et al., 2010). However, the hotspots drastically varied in size, from containing only a single village to a diameter of more than 60 km. Also, the ten hotspots that overlapped with the space-time cluster did not all overlap, and the variation (regarding the hotspot size and location) was erratic. In a two-year cohort study among children from villages surrounding a hydropower dam in Ethiopia in 2008-2010, the hotspot for P. falciparum malaria clinical case incidence was stable over the two study years. A hotspot of P. vivax malaria cases was found in each study year but on opposite sides of the study area (Seyoum et al., 2017).

Studies from the GMS continue to cast doubt on the stability of (local) hotspots. The survey in Ratanakiri province, Cambodia, in the dry season in 2012 detected a hotspot of PCRdetected P. falciparum infection that was also found in another survey in the rainy season of the same year. However, the area was not a hotspot of PCR-detectable infections in a consecutive survey one year later. the effect that reducing malaria transmission in the hotspot would also reduce transmission in the surrounding area [START_REF] Bousema | The Impact of Hotspot-Targeted Interventions on Malaria Transmission in Rachuonyo South District in the Western Kenyan Highlands: A Cluster-Randomized Controlled Trial[END_REF].

Applying the scanning method across a wide area based on village coordinates can lead to hotspot detections that are limited in their usefulness for malaria control and elimination.

For example, in the Ratanakiri survey in 2012, one hotspot was a single village with high village-level prevalence, with no extra benefit to conventional village stratification. Other hotspots were vast in geographical extension, some of which contained relatively few infections, e.g. the P. ovale and P. malariae clusters were 32 km and 30 km wide in radius and contained 13 cases and 18 cases, respectively (Sluydts et al., 2014).

Even assuming that detected hotspots would have predictive value of future infection and thus define suitable targets for control interventions, it would be resource-intense for an NMP to survey the entire operational area at sufficient coverage for detailed hotspot mapping, potentially even multiple times over the years. The spatial scanning method also does not generalize hotspot formation. It is unlikely that it would be possible to find common denominators of a set of hotspots in order to extrapolate hotspots in other areas without the need to derive them from detected infections, given the complex interplay of host, mosquito, and pathogen characteristics that make up favourable niches of clustered transmission. Thus, it remains to be demonstrated that spatial scanning for infection hotspots can cost-effectively direct malaria control and elimination interventions and reduce transmission.

Until then, interventions targeting areas of high clinical incidence in the surveillance data and areas populated by known risk groups for infection remain the most practicable guidance for geographical resource allocation by NMPs. However, this stratification of interventions should also be informed by data on the reservoir of sub-patent or asymptomatic infections, as areas of high transmission can have exceptionally high rates of these low parasitaemia infections due to the built-up of immunity. This data would also require (repeated) surveys with active case detection based on molecular detection of infection across the operational area, though at less coverage than hotspot mapping would depend on.

Generalizing spatial clustering of infection risk by the spatial signature method

SaTScan has proven powerful to detect areas of increased risk of observed outcomes, i.e. to localize (albeit potentially transient) hotspots of parasite carriage, and thus to demonstrate heterogeneity in infection risk on small spatial scales. However, it does not enable to generalize such spatial clustering and thus transfer it to other locations. Also, the radius of areas with increased risk varies greatly depending on the setting and underlying location patterns and thus cannot be generalized. Furthermore, focusing on a detected hotspot bears the risk of losing sight of ongoing transmission chains outside the hotspot within active foci. The method can detect multiple areas with statistically significantly increased risk, but only the highest likelihood cluster is usually reported. Also, the hypothesized effect of reductions outside hotspots, if the transmission is hit inside them, has not been underpinned by statistically significant evidence.

We have tried to overcome these methodological blanks by trying to answer the question of how far, on average, around infections (index infections) one would need to search for co-clustering infections (neighbouring infections). For that purpose, we introduced the spatial signature method to malaria epidemiology. where few infections are rapidly detected, reported, treated, and followed up. In China, for example, the success in the late stages towards malaria elimination was also attributed to their 1-3-7 surveillance, i.e. reporting confirmed cases within one day, investigating cases detected by RDTs within three days, and applying targeted control responses within a week.

In Cambodia, an adapted approach was used in an operational district near-elimination, including active case detection of co-travellers, household and family members, as well as surrounding households [START_REF] Kheang | Malaria elimination using the 1-3-7 approach: lessons from Sampov Loun, Cambodia[END_REF]. Spatial distances provided by evidence derived from spatial signature analyses could also contribute to the parameters for this intervention and thus allow further accelerating towards malaria elimination in a feasible and cost-effective manner.

Conclusions

The enormous effort of routine surveillance of clinical cases from health care facilities and VMWs/MMWs allows for monitoring malaria incidence and comparing countries or regions within a country. This data confirms the remarkable reductions in malaria transmission over the last two decades in Cambodia and the GMS overall. As country-wide incidence has been reduced, the landscape of persisting transmission has become increasingly fragmented, and the remaining burden is found in more and more remote areas. Field studies in such areas, like our survey, further reveal the extent of high local transmission levels. These findings support the increase of peripheral access to malaria diagnosis and treatment via the establishment and recent scale-up of the VMW programme by the Cambodian NMP.

Beyond geographical fragmentation of malaria incidence, infection risk is highly heterogeneous on the individual level. As corroborated by our survey, travelling to or working in the forest is the primary behavioural risk factor for infection. Thus, the MMW initiative of the Cambodian NMP and its focus on forest goers appears well targeted. The countries of the GMS have committed to eliminating malaria until 2030 or earlier.

Cambodia aims at the overall elimination of malaria by 2025 and had planned to eliminate P.

falciparum by 2020. The challenges that elimination programmes in Cambodia, the GMS in general, but also other regions face are primarily: the high prevalence of asymptomatic, subpatent infections, the difficulties of curing P. vivax, and the spatial fragmentation of infection risk. These roadblocks to malaria elimination are well established but must be further delineated. As milestones are missed (such as P. falciparum malaria elimination in Cambodia by 2020) and threats such as drug resistance are looming, the need to better inform malaria elimination programmes and render them more efficient with novel tools is apparent.

Perspectives

A better understanding of how relapses contribute to P. vivax transmission remains vital. Membrane feeding studies suggest infectivity of asymptomatic infections in field studies, and mathematical models reconstruct such dynamics [START_REF] White | Modelling the contribution of the hypnozoite reservoir to Plasmodium vivax transmission[END_REF][START_REF] White | Mathematical modelling of the impact of expanding levels of malaria control interventions on Plasmodium vivax[END_REF].

Tools to disentangle relapses from new blood-stage infections in longitudinal studies and analyses of their transmission share would be illuminating. Generally, further cohort studies need to shed light on transmission dynamics. In the Asia-Pacific ICEMR, such a study took place in the same region as our survey and will assess spatiotemporal infection patterns.

Data on infection and household location will allow to fit mathematical models of P.

falciparum and P. vivax transmission and extend them to be spatially explicit. These can then compare approaches to identify and hit pockets of residual transmission, e.g. active vs. passive designs -so that elimination is reached most effectively.

Disillusion grows that elimination can be achieved with the insufficiently sensitive RDTs and LM. For high-risk groups like forest workers, experts thus discuss prophylaxis or regular presumptive drug administration. Presumptive drug administration could also be considered where entire villages remain at high risk. Including 8-aminoquinolines would be an indirect way to hit the silent hypnozoite reservoir, a potentially strong blow to P. vivax transmission.

That hypnozoites cannot be diagnosed remains an obstacle to P. vivax elimination.

Antibody responses to P. vivax antigens allowed to sensitively and specifically detect recent infections and thus hypnozoite carriage (Longley et al., 2020). Serological testing and treatment could thus become a novel tool for relapse prevention and transmission reduction.

At very low incidence, NMPs begin to follow up cases, co-exposed individuals, household members, and potentially neighbours. Spatial signatures could inform that process.

Further use in similar settings must show the generalizability of distance estimates. The clustering of infections across diverse settings suggests using spatial signatures for reactive approaches also for burden reduction. The method provides a tool for NMPs to make informed decisions on operational distances, considering the number of households needed for a certain coverage vs. feasibility. village is in category "outside forest", "forest fringe", or "inside forest", resp. Significance asterisk for Kruskal-Wallis test of differences in village-level prevalence.

Supplementary Figures

Figure S3

Prevalence of infection by age per gender and species. Significance asterisks for test of differences in prevalence across age groups per gender strata.

  al., 2009; Dondorp et al., 2009; Noedl et al., 2008; Noedl, Socheat, & Satimai, 2009; Vijaykadga et al., 2006; World Health Organization, 2013). Except for China, which reports zero cases since 2017, malaria remains endemic across the GMS, with high malaria incidence and mortality observed until recently in Cambodia (World Health Organization, 2021a, 2021b). In the WHO Strategy for malaria elimination in the Greater Mekong Subregion 2015-2030, all GMS countries committed to eliminating P. falciparum in areas where multidrug resistance is known by 2020, all P. falciparum malaria by 2025 and all malaria by 2030. Some countries even declared earlier targets, e.g. elimination of P. falciparum malaria overall by 2020 and complete malaria elimination by 2025 in Cambodia (Cambodian Ministry of Health, 2011; Chinese Ministry of Health, 2010; World Health Organization, 2015b).

Figure 2

 2 Figure 2 Annual case numbers of indigenous overall and P. falciparum malaria cases in the GMS from 2000 until 2020 (World Health Organization, 2021a).

Figure 3

 3 Figure 3 Malaria case numbers (blue bars, left axis) and deaths (orange line, right axis) as per the Cambodian control programme (Cambodian National Center for Parasitology, 2021).

  interventions to tailor elimination interventions to the local distribution of the remaining reservoir of infection and to target high-risk groups, see Figure 4 (Global Malaria Programme, 2017; World Health Organization, 2015a, 2022).

Figure 4

 4 Figure 4 The effect of (a) an untailored intervention programme compared to (b) a programme that stratifies interventions by transmission levels (World Health Organization, 2014).

Figure 5

 5 Figure 5 Provinces in Cambodia (left) and malaria incidence in the GMS in 2012 and 2020 (NIRVn, 2014; World Health Organization, 2021a).

  One way to delineate where remaining transmission occurs is to associate disease or infection risk on different spatial scales with increasing granularity, e.g. villages, hamlets, or individual households. Bannister-Tyrrell et al., for example, conducted a cross-sectional survey in three villages in Ratanakiri province, north-eastern Cambodia, in 2016-2017. They found that odds for Plasmodium spp. infection were sufficiently explained by household-level information by contrast to individual or village-level covariates, suggesting the household as the unit of risk clustering (Bannister-Tyrrell et al., 2018).

  . In a low transmission area in South Africa, Mpumalanga province, SaTScan assisted in identifying clusters of confirmed malaria cases in seven towns over three years, 2002-2005 (Coleman et al., 2009). Based on cross-sectional surveys at the Thailand-Myanmar border in 2011-2012, Parker et al. found clusters of P. vivax infections in the dry season around a few year-round water sources (Parker et al., 2015).

Figure 6

 6 Figure 6 Spatial signature of dengue cases in the same month, i.e. the relative probability (y) of two case pairs being of the same serotype within a distance interval (x) compared to any other case pair carrying the same genotype. The ribbon indicates the 95% confidence interval as derived from a null distribution that erased any spatial clustering in time (Salje et al., 2012).

Chapter 3 :

 3 Forest malaria in Cambodia: the occupational and spatial clustering of Plasmodium vivax and Plasmodium falciparum infection risk in a cross-sectional survey in Mondulkiri province,

Fig. 1

 1 Fig. 1 Geographic and demographic description of survey population. a Map of survey household locations, coloured by village in shades of blue, purple, or green if village is in category "outside forest", "forest fringe", or "inside forest", respectively. Background Landsat-8 image courtesy of the U.S. Geological Survey. b Representativeness of age distribution in women and men by overlaying distribution in census (bars) by distribution in survey (points)

Fig. 2

 2 Fig.2Prevalence of P. vivax infection per village (as boxplots by proximity of villages to the forest in a and as squares in b). Household locations of survey participants in the map background, transparently coloured by village in shades of blue, purple, or green if village is in category "outside forest", "forest fringe", or "inside forest", respectively. Significance asterisk for Kruskal-Wallis test of differences in village-level prevalence

Fig. 4

 4 Fig. 4 Spatial clusters of Plasmodium infection (red-shaded circles) as detected by covariate-adjusted SaTScan analysis. White dots represent household locations in the survey, red dots for PCR-positive participants

Figure 1

 1 Figure 1 Spatial signature of P. vivax or P. falciparum infections in the Cambodian cross-sectional

Figure 2

 2 Figure 2 Spatial signatures of infections in the cross-sectional surveys in Thailand, Brazil, and

Figure 3

 3 Figure 3 Spatial and spatiotemporal signatures of infections in the cohort studies in Thailand and

Figure 4

 4 Figure 4 Spatiotemporal signatures of infections with a matching genotype in the cohort study in

Figure 5

 5 Figure 5 Comparative analysis of distance to 50% reduction in prevalence by global study

FiguresFigure 1

 1 Figures

Figure 2

 2 Figure 2 Spatial signatures of infections in the cross-sectional surveys in Thailand, Brazil, and

Figure 3

 3 Figure 3 Spatial and spatiotemporal signatures of infections in the cohort studies in Thailand and

Figure 4

 4 Figure 4 Spatiotemporal signatures of infections with a matching genotype in the cohort study in

Figure 5

 5 Figure 5 Comparative analysis of distance to 50% reduction in prevalence by global study

  Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022 Clustering of Asymptomatic and Clinical P falciparum Infections for Targeted Interventions • cid 2021:73 (15 december) • 2177

  Dielmo and 81 in Ndiop), but decreased progressively over time to 86 in 2014 (49 in Dielmo and 37 in Ndiop), 13 in 2015 (3 in Dielmo and 10 in Ndiop), and 11 in 2016 (3 in Dielmo and 8 in Ndiop) (Table

  Author contributions. M. N. and A. T-B. conceived and designed the study. F. D. S., J. F., A. B., N. D., C. S., and A. T. B. coordinated the fieldwork and samples collection. M. N., A. F. M., R. S., B. D., and L. G. T. performed

Figure 4 .

 4 Figure 4. Spatial clustering of clinical cases near PCR-positive Plasmodium falciparum infections in (A) Dielmo and (B) Ndiop villages. Abbreviation: PCR, polymerase chain reaction.

Figure 3 .

 3 Figure 3. Spatial clustering of PCR-positive (asymptomatic) Plasmodium falciparum infections in (A) Dielmo and (B) Ndiop villages. Abbreviation: PCR, polymerase chain reaction. Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

2. 1 ,

 1 was responded to not only by reinstating the VMW programme and changing the first-line ACT. The NMP also launched the Intensification Plan that ran from 2018 until 2020 and targeted areas and populations with increased malaria risk with further scaled-up testing and treatment by VMWs/MMWs. The clustering of infections at the village or the sub-village level and in hard-to-reach populations, as described in our and other cross-sectional surveys, explicitly informed the programme[START_REF] Sovannaroth | Accelerating malaria elimination in Cambodia: an intensified approach for targeting at-risk populations[END_REF][START_REF] Stratil | Eliminating Plasmodium falciparum malaria: results from tailoring active case detection approaches to remote populations in forested border areas in north-eastern Cambodia[END_REF].The concept of transmission 'hotspots' is based on the idea that in every endemic setting, small clusters of households exist that have an increased risk of transmission of Plasmodium spp. infection due to local circumstances that facilitate transmission, e.g. proximity to breeding sites[START_REF] Bousema | Hitting hotspots: spatial targeting of malaria for control and elimination[END_REF]. From these overly active transmission systems, infections 'spill' outwards into the surroundings and maintain transmission there, particularly at overall low transmission levels. Likewise, transmission cycles can persist over the low transmission season within these hotspots and cause expanding transmission with the onset of the high transmission season. Targeting such hotspots would be efficient in reducing infection levels. Malaria control and elimination programmes would thus need to survey intervention areas once, detect the hotspots, and target their interventions to them. This concept presumes the stability of such hotspots over time. Some studies indeed found areas that were repeatedly hotspots in consecutive surveys. In two surveys one year apart among four villages in Tanzania in the dry seasons of 2010 and 2011, being in a P.

  Figure S1 Classification of villages as to their forest proximity based on forest cover in 750 m

Figure S2

 S2 Figure S2 Prevalence of P. falciparum infection per village (as boxplots by proximity of villages

  positive P. malariae samples omitted

		N	PCR positivity
			Plasmodium	P. vivax	P. falciparum	Co-infections
	Proportion (n) of survey samples	4200	8.3% (349)	6.4% (268)	3.0% (125)	1.1% (48)
	Extrapolated population prevalence	10,053	8.9%	6.8%	3.3%	1.3%
	Extrapolated prevalence per village
	Outside forest
	Trapaingphiae	594	0.6% (3)	0.6% (3)	0% (0)	0% (0)
	Chhnaeng	550	2.7% (12)	1.9% (8)	0.9% (4)	0.3% (1)
	Oham	333	8.0% (20)	6.2% (16)	2.9% (7)	1.1% (3)
	Ohrana	470	5.7% (25)	4.2% (18)	1.8% (8)	0.2% (1)
	Sraepreas	228	6.5% (13)	5.6% (11)	1.7% (3)	0.8% (1)
	Sraektum	296	13.1% (36)	8.6% (24)	4.8% (13)	0.8% (2)
	Lapakhe	71	10.6% (4)	6.7% (3)	7.8% (2)	3.9% (1)
	Ohkaunpreas	317	6.9% (21)	5.9% (18)	1.7% (5)	0.7% (2)
	Trapaingtouk	201	1.9% (3)	1.2% (2)	0.6% (1)	0% (0)
	Forest fringe
	Poucha	239	9.7% (24)	6.7% (17)	4.3% (10)	1.4% (3)
	Sraeampilkroam	218	11.0% (23)	7.2% (15)	3.8% (8)	0.6% (1)
	Sraeampilleu	168	10.6% (17)	8.2% (13)	2.4% (4)	0% (0)
	Inside forest
	Beng-Gaty	106	37.9% (40)	30.9% (32)	25.1% (27)	18.1% (19)
	Ohchra	152	40.4% (60)	36.3% (54)	10.0% (15)	5.9% (9)
	Sraelvy	154	18.7% (28)	15.5% (23)	3.9% (6)	1.3% (2)
	Ohtrone	103	19.7% (20)	10.4% (11)	12.4% (12)	3.1% (3)

Table 2 Detectability of infections through the public health care system

 2

	Measure	PCR positivity
		P. vivax	P. falciparum
	LM +	16.8% (45/268)	25.6% (32/125)
	Symptoms at interview	6.7% (18/268)	16.8% (21/125)
	Symptoms & LM +	2.2% (6/268)	4.8% (6/125)
	Symptoms & RDT +	0.4% (1*/268)	4.0% (5/125)
	Symptoms & RDT/LM +	2.2% (6/268)	7.2% (9/125)

Proportion (N) of PCR-positive samples that were also detectable by LM, inspection of symptoms, or by testing of symptomatic individuals with LM or RDTs *Actually RDT + for P. falciparum in a co-infection with P. vivax as by PCR

Table 3 Densities of asexual and sexual stages in LM + samples

 3

	LM positivity
	P. vivax	P. falciparum

Geometric mean and range always given from among samples with non-zero densities Gametocytaemia Proportion (N) of LM + samples with gametocytes 2.7% (1/37) 37.0% (10/27) Geometric mean [/μl blood] 31.9 51.1 Range [/μl blood] 31.9-31.9 15.6-175.0

Table 4 Univariate association of infection risk and behavioural covariates

 4

	None	3465	4.9% (171)	< 0.001 2.0% (70)	< 0.001
	Field sites	90	3.3% (3)		4.4% (4)
	Forest sites	268 22.4% (60)		14.9% (40)
	A village	177 13.6% (24)		5.6% (10)
	A city	59	1.7% (1)		0% (0)
	Unspecified	123	7.3% (9)		0.8% (1)
	Work in the last two months in…
	…deep forest
	No	4055	5.7% (231)	< 0.001 2.4% (98)	< 0.001
	Yes	145 25.5% (37)		18.6% (27)
	…cassava field
	No	2115	5.6% (118)	< 0.05	2.9% (62)	n.s
	Yes	2085	7.2% (150)		3.0% (63)
	No work
	No	3186	7.9% (253)	< 0.001 3.7% (119)	< 0.001
	Yes	1014	1.5% (15)		0.6% (6)
	Slept outdoors last night
	Indoors	4124	6.2% (256)	< 0.01	2.9% (118)	< 0.01
	Outdoors	76 15.8% (12)		9.2% (7)
	Sprays repellent usually at bedtime
	No	3668	6.7% (247)	< 0.05	3.2% (116)	< 0.10
	Yes	524	4.0% (21)		1.7% (9)
	Slept under net last night
	No	320 10.0% (32)	< 0.01	6.6% (21)	< 0.001
	Yes	3880	6.1% (236)		2.7% (104)

Prevalence (n) of PCR-positivity across the strata of those behavioural variables that were statistically significant in univariate logistic regression. "n.s. ": Not significant

Covariate N P. vivax P. falciparum

Prevalence p-value Prevalence p-value

Work-unrelated travels overnight in the last month to…

Table 5 Risk factors after multivariate mixed-effects logistic regression for Plasmodium infection as detected by PCR

 5

	Covariate	N	aOR	95% CI	p
	Gender
	Female		Reference		< 0.001
	Male		3.06	[2.26-4.13]
	Age [years]
	2-10		Reference		< 0.001
	11-15		4.51	[2.52-8.06]
	16-20		7.47	[4.14-13.47]
	21-25		7.84	[4.20-14.63]
	26-30		8.08	[4.36-14.94]
	31-35		7.71	[4.05-14.70]
	36-40		7.15	[3.76-13.58]
	41-45		5.33	[2.67-10.65]
	46-50		4.16	[1.92-8.99]
	51-80		2.68	[1.36-5.29]
	Forest proximity of village
	Outside forest		Reference		< 0.001
	Forest fringe		2.14	[1.02-4.52]
	Inside forest		12.47	[6.29-24.71]
	Material of roof
	Grass/leaves		0.96	[0.26-3.55]	< 0.01
	Tent		3.43	[1.36-8.64]
	Corrugated iron		Reference
	Wood planks/cement/tiles		0.50	[0.30-0.84]
	Household owns a toilet
	No		Reference		< 0.05
	Yes		0.69	[0.48-1.00]
	Household owns buffaloes
	No		Reference		< 0.01
	Yes		2.11	[1.21-3.69]
	Household head had received information on malaria via TV in the past
	3 months
	No		Reference		< 0.01
	Yes		0.37	[0.16-0.81]
	Insecticides had been sprayed inside the house in the past year
	No		Reference		< 0.01
	Yes		0.62	[0.43-0.89]
	Slept outdoors last night
	No		Reference		0.08
	Yes		1.99	[0.93-4.25]
	Slept under net last night
	No		Reference		0.96
	Yes		0.99	[0.61-1.59]

Table 5 (continued)

 5

	Covariate	N	aOR	95% CI	p
	Work-unrelated travels overnight in the last month to…
	None	3465 Reference		< 0.01
	Field sites		90 0.63	[0.25-1.59]
	Forest sites	268 2.17	[1.41-3.35]
	A village	177 1.28	[0.75-2.17]
	A city		59 0.27	[0.03-2.13]
	Unspecified	123 1.23	[0.56-2.74]
	Work in the last two months in…
	…deep forest
	No	4055 Reference		< 0.001
	Yes	145 2.88	[1.69-4.93]
	…cassava field
	No	2115 Reference		< 0.05
	Yes	2085 1.37	[1.01-1.85]

Table 1

 1 Design and basic summary of included cross-sectional surveys and cohort studies.

	Study type	Country	Number of	Timing and duration	P. vivax	P. falciparum	Median age	Gender [%]
		(reference)	participants		PCR prevalence	PCR prevalence	[range]
	Cross-	Cambodia	4,200	Dec 2017-Apr 2018	6.4%	3.0%	22 years	Female 53%
	sectional	(9)		(dry season)			[2-79]
	surveys once) (sampling	(7) Thailand	4,309	(low transmission season) Sep-Oct 2012	3.1%	0.9%	[0-92] 20 years	Female 52%
		Brazil	2,010 (Survey 1)	Nov 2012-Jan 2013	4.3%	0.8%	23 years	Female 47%
		(6)		(rainy season)			[0-100 †]
			2,073 (Survey 2)	Aug-Sep 2013	3.4%	<0.1%	25 years	Female 48%
				(dry season)			[0-100 †]
		Solomon	3,501	May-June 2012	13.4%	0.1%	18 years	Female 53%
		Islands		(minimal seasonality)			[1-100]
		(48)
	Cohort	Thailand	999	May 2013-Jun 2014	1.7-4.2%*	0-1.3%*	23 years	Female 54%
	studies	(8)	(14 sampling visits)				[1-82]
	(monthly sampling)	(47) Brazil	(13 sampling visits) 1,274	Apr 2013-Mar 2014	2.5-6.5%*	0-1.0%*	[0-100 †] 25 years	Female 49%

Table 2

 2 Distance to 50% reduction in prevalence and global study prevalence across the studies.

	Study type	Study site	P. falciparum		P. vivax
			Global study	Distance to 50%	Global study	Distance to 50%
			prevalence	reduction in	prevalence	reduction in
				prevalence		prevalence
	Cross-sectional	Cambodia	3.0%	975 m	6.4%	875 m
	surveys	Thailand	1.1%	75 m	3.3%	25 m
		Brazil Survey 1	-	-	4.2%	175 m

  demonstrate spatial clustering of P. vivax and P. falciparum infections across a diverse set of study sites and transmission intensities. Introducing a novel method in spatiotemporal malaria epidemiology, we quantify the distance within which clustering occurs around index infections. These distances are often short, e.g. below 200 m, tending to lower values at lower global study prevalence. The spatial signature of Plasmodium spp. infections offers a new tool to extract insights on Plasmodium spp. epidemiology from observational epidemiological field studies. It also provides a method to inform reactive infection detection strategies regarding effective and feasible radius choices of interventions around detected infections.

1 Mirco Sandfort, 2,3 Adja Fatou Mbodj, 1 Babacar Diouf, 1 Cheikh Talla, 4 Joseph Faye, 4 Rokhaya Sane, 1 Laty Gaye Thiam, 1 Alassane Thiam, 1 Abdoulaye Badiane, 4 Ines Vigan-Womas, 1 Nafissatou Diagne, 5 Fatoumata Diene Sarr, 4 Ivo Mueller, 2 Cheikh Sokhna, 5 Michael White, 2 and Aissatou Toure-Balde 1 1

  Institut Pasteur Dakar, Pôle Immunophysiopathologie & Maladies Infectieuses, Dakar, Sénégal; 2 Malaria: Parasites and Hosts Unit, Department of Parasites & Insect Vectors, Institut Pasteur, Paris, France; 3 Sorbonne Université, Collège doctoral, Paris, France; 4 Institut Pasteur Dakar, Pôle Epidémiologie, Recherche Clinique et Science des données, Dakar, Sénégal; and 5 VITROME, Campus international IRD-UCAD, Dakar, Sénégal

	Fine-scale Spatiotemporal Mapping of Asymptomatic
	and Clinical Plasmodium falciparum Infections:
	Epidemiological Evidence for Targeted Malaria
	Elimination Interventions
	Makhtar Niang,
	CSS	Cross-sectional survey
	GPS	Global positioning system
	PCR	Polymerase chain reaction

Table 1 . Demographic Characteristics of the Study Population

 1 Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

		2013		2014		2015		2016
		N = 611		N = 713		N = 741		N = 727
	Total	N	%	N	%	N	%	N	%
	Location
	Dielmo	301	49.3	348	48.3	357	48.2	354	48.0
	Ndiop	310	50.7	365	51.7	384	51.8	373	52.0
	Sex
	Male	249	40.7	327	45.4	340	45.9	311	42.2
	Female	362	59.3	386	54.6	401	54.1	416	57.8
	Age
	Mean	21.3	…	22.1	…	22.3	…	22.3	…
	Range	0.4-91.5	…	0.2-92.5	…	0.2-93.4	…	0.3-82	…
	±SD	19.3		18.6		18.9		18.8
	Age groups, y
	≤5	113	18.6	120	16.8	121	16.3	130	17.9
	5-15	201	32.9	215	30.2	226	30.5	211	29.0
	>15	297	48.5	378	53.0	394	53.2	386	53.1

Abbreviation: SD, standard deviation.

Table 2 . Microscopy and qPCR-Based Parasite Prevalence and Clinical Plasmodium falciparum Cases in the Studied Areas and Periods

 2 Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

		2013		2014		2015		2016
	No.	611		713		741		727
	Villages	Dielmo	Ndiop	Dielmo	Ndiop	Dielmo	Ndiop	Dielmo	Ndiop
	N (clinical surveillance)	312	332	353	366	357	385	356	376
	N (sample)	301	310	348	365	357	384	354	373
	Microscopy
	N	0	0	1	1	1	0	0	0
	%	0	0	0.28	0.27	0.28	0	0	0
	qPCR (all species) a
	N	14	9	45	47	25	21	22	64
	%	4.7	2.9	12.9	12.9	7.0	5.2	6.2	17.2
	qPCR (Pf)
	N	13	8	43	47	24	20	22	64
	%	4.3	2.6	12.4	12.9	6.7	5.2	6.2	17.2
	Submicroscopic
	N	13	8	42	46	23	20	22	64
	%	4.3	2.6	12.1	12.6	6.4	5.2	6.2	17.2
	Clinical malaria
	N	45	81	49	37	3	10	3	8
	Age groups, y (%)
	<5	1/54 (1.9)	12/61 (19.7)	5/49 (10.2)	6/72 (8.3)	0/51 (0)	0/70 (0)	0/50 (0)	1/81 (1.2)
	5-15	23/112 (20.5)	31/97 (32.0)	19/113 (16.8)	13/104 (12.5)	1/110 (0.9)	5/117 (4.3)	0/106 (0)	4/106 (3.8)
	>15	21/146 (14.4)	38/174 (21.8)	25/191 (13.1)	18/190 (9.5)	2/196 (1.0)	5/198 (2.5)	3/200 (1.5)	3/189 (1.6)

Abbreviations: Pf, Plasmodium falciparum; qPCR, quantitative polymerase chain reaction. a The qPCR detected no P. vivax. P. ovale was found as single infections in 2 individuals in 2015, and in co-infections with P. falciparum in 1 and 9 individuals in 2013 and 2014, respectively. P. malariae was not detected in 2015, but 2 unique cases were detected in 2013 and 2014, respectively. of Asymptomatic and Clinical P falciparum Infections for Targeted Interventions • cid 2021:73 (15 december) • 2179

Table 3 . Individual Versus Household Risk Analysis of the Relationship Between Asymptomatic Plasmodium falciparum Carriage and Subsequent Clinical Malaria Episode in Dielmo and Ndiop Villages

 3 Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

		All Years
		2016
		2015
	Analysis at Individual Level	2014
		2013

Table 4 . Results of Logistic Regression for the Association Between Plasmodium falciparum Prevalence by PCR and the Subsequent Incidence of Clinical Malaria

 4

		Pfclin ind -PfPCR ind	Pfclin ind -PfPCR house	Pfclin house -PfPCR house
		Unadjusted	Adjusted	Unadjusted	Adjusted	Unadjusted	Adjusted
	Dielmo
	OR (95% CI)	1.57 (0.76-3.24)	1.76 (0.83-3.73)	1.95 (1.15-3.30)	2.54 (1.45-4.45)	6.86 (3.33-14.13)	4.51 (1.31-15.62)
	P value	.22	.14	.013	.001	1.7 × 10 -7	.017
	Ndiop
	OR (95% CI)	1.05 (0.54-2.00)	1.78 (0.89-3.55)	0.81 (0.53-1.23)	1.05 (0.70-1.74)	5.05 (2.44-10.45)	1.32 (0.32-5.47)
	P value	.88	.10	.32	.66	1.2 × 10 -5 *	.70
	Dielmo and Ndiop
	OR (95% CI)	1.26 (0.78-2.04)	1.77 (1.06-2.93)	1.19 (0.86-1.65)	1.58 (1.12-2.24)	5.90 (3.54-9.84)	2.44 (1.00-5.98)
	P value	.88	.028	.29	.0097	1 × 10 -11	.0499

  al., 2017). Falq et al. detected infections in the northern

	Preah Vihear province in 2014 at a prevalence of 0-4.6% for P. falciparum, 0-3.6% for P. vivax,
	or 0-12.1% for Plasmodium spp. across all 21 villages of one district, despite the use of ultra-
	sensitive PCR and sampling in the rainy season (Falq et al., 2016). Only a survey in Ratanakiri
	in the rainy season more than 15 years earlier found a more extreme village-level infection
	prevalence, ranging less widely though (Steenkeste et al., 2010). Between-village variation in
	prevalence was also described in Thailand (Sattabongkot et al., 2018) and Laos (Iwagami et
	al., 2017; Phommasone et al., 2016; Pongvongsa et al., 2016).

  Tripura et al. from 2015 in Battambang province, a similar trend of village-level prevalence and proximity to the forest is apparent from the map. However, it is not analytically tested[START_REF] Tripura | Submicroscopic Plasmodium prevalence in relation to malaria incidence in 20 villages in western Cambodia[END_REF]. Incardona et al. sampled from randomly selected villages in three provinces in 2001-2003 and provided maps of the villages, their infection prevalence based on LM, and forest-covered areas (drawn from aerial and satellite photographs and field verifications). Prevalence tended to be high in remote, forested, mountainous areas or camps outside villages at the forest fringe(Incardona et al., 2007).The subsequent analysis of the survey in Battambang focused on behavioural exposure by spending time in the forest without accounting for the baseline exposure when residing close to the forest[START_REF] Tripura | Submicroscopic Plasmodium prevalence in relation to malaria incidence in 20 villages in western Cambodia[END_REF]. Incardona et al. accounted for distance to the forest in their multivariable regression model for infection risk and found an association in two of the three provinces(Incardona et al., 2007). However, they could not simultaneously account for behavioural exposure. Our survey demonstrated by multivariable mixed-effects logistic regression that both behavioural exposure (i.e. working in or travelling to the forest) and living in villages at the forest fringe or inside the forest were independent risk factors of infection.

  Sluydts et al. found hotspots of infection risk with Plasmodium spp. in general, and by species(Sluydts et al., 2014). These hotspots were located mainly at a river, but locations varied per species. The hotspots had a 24-32 km radius and were based on the villages' coordinates, i.e. could not stratify infection risk beyond the village scale by design. By contrast, in three villages in Ratanakiri in the dry season in 2016, Durnez et al. searched for spatial clusters of PCRdetectable infections per village separately, based on GPS coordinates per household (Durnez et al., 2018). Clusters of P. vivax, P. falciparum, or P. malariae infections could be located among field houses in densely forested areas and inside two villages close to the forest. These hotspots were between less than 100 m to 1.8 km wide. One cluster in a village included only one house inhabited by numerous families, which could hint at social clustering in the village. Spatial clustering of infections on sub-village-level elsewhere in the GMS was shown by Parker et al. in a village at the Thailand-Myanmar border in 2011-2012, where cases clustered in households close to a water drainage system and other continuous water sources

  The same holds for a hotspot of P. vivax infection detected in the dry and rainy seasons in 2012. A P. vivax hotspot detected in 2013 was at a different site. Hotspots based on clinical case incidence were in yet different locations for P. falciparum and would not have predicted the hotspot of P. vivax infections in 2013 (Kerkhof et al., 2016; Sluydts et al., 2014). In two consecutive surveys among households from 3 villages in Ratanakiri province in 2016 and 2017, living in a household in a hotspot of PCR-detected infection in 2016 was not associated with an individual or household-level risk of infection at the follow-up survey one year later (Bannister-Tyrrell et al., 2019). Furthermore, a clusterrandomized trial of hotspot-targeted interventions in Kenya could not observe evidence for

  It was originally developed and applied to dengue fever, HIV/AIDS, and measles(Lessler et al., 2016; Salje et al., 2012). Its initial development was designed to infer spatial clustering from case locations without knowing the spatial distribution of the underlying population from which the cases arose. Instead, it used data on the genetic relatedness of the virus strains of the cases. Spatial clustering was inferred from cases closer to each other having a higher chance of being infected with more closely related pathogen strains.In malaria surveys, the genetic relatedness of the parasite strains of infections by P. falciparum, P. vivax, or other species is laborious to determine and rarely reported. However, an underlying population in which the cases were detected is usually known. Hence, we can assess the pooled prevalence of neighbouring infections at a certain distance from index infections and how it develops with increasing distance, see Chapter 4. By randomly re-That is, infections tend to cluster more in lower transmission settings. Overall, similar distance ranges within which most of the risk reduction occurs were observed in the studies of dengue fever, HIV/AIDS, and measles(Lessler et al., 2016; Salje et al., 2012).In our study in the two Senegalese villages of Ndiop and Dielmo, we had the chance to apply the spatial signature method in a Sub-Saharan setting and assess the degree of spatial clustering of PCR-detectable P. falciparum infections and clinical malaria episodes(Niang et al., 2021), see Chapter 5. Based on blood samples from the general village populations from each year from 2013 until 2016 and household GPS locations, PCR-detectable infections were found to spatially cluster in Ndiop within 70 m around index infections but not in Dielmo.Clinical cases were not found to spatially cluster beyond the household level.When applied to the monthly time points of cohort studies of PCR-detectable P. vivax infections in Thailand and Brazil, the dilution of spatial clustering of pairs of incident infections (first detections of an infection in a given individual) with widening temporal windows indicated temporal clustering. However, in both settings and particularly in the Brazilian study, the elevated period prevalence of incident case pairs at close proximity was still statistically significant when assessed across the entire year-long study period, indicating the predictive potential of detecting nearby cases around the location of index cases across a year in time.Among mobile and migrant populations, MMWs should also provide RDT testing and effective treatment but also actively search for cases that otherwise might not have reported themselves to the MMWs. Firstly, the plan includes active test and treat outreach twice per month to individuals in forest hotspots. Secondly, MMWs are supposed to ask positive forest goers for their co-travellers to test them for infection and treat them if necessary. No active case detection is intended for the village-level, VMW-guided layer of the intervention.The more homogeneously distributed infection risk across the entire village population in forest villages in our Mondulkiri cross-sectional survey, as well as the short-range distance clustering of infections in our spatial signature analyses, suggest that such an active case detection approach could be helpful in tackling within-village transmission cycles. It would be helpful to repeat the spatial signature analysis on survey data from various regions with high levels of persistent transmission in Cambodia to assess the generalizability of specific estimates of distances within which infections cluster to a certain degree. Based on this, the NMP would then be able to make an informed choice at what distance from index cases to search for other cases, balancing the expected share of clustering infections that would be covered against the feasibility, i.e. the number of households that would need to be screened.AsKunkel et al. argue that the sensitivity of the testing by RDTs is insufficient[START_REF] Kunkel | Choosing interventions to eliminate forest malaria: preliminary results of two operational research studies inside Cambodian forests[END_REF]) and PCR detection is not feasible at the VMW points of care, a spatially informed drug administration scheme could be considered: Families who reside within a certain proximity around index cases are offered treatment without certainty of actual current infection, in analogy to the intermittent preventive treatment of forest goers suggested byKunkel et al.

allocating case locations, statistical significance can be assessed by comparing the observed signature with a null distribution. In that way, several purposes are fulfilled: 1) Spatial clustering is tested for and detected if case pair prevalence is elevated at a close distance above the null distribution, 2) the magnitude of the spatial clustering is quantified by the increase of prevalence with a shorter distance, and 3) the clustering is assessed on the continuous scale of distance between observation pairs. Spatiotemporal patterns can be assessed by comparing pairs of observations sampled at different times. As demonstrated, spatial and spatiotemporal clustering of PCR-detectable infection risk existed to differing degrees across a diverse set of transmission settings from Cambodia, Brazil, Thailand, and Solomon Islands. At medium to low prevalence, the distance to a 50% reduction in prevalence was within 1 km, often even below 100 m around index cases, with a trend to lower distances at lower global study prevalence.

6.1.6. Spatially informing (re-)active case detection schemes

As the major recent intervention scheme to accelerate progress towards the malaria elimination targets, the Cambodian NMP launched the Intensification Plan

(Sovannaroth et al., 2022)

. On top of the already established malaria control and intervention activities, the plan contains a further scale-up of VMW and MMW coverage and activities. In identified hotspots, VMWs ought to fill gaps in households owning LLINs and their hammock versions, continue to provide RDT testing and effective ACT treatment, and ensure immediate referral of severe malaria cases to the nearest health clinic, all among the general village populations. and Dysoley et al. (Lek et al., 2020). Once transmission has been largely interrupted in an area, e.g. by the Intensification Plan interventions, malaria elimination programmes need to switch to responsive approaches

  Spatial scanning methods like SaTScan are powerful for scanning study areas for statistically significant clusters of positive observations, e.g. hotspots of PCR-detected cases.Their use in spatial malaria epidemiology has been embedded in the hypothesis that certain locations are particularly favourable for transmission and spill infections into neighbouring areas that otherwise would not sustain transmission. Breaking transmission in these hotspots would also halt the spread beyond the intervention area. However, studies have found hotspots not stable over time or not predictive for future infection. A trial did not yield evidence for incidence reduction outside the intervention area. Furthermore, hotspot detection locates clusters but does not generalize spatial dynamics in a given area. Conducting resource-intense surveys in all areas of operation does not seem feasible for NMPs.

	Our survey confirmed the disproportionate association of infection risk with time spent
	in the forest. However, residents of villages in the forest were at high risk across all ages and
	gender, and households from these villages formed hotspots of infections despite adjustment
	for forest-going behaviour. This suggests peri-domestic transmission and that targeting high-

The predominance of P. vivax infections affirms the roll-out of radical cure in combination with G6PD testing. Clinical incidence relies on the self-reporting of patients to health care facilities, VMWs, or MMWs in reach, proper diagnosis, and onward notification. The vast majority of asymptomatic infections, as found in field studies with active outreach and molecular parasite detection, means that most infections are missed by curative health care. Even pro-active testing by VMWs or MMWs has a limited effect when diagnosis by LM or RDTs is not sensitive enough to detect the sub-patent, low-parasitaemia infections, which nevertheless contribute to transmission. risk groups and passive case detection might not suffice for elimination. As the high prevalence calls for burden reduction and risk is high across the general population, the NMP may need to consider MDA or mass testing and treatment. The spatial signature method calculates the pooled prevalence of infections in widening areas (and potentially time windows) around other infections. The method detects clustering with statistical significance, summarizes the extent of clustering, generalizes spatial dynamics in infection risk across the observed area, and evaluates the radius around infections within which other infections occur at elevated likelihood. It revealed spatial clustering of household locations of PCR-positive individuals within close proximity across several field studies, notably in Brazil, Thailand, and Cambodia. The clustering tended to occur in narrower areas around infections at decreasing overall prevalence in the area. Infections in cohort studies appeared to cluster spatially and in time over a few months. These findings could support reactive interventions, such as reactive drug administration or reactive testing and treatment, and enable NMPs to make informed choices of operational distances.

  Leanne Robinson, Benoit Witkowski, Ivo Mueller. Retreat of ED393 Pierre Louis de Santé Publique : Epidémiologie et Sciences de l'Information Biomédicale, Saint-Malo, 21-23 Oct 2019.• "The spatial signature of malaria infection: A new measure to calibrate transmission models and inform trial design of targeted malaria control measures", Mirco Sandfort, Amélie Vantaux, Leanne Robinson, Benoit Witkowski, Ivo Mueller; Michael White. The

	Malaria Endgame, Keystone Symposium, Addis Ababa, Ethiopia, October 38-November
	2, 2019.

Table ST2

 ST2 Full list of covariates and their association with prevalence ("pos.") and risk of infection (as detected by PCR) for P. vivax and P. falciparum. Statistical significance of the covariate based on prevalence by χ 2 test (or Fisher's exact test if required) and based on univariate logistic regression with random intercepts for households and villages by likelihood ratio test (LRT). "n.s.": Not significant (p≥0.05).

						P. vivax P. vivax P. vivax P. vivax P. vivax P. vivax					P. falciparum P. falciparum P. falciparum P. falciparum P. falciparum P. falciparum
	Covariate Covariate Covariate Covariate Covariate Covariate		N N N N N N	pos. (n) pos. (n) pos. (n) pos. (n) pos. (n) pos. (n)	p (χ 2 ) p (χ 2 ) p (χ 2 ) p (χ 2 ) p (χ 2 ) p (χ 2 )	OR OR OR OR OR OR	95% CI 95% CI 95% CI 95% CI 95% CI 95% CI	p (LRT) p (LRT) p (LRT) p (LRT) p (LRT) p (LRT)	pos. (n) pos. (n) pos. (n) pos. (n) pos. (n) pos. (n)	p (χ 2 ) p (χ 2 ) p (χ 2 ) p (χ 2 ) p (χ 2 ) p (χ 2 )	OR OR OR OR OR OR	95% CI 95% CI 95% CI 95% CI 95% CI 95% CI	p (LRT) p (LRT) p (LRT) p (LRT) p (LRT) p (LRT)
	Water source …chicken Household head Work in the last Having slept	51-80 Surface/rain No Indoors	474 303 1088 4124	3.6% 9.9% 5.0% 6.2%		2.88 [1.46-5.70] 1.06 [0.65-1.74] Reference Reference		1.1% 3.6% 2.5% 2.9%		1.76 [0.52-5.93] 0.85 [0.39-1.84] Reference Reference
	Forest proximity had received information on two months in… …nearby forest outside last night	Outside forest water Well Yes No Outdoors	(17) 3.4% (30) 7.6% (54) <0.05 6.9% 6.2% (256) <0.01 76 15.8% 3060 2011 3111 4053	Reference Reference 1.12 [0.80-1.57] Reference 3.11 [1.54-6.30]	n.s. <0.01	(5) 1.4% (11) 3.7% (27) n.s. 3.2% 2.9% (118) <0.01 9.2%	Reference Reference 1.04 [0.63-1.73] Reference 2.97 [1.14-7.74]	n.s. <0.05
	Covariate Gender Age [years] of village …dogs malaria in the past 3 months via Sprays repellent Material of floor Material of walls Material of roof Toilet in house …pigs …TV …deep forest usually at bedtime Slept under net Household head attended school …cows …household visit …rubber last night plantations Antimalarials in Main income for households Household owns …buffaloes …ducks …cats Insecticides had been sprayed inside the house in the past year …cashew nut the last 2 months Work-unrelated travels overnight in the last month to… plantations Felt sick at …cassava field interview Felt feverish at …rice field interview Axillary No work temperature	N 2231 1969 1054 527 424 11.6% pos. (n) 3.6% (80) <0.001 p (χ 2 ) 9.5% (188) 1.5% (16) <0.001 7.0% (37) (49) 335 10.1% (34) 347 10.1% (35) 309 8.4% (26) 289 9.0% (26) 244 6.6% (16) 197 6.1% (12) (103) <0.001 625 7.2% <0.001 (152) 57 8.8% (214) 1585 4.9% (252) <0.05 (12) 147 10.9% 3668 6.7% (45) 515 23.3% (120) 3578 7.0% (249) <0.001 622 3.1% (19) 65 13.8% (9) <0.01 3802 6.5% (248) 217 4.6% (10) 116 0.9% (1) 81 3.7% (3) <0.001 79 12.7% (10) Corrugated iron 3301 Female Male 2-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 Forest fringe Tap No Yes No Inside forest Else Cement/tiles No wall/tent Wooden Iron sheets Cement/bricks Grass/leaves Tent 7.0% (232) Wood/cement/ tiles 739 3.1% (23) (5) Bottled water 1828 4.4% (81) No toilet 2383 8.2% (195) <0.001 (77) <0.01 Yes 2614 7.3% (191) No 3107 4.0% No 3784 6.9% (262) <0.001 (16) No 4055 5.7% (247) <0.05 Yes 524 4.0% Yes 415 1.4% (6) (231) <0.001 (21) Yes 145 25.5% No 320 10.0% Any type 1816 4.0% (73) No 1264 8.5% (107) <0.001 (123) <0.001 Yes 1092 13.3% (145) No 3451 5.0% No 3983 6.2% (248) n.s. (37) No 3904 6.8% (32) <0.01 Yes 3880 6.1% Yes 216 9.3% (20) (264) <0.001 (236) Yes 296 1.4% No 3978 6.0% Yes 2935 5.5% (161) Rubber plantation 95 1.1% (1) <0.05 Farming 3829 6.7% (256) Wood logging 73 9.6% (7) Driving 22 9.1% (2) Workmanship 50 2.0% (1) Selling/service 130 0.8% (1) (15) (172) <0.001 Yes 748 12.8% (96) No 3977 5.9% (234) <0.001 Yes 222 15.3% (34) No 3938 6.6% (259) <0.10 Yes 261 3.4% (9) No 3687 6.6% (244) n.s. Yes 512 4.7% (24) No 2935 7.3% (214) <0.001 (4) No 3053 5.2% (238) <0.001 Yes 211 14.2% Yes 1264 4.3% (54) None 3465 4.9% (171) <0.001 Field sites 90 3.3% (3) Forest sites 268 22.4% (60) A village 177 13.6% (24) A city 59 1.7% (1) Unspecified 123 7.3% (9) (158) <0.001 (30) Yes 1147 9.6% (110) No 4017 6.4% No 2115 5.6% (118) <0.05 (258) n.s. Yes 170 5.9% Yes 2085 7.2% (150) (10) No 3930 6.4% No 3872 6.1% (238) <0.05 (252) n.s. Yes 249 6.4% Yes 328 9.1% (30) (16) <37.5°C 3917 6.4% No 3186 7.9% (253) <0.001 Yes 1014 1.5% (15) (250) n.s. ≥37.5°C 273 5.5%	P. vivax OR Reference 3.45 [2.60-4.57] 95% CI Reference 4.41 [2.43-8.01] 8.16 [4.58-14.56] 8.73 [4.74-16.06] 9.11 [4.96-16.74] 6.73 [3.54-12.77] 6.51 [3.40-12.43] 4.61 [2.25-9.41] 4.73 [2.20-10.18] 2.43 [1.03-5.73] 1.46 [0.49-4.33] Reference 1.37 [0.77-2.44] Reference 9.23 [4.28-19.91] Reference 0.59 [0.35-0.97] 2.95 [1.29-6.76] Reference 1.04 [0.53-2.03] 0.27 [0.04-1.72] 1.54 [0.47-5.08] 3.93 [1.60-9.69] Reference 0.60 [0.38-0.96] 0.79 [0.57-1.10] Reference 1.25 [0.92-1.70] Reference Reference Reference 0.62 [0.38-1.01] 0.31 [0.14-0.70] 8.03 [4.83-13.35] <0.001 p (LRT) <0.001 <0.001 n.s. n.s. <0.001 <0.05 n.s. <0.01 n.s. n.s. <0.01 Reference 0.67 [0.47-0.94] <0.05 Reference 1.40 [0.98-1.99] n.s. Reference Reference Reference 0.46 [0.30-0.71] <0.01 0.76 [0.42-1.38] n.s. 0.60 [0.22-1.61] n.s. Reference 0.83 [0.62-1.10] n.s. 0.31 [0.05-2.05] Reference 1.85 [0.79-4.34] 2.23 [0.45-11.08] <0.05 0.56 [0.08-3.70] 0.15 [0.02-0.94] 1.45 [1.02-2.06] <0.05 Reference 1.90 [1.12-3.20] <0.05 Reference 0.80 [0.40-1.61] n.s. Reference 0.83 [0.49-1.39] n.s. Reference Reference 1.73 [1.10-2.73] <0.05 0.60 [0.43-0.84] <0.01 Reference 0.81 [0.26-2.51] 5.47 [3.73-8.01] <0.001 2.79 [1.68-4.61] 0.44 [0.07-2.85] 2.14 [1.03-4.42] 1.10 [0.82-1.48] n.s. Reference Reference 0.98 [0.51-1.91] n.s. 1.71 [1.28-2.28] <0.001 Reference Reference 0.88 [0.51-1.51] n.s. 0.75 [0.48-1.19] n.s. Reference Reference 0.24 [0.14-0.40] <0.001 0.72 [0.41-1.24] n.s.	pos. (n) 1.9% (42) <0.001 p (χ 2 ) 4.2% (83) 0.7% (7) <0.001 3.0% (16) 3.8% (16) 5.4% (18) 4.9% (17) 4.5% (14) 4.2% (12) 5.3% (13) 3.6% (7) (43) 3.5% <0.001 (74) 8.8% (98) 2.5% n.s. (7) (116) <0.05 6.1% 3.2% <0.001 (22) 11.7% (60) 3.2% (116) <0.05 1.4% (9) 9.2% (6) <0.01 3.1% (116) 1.4% (3) 0% (0) 1.2% (1) <0.01 2.5% (2) 3.4% (113) 1.2% (9) (5) 1.9% (35) 4.1% (97) <0.001 (39) 3.3% (86) 1.7% 3.2% (9) 2.4% (116) <0.10 1.7% (122) <0.01 0.7% (3) (9) (98) <0.001 18.6% 6.6% 1.5% (28) 4.2% (53) <0.01 (54) <0.001 6.5% (71) 2.2% 2.9% (27) 3.2% (21) <0.001 2.7% (114) <0.10 5.1% (11) (104) (124) <0.01 0.3% 2.3% 2.5% (72) 1.1% (1) n.s. 3.1% (120) 4.1% (3) 4.5% (1) 0% (0) 0% (0) (6) (77) <0.001 6.4% (48) 2.7% (109) <0.001 7.2% (16) 2.9% (115) n.s. 3.8% (10) 3.2% (118) <0.05 1.4% (7) 3.6% (1) 2.5% (91) <0.001 16.1% (106) <0.001 1.5% (19) 2.0% (70) <0.001 4.4% (4) 14.9% (40) 5.6% (10) 0% (0) 0.8% (1) (34) (77) <0.01 4.2% 2.8% (48) 2.9% (114) <0.05 6.5% (62) n.s. 3.0% (11) 2.7% (63) 2.7% (108) <0.001 6.8% (106) <0.01 5.8% (17) 2.7% (19) 3.7% 0.6% (19) (119) <0.001 (106) <0.001 7.0%	P. falciparum OR 95% CI Reference 2.69 [1.78-4.07] Reference 4.44 [1.74-11.34] 5.25 [2.04-13.48] 10.18 [4.00-25.88] 8.49 [3.30-21.81] 9.21 [3.48-24.33] 7.48 [2.76-20.26] 8.66 [3.20-23.45] 6.40 [2.07-19.80] 2.60 [1.00-6.75] 4.11 [1.09-15.48] n.s. p (LRT) <0.001 <0.001 Reference 1.42 [0.64-3.15] n.s. Reference <0.001 16.49 [6.96-39.05] Reference 0.64 [0.30-1.38] n.s. 5.45 [1.79-16.61] Reference 0.66 [0.19-2.26] <0.01 No cases 1.00 [0.12-8.30] n.s. 1.62 [0.29-8.87] Reference 0.46 [0.22-1.00] 0.80 [0.48-1.33] 1.02 [0.64-1.61] n.s. Reference Reference Reference 0.60 [0.28-1.27] n.s. 0.39 [0.11-1.32] n.s. 10.75 [5.81-19.89] <0.001 Reference Reference 0.56 [0.33-0.96] <0.05 1.53 [0.89-2.61] n.s. Reference Reference Reference 0.32 [0.18-0.56] <0.001 1.20 [0.54-2.64] n.s. 0.26 [0.03-2.14] n.s. Reference Reference 0.70 [0.45-1.07] n.s. 0.78 [0.09-6.52] Reference 1.56 [0.43-5.72] 1.96 [0.18-20.97] n.s. No cases No cases 1.61 [0.97-2.68] n.s. Reference 1.98 [0.94-4.16] n.s. Reference 1.98 [0.90-4.37] n.s. Reference 0.58 [0.24-1.40] n.s. Reference Reference 5.98 [3.52-10.15] <0.001 0.40 [0.23-0.71] <0.001 Reference 2.61 [0.82-8.36] 8.38 [4.96-14.18] <0.001 2.37 [1.08-5.17] No cases 0.50 [0.06-3.94] 0.90 [0.58-1.40] n.s. Reference Reference 2.47 [1.19-5.14] <0.05 1.61 [1.03-2.50] <0.05 Reference Reference 2.39 [1.31-4.38] <0.01 1.19 [0.65-2.19] n.s. Reference 0.23 [0.10-0.53] <0.001 Reference 2.40 [1.34-4.28] <0.01

Table ST3

 ST3 Fixed effects parameters of multivariate logistic regression model for Plasmodium spp. infection as detected by PCR, including a gender-age interaction term.

	on malaria
	via TV in the
	past 3
	months
	Insecticides	No	Reference
	had been	Yes	-0.47608	0.18622
	Covariate sprayed inside the (Intercept) house in the Gender past year Having slept Age [years] alone outside last night Having slept under a net last night Work-unrelated travels overnight in the last Age [years] in men (gender-age interaction) month to… Work in the last two months… …in the deep forest …in a cassava field	Female Male No 2-10 Yes 11-15 16-20 No 21-25 Yes 26-30 31-35 None 36-40 Field sites 41-45 46-50 Forest sites A village 51-80 A city 2-10 Unspecified 11-15 16-20 21-25 26-30 31-35 No Yes 36-40 41-45 No Yes	β -4.67888 Reference -0.47247 Reference 0.72020 Reference 1.06923 1.23856 Reference 0.60961 -0.01898 0.46972 0.91935 Reference 0.32925 -0.52234 0.81629 0.57800 0.68471 0.14994 -0.19417 -1.42758 0.12539 Reference 0.68542 1.31719 2.43349 2.63857 1.92763 Reference 0.95113 2.74284 1.48228 Reference 0.31620	Std. error 0.46687 0.46214 0.39234 0.40483 0.40158 0.24718 0.49079 0.50472 0.49992 0.48253 0.51226 0.52053 0.22425 0.27518 0.63309 1.06795 0.57775 0.41792 0.58371 0.58402 0.65292 0.66262 0.27823 0.66384 0.67905 0.70859 0.15641	<0.01 p <0.001 0.07 0.94 <0.05 <0.001 <0.05
		46-50	1.47956	0.81076
		51-80	1.92305	0.73188
	Forest	Outside	Reference
	proximity of village	forest Forest fringe 0.77026	0.39881	<0.001
		Inside forest 2.59209	0.36768
	Material of	Grass/leaves -0.03273	0.67613
	roof	Tent	1.35235	0.48477
		Corrugated	Reference
		iron			<0.01
		Wood	-0.70368	0.26758
		planks/
		cement/tiles
	Household owns a toilet	No Yes	Reference -0.35946	0.18914	0.06
	Household	No	Reference
	owns	Yes	0.82853	0.28977	<0.01
	buffaloes
	Household	No	Reference
	head had received	Yes	-1.02219	0.40875	<0.01
	information

Table ST4

 ST4 Details of spatial clusters identified by covariate-adjusted SaTScan analysis.

	Cluster Lat	Long	Radius # Pop	# Obs	# Exp	RR	p
	1	12.22155 106.9107 4.5 km 273	80	37	2.5	<0.001
	2	12.24500 106.8488 2.5 km 152	60	28	2.4	<0.001

Table ST5

 ST5 Portion of villages that form the spatial clusters in the SaTScan analysis.

	Village	Cluster # households	# households in cluster	% of households in cluster
	Beng-Gaty 1	42	37	88.1%
	Sraelvy	1	36	20	55.6%
	Ohtrone	1	23	19	82.6%
	Ohchra	2	39	39	100%

including Cambodia, China (Yunnan province), Laos, Myanmar, Thailand, and Vietnam

Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

Figure 1. Spatiotemporal localization of asymptomatic (AS, green), clinical (Cl, red), and asymptomatic with subsequent clinical (AS + CL, orange) Plasmodium falciparum infections within households in Dielmo village from 2013 to 2016. Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

Figure 2. Spatiotemporal localization of asymptomatic (AS, green), clinical (Cl, red), and asymptomatic with subsequent clinical (AS + CL, orange) Plasmodium falciparum infections within households in Ndiop village from 2013 to 2016. Downloaded from https://academic.oup.com/cid/article/73/12/2175/6157008 by Robert Koch-Institut user on 13 October 2022

Acknowledgements

Acknowledgements .................................................................................................... Abstract .........................................................................................................................

Acknowledgements

The authors express their gratitude to the study participants, village heads, VMWs, local health facilities, and officials for making this study possible. We also thank the interviewers, technicians, and other study staff for their support.

Acknowledgements

The authors express their gratitude to the participants and field teams of the original studies.

We also thank the reviewers for their availability and agreement to review this manuscript. Acknowledgments. The authors thank the population of Dielmo and Ndiop villages who volunteered to participate in the study without whose cooperation this study would not have been possible. The authors acknowledge the kind donation of the Plasmodium vivax DNA by Dr. Ambroise Ahouidi (Le Dantec Hospital of Dakar). The healthcare workers in Dielmo and Ndiop are duly acknowledged for their cooperation in conducting this study.

Financial support. The study was funded by Institut Pasteur de Dakar, Institut de Recherche pour le Developpement de Dakar, and by the European & Developing Countries Clinical Trials Partnership (EDCTP), which is part of the EDCTP2 programme supported by the European Union (fellowship number TMA2018SF-2468). The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP and other funders.

Potential conflicts of interest. The authors have declared that no competing interests exist.

for her administrative support and competence, and the rapporteurs and jury members for their time.

Funding This study is part of the International Centers of Excellence for Malaria Research program "Understanding, tracking and eliminating malaria transmission in the Asia-Pacific Region", funded by the National Institutes of Health, MD, US (grant 1U19AI129392-01) and received additional funding by NHMRC (Australia, GNT1092789). Some of the included studies were supported by the TransEPI consortium funded by the Bill & Melinda Gates Foundation, USA (www.gatesfoundation.org). IM is supported by an NHMRC Principal Research Fellowship (GNT1155075). MS is part of the PhD program of the doctoral school ED393 Pierre Louis de santé publique and was supported by the Sorbonne Université (contract n°2695/2017). MW is supported by the European Research Council (852373). HS is supported by the European

Availability of data and materials

The de-identified dataset analysed for this study is being made publicly available in ClinEpiDB repository, https ://cline pidb.org/ce/app/. In the meantime, it is available from the corresponding author on reasonable request.

Availability of data and materials

The observational studies from which data were used list their data availability statements in the original publications.

Research Council (804744). LJR is supported by an NHMRC Fellowship (Australia, GNT1161627). The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, and in writing the manuscript.

Annex

Annex Chapter 3 -Supplementary material to chapter 3

Supplementary information

Supplementary information accompanies this paper at https ://doi. org/10.1186/s1293 6-020-03482 -4.

Additional file 1. Additional tables ST1-ST5 and additional figures S1-S3.

Abbreviations ACT : Artemisinin-based combination therapy; AIC: Akaike Information Criterion; aOR: Adjusted odds ratio; G6PD: Glucose-6-phosphate dehydrogenase; GPS: Global positioning system; K + EDTA: Potassium ethylenediaminetetraacetic acid; LLIN: Long-lasting insecticidal net; LM: Light microscopy; MMW: Mobile malaria worker; N: Sample size; NMCP: National malaria control programme; PCR: Polymerase chain reaction; RDT: Rapid diagnostic test; VMW: Village malaria worker; WHO: World Health Organization.

Authors' contributions

AV, LR, BW, and IM conceived and designed the study and were overall responsible for the study. SK, AV, and BW implemented and managed the data collection, with support by SG, NK, and DL, and supervised the sample processing. SK conducted the data collection, supported by data management by MS and TO. TO, MS, SK, AV, LR, and IM designed the questionnaires. MW contributed to the statistical analysis. AP contributed the land cover analysis. MS analysed the data and wrote the first draft of the manuscript. AV, MW, TO, BW, LR, and IM contributed to data interpretation and writing. All authors read and approved the final manuscript.

Funding

This study is part of the International Centers of Excellence for Malaria Research programme "Understanding, tracking and eliminating malaria transmission in the Asia-Pacific Region", funded by the National Institutes of Health, MD, US (grant 1U19AI129392-01) and received additional funding by NHMRC (Australia, GNT1092789). IM is supported by an NHMRC Principal Research Fellowship (GNT1155075). LJR is supported by an NHMRC Career Development Fellowship (GNT1161627). MS is part of the PhD programme of the doctoral school ED393 Pierre Louis de santé publique and supported by the Sorbonne Université (contract n°2695/2017). AP is supported by the Pasteur Institute International Network PhD fellowship programme "Calmette & Yersin". The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, and in writing the manuscript.

Ethics approval and consent to participate

This study was approved by the National Ethics Committee for Health Research, Ministry of Health, Kingdom of Cambodia (reference 239NECHR) and by the Institut Pasteur Institutional Review Board (reference 2017-03). Written informed consent to participate in the study was obtained from all participants (or their parent or legal guardian).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The spatial signature of Plasmodium vivax and P. falciparum infections: Quantifying the clustering of infections in crosssectional surveys and cohort studies Mirco Sandfort 1,2,* , Wuelton Monteiro 3,4 , Marcus Lacerda 3,4,5 , Wang Nguitragool 6 , Jetsumon Sattabongkot 7 , Andreea Waltmann 8,9 , Henrik Salje 10 , Amélie Vantaux 11 , Benoit Witkowski 11 , Leanne J. Robinson 8,9,12 , Ivo Mueller 1,8,9 , Michael White

Abstract

Background: Over the last decades, enormous successes have been achieved in reducing malaria burden globally. In Latin America, South East Asia, and the Western Pacific, many countries now pursue the goal of malaria elimination by 2030. It is widely acknowledged that Plasmodium spp. infections cluster spatially so that interventions need to be spatially informed, e.g. spatially targeted reactive case detection strategies. We introduce the spatial signature method as a tool to quantify the distance around an index infection within which other infections significantly cluster.

Methods:

We considered data from cross-sectional surveys from Brazil, Thailand, Cambodia, and Solomon Islands, conducted between 2012 and 2018. Household locations were recorded by GPS and finger-prick blood samples from participants were tested for Plasmodium infection by PCR. We also included cohort studies from Brazil and Thailand with monthly sampling over a year from 2013 until 2014. We calculated the prevalence of PCR-confirmed infections at increasing distance around index infections (and growing time intervals in the cohort studies). Statistical significance was defined as prevalence outside of a 95%-quantile interval of a bootstrap null distribution after random reallocation of locations of infections.

Results: Prevalence of Plasmodium vivax and P. falciparum infections was elevated in close proximity around index infections and decreased with distance in most study sites, e.g. from 21.3% at 0 km to the global study prevalence of 6.4% for P. vivax in the Cambodian survey. In the cohort studies, the clustering decreased with longer time windows. The distance from index infections to a 50% reduction of prevalence ranged from 25 m to 3,175 m, tending to shorter distances at lower global study prevalence.

Conclusions:

The spatial signatures of P. vivax and P. falciparum infections demonstrate spatial clustering across a diverse set of study sites, quantifying the distance within which the clustering occurs. The method offers a novel tool in malaria epidemiology, potentially informing reactive

Declarations Ethics approval and consent to participate

The observational studies from which data were used list their ethics approval references in the original publications.

Consent for publication

The observational studies from which data were used state the consent for publication in the original manuscripts.

Competing interests

The authors declare that they have no competing interests.

List of posters

• "Spatial heterogeneity and determinants of residual malaria transmission risk in North-Eastern Cambodia", Mirco Sandfort, Thomas Obadia, Saorin Kim, Michael White, Amélie • "Pockets of high malaria infection prevalence in villages inside the forest and their microepidemiology: A cross-sectional survey in rural Cambodia", Mirco Sandfort, Amélie Vantaux, Saorin Kim, Thomas Obadia, Soazic Gardais, Nimol Khim, Michael White, Annex Chapter 5 -Supplementary material to chapter 5

List of figures

Supplementary Material

Detection and characterization of Plasmodium species by LM and qPCR

Microscopic examination

Thick and thin blood films were prepared, stained with 10% Giemsa for 25 minutes and screened for the presence of malaria parasites using LM with 100x oil immersion lens. The number of parasites per 200 white blood cells in thick film was recorded and parasite density was estimated by counting the number of leucocytes by field examined and by arbitrarily considering that 8,000 leucocytes were present in 1µl of blood. Two microscopists read all slides, and in case of discrepancy, a third microscopist examined the slides and the results were combined. A slide was considered positive after two concordant readings and at least, two hundred oil-immersion fields were examined before a slide was declared negative for Plasmodium.

Molecular detection and characterization of Plasmodium species

The detection of Plasmodium spp was carried out by qPCR following genomic DNA isolation of Plasmodium parasites using QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to manufacturer's instructions. Genomic DNA from blood samples of microscopically-confirmed P. falciparum, P. malariae, P. ovale and P. vivax infected patients served for positive controls in all amplifications; sterile water and gDNA from uninfected blood samples served for negative controls to ensure lack of contamination [20].

The qPCR-based detection and characterization of Plasmodium spp was based on two steps real time PCR as previously described [21,22]. Firstly, Plasmodium parasites were detected by a "screening real-time PCR" with genus-specific primers targeting the Plasmodium cytochrome b gene using the Eva Green dye (Solis Biodyne) followed by melt curve analysis of resulting amplicons from the Bio-Rad CFX96 real time thermal cycler as described previously [21]. Secondly, positive Plasmodium DNA samples were ten-fold diluted and analyzed for malaria species using a nested real-time PCR assays with genus-specific primers for P. falciparum, P. vivax, P. malariae, and P. ovale as described by Canier et al. [21]. The presence of P. knowlesi, the fifth human malaria parasite [23] was not investigated in this study.

Supplementary Figure S1

Supplementary Figure S2