Bouchet Pierre

Sipad

Jacquot Sophie
email: sophie.jacquot@univ-orleans.fr

Toinard Christian
email: christian.toinard@insa-cvl.fr

Towards a SaaS application providing an ethical support for human caregiving of people living at home

Keywords: Home caregiving, SaaS, ethics, computation, AI

With an aging population, we have to prepare for a growing number of people with a loss of autonomy. Many papers focus on digital solutions for assisting and monitoring those people. In contrast, solutions are missing for monitoring human caregivers. This paper suggests using digital solutions towards a support for ethical reasoning through data collected among various participants including the recipient, the caregivers, the family and may be the physicians. Our purpose is thus to ease the cooperation among the participants. We start from an existing SaaS application marketed in France called SIPAD Connect. This paper proposes several additions in order to better support cooperation. First, new data and indicators are suggested for the recipient. Second, the participants can give their opinions for those data and indicators. Those opinions can be computed in order to raise alerts. The proposed alerts include not only the situation of the recipient but also feedbacks aiming at improving the SaaS application. Thus, both the cooperation and the computation open discussions regarding the recipient, the efficiency of the care and the quality of the SaaS application.

I. INTRODUCTION

Europe will face a significant increase in the number of people facing a loss of autonomy, in proportion of total demography. People with a loss of autonomy generally prefer to stay at home or, as an alternative, seniors' residences. Aging in place have significant benefits regarding health care costs [START_REF] Loi | The cost effectiveness of aging in place: A literature review[END_REF], quality of life [START_REF] Vanleerberghe | The quality of life of older people aging in place: a literature review[END_REF] or maintaining social networks [START_REF] Wiles | The Meaning of "Aging in Place" to Older People[END_REF] to name a few. For this purpose, different approaches are available. First, the family provide some care. Second, automation and connected devices can assist the person. Third, help at home provide some assistance (e.g. for eating and cleaning), and if conditions are met, through social grants. Despite all these approaches can be mixed, our study takes place in the third situation where caregivers regularly come at home for different assistances. However, in such a context, the relatives and sometimes the social services or the physicians are also involved in the care. Note that caregivers can name either family caregivers, such as older people's kids, or professional caregivers.

Since dedicated private companies offer such caregiving in the context of a social grant, the social services but also the family need interactions with the recipient. Dedicated SaaS applications are proposed to help the connection between the different stakeholders. SIPAD developed such a Cloud software. The SIPAD SaaS application, called SIPAD Connect, has several advantages related to data that are collected by professional caregivers regarding the different tasks of care. We recall that a SaaS (Software as a Service) application is based entirely on the Internet. In other words, the application can be accessed by users from any device, provided an Internet connection. The application is based on databases and isn't installed directly on users' device. This paper describes the approach currently followed in the context of Pierre Bouchet's Ph.D. thesis. The general objective is to extend the SIPAD's SaaS application towards a service that supports an ethical discussion. In contrast with a complex ethical reasoning model [START_REF] Fondras | A reasoning approach towards Ethics-Based Medicine and care of disabled people[END_REF], lightweight services offer a flexible way of cooperation. Those services take full advantages of the participant's opinion in order to provide graphics and alerts through a dedicated computation science. Moreover, the participants also give an opinion regarding the proposed computations. Thus, the cooperation is extended in direction of a meta-evaluation of the computations. Dedicated alerts and notifications invite the participants to cooperate for analyzing the conflicting evaluations. In addition, alerts and notifications invite SIPAD to revise and update the model of data or the computations to cope with inconsistent results.

II. STATE OF THE ART

A. Current software products

Since the population of older people is expected to increase during the next decades, both in France and abroad, then the number of people facing a loss of autonomy will increase too. It is necessary to find solutions in order to allow people to stay at home as long as possible.

As previously said, various approaches are available to cope with a loss of autonomy. We can mention family care as well as Smart Home Monitoring Systems (SHMS) that covers all sensors-based solutions. SHMS technologies aim at monitoring people with equipment running algorithms based on Artificial Intelligence (AI). Sensors can take different forms to name a few : (i) in-home passive sensors based on infrared, (ii) body-worn sensors such as accelerometers, (iii) video monitoring coupled with image recognition or (iv) sound recognition.

All these solutions are widely studied in literature [START_REF] Alam | A Review of Smart Homes-Past, Present, and Future[END_REF]- [START_REF] Vandeweerd | HomeSense: Design of an ambient home health and wellness monitoring platform for older adults[END_REF]. Note that a new multidisciplinary domain has emerged called gerontechnology [START_REF] Micera | Gerontechnology[END_REF] which covers all technologies matching with gerontology. The aim of SHMS is to detect any abnormal situation and especially falls or behaviours as soon as possible in order to quickly intervene.

Our study proposes another approach that in some situation could be coupled with SHMS. We are setting in a context of home care with associated professionals. We found no thorough research as there is for SHMS regarding the use of AI and digital solutions, besides an application allowing people to order care services [START_REF] Lilianira | The Technology Processing of "4 Care" Home Care Application Based on Artificial Intelligence[END_REF].

B. Home care for the loss of autonomy

In order to deal with loss of autonomy at home, several measures can be taken [START_REF] Genet | Home care in Europe: a systematic literature review[END_REF]. One may need homecare support, which assists people on daily tasks such as laundry, shopping, cooking, personal care and so on. It is based on an assessment of recipient's care needs. Then it is possible for people to adapt their homes to their loss of autonomy, e.g., by buying grab bars, mobility aids and other technical assistance.

Another kind of home care service is telecare. It is quite similar to gerontechnology as telecare detects when a problem occurs and is connected to a call centre. Next, call centre will contact either emergency teams or relatives, depending on the gravity.

To date, digital technology is quite lacking for professional use, besides SHMS. Few propositions exist for a digital tool intended for professional caregivers in order to help them for their interventions. In addition, no application exists for collecting data from professional caregivers and supporting cooperation in direction of an ethical reasoning.

C. Ethics of the care for the loss of autonomy

Hood et al. have proposed a new vision of healthcare using new technologies in 2004 and this global vision was taken up various times since [START_REF] Hood | Systems Biology and New Technologies Enable Predictive and Preventative Medicine[END_REF]- [START_REF] Flores | P4 medicine: how systems medicine will transform the healthcare sector and society[END_REF]. They brought forth the notion of P4 medicine, i.e. a medicine that is:

1. Predictive, e.g. predicting falls [START_REF] Pilotto | Technology in geriatrics[END_REF].

Participatory. Allowing patients to participate

actively in the decision making.

3. Personalized. Data is collected and processed from one's everyday living.

4. Preventive. The system is preventive as measures can be taken to limit the predicted risk.

The participation of recipient to the decision making is a part of ethical reasoning as it is at the core of nursing practices [START_REF] Goethals | Nurses' ethical reasoning and behaviour: A literature review[END_REF]. Ethical reasoning is a global framework that has been studied and adapted for medicine [START_REF] Kerridge | Ethics and evidence based medicine[END_REF], [START_REF] Strech | Evidence-based ethics -What it should be and what it shouldn't[END_REF]. Yet, quite a few models of ethical reasoning has been developed for home care [START_REF] Dowd | Ethical Reasoning: A Basis for Nursing Care in the Home[END_REF]. A global approach was proposed in 2018 for the care of disabled people based on ethics [START_REF] Fondras | A reasoning approach towards Ethics-Based Medicine and care of disabled people[END_REF]. But these approaches were not turned towards digital. We propose a new approach for home care that conforms P4 medicine and deals with multiple existing ethical risks regarding the use of AI.

D. Discussion

Pilotto et al. have suggested in 2018 to use technologies not as disconnected tool to follow some specific tasks. They suggested to integrate technologies directly into the care system, aiming at a personalised approach [START_REF] Pilotto | Technology in geriatrics[END_REF]. As we will detail below, our approach is integrated into home care existing practices.

Rubeis has pointed out four ethical risks about gerontechnologies based on AI [START_REF] Rubeis | The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care[END_REF]. He suggested to name them "the four ds" to contrast with P4 medicine. The four ds stand for:

1 https://sipad.com/ 1. Discriminatory: risk related to stereotypes and categorizations liked used in AI frameworks.

2. Dehumanized: risk of disempowerment with tasks decided by AI in addition to a legal risk.

3. Disciplining: risk of a conformance to a "norm" set by AI.

4. Depersonalized: risk that an AI system will be considered more trustworthy than personal experiences.

Other ethical considerations, essentially consent, privacy and black box effect have been studied such as in [START_REF] Safdar | Ethical considerations in artificial intelligence[END_REF]- [START_REF] Murphy | Towards an ethical framework for the design and development of inclusive home-based smart technology for smart spaces for older adults and people with disabilities[END_REF]. However, those ethical considerations were focused on SHMS, even though they may apply to our context. Thus, the risks related to an AI deal with deontology and ethics. In contrast with a pure AI system, our approach is consistent with P4 medicine but provides a mean of ethical reasoning through the cooperation of the participants. The proposed decisional assistance is always evaluated with complementary human opinions. It also addresses conflicting objectives of care and ethics as well as assessments.

III. THE CURRENT SIPAD SAAS APPLICATION

A. The SaaS environment

SIPAD 1 is a French company which main objective is to interconnect all relevant stakeholders. It connects professionals of home help, telecare, medical equipment with the recipient and its relatives. In other words, SIPAD has a multidisciplinary approach of care. The target not only is older people but also disabled people of all ages. It addresses people living at home. The very essence of SIPAD is to encourage independent living at home with the support of professionals and family, as long as possible. SIPAD offers a digital logbook and others features as described further.

SIPAD Connect is a SaaS solution described in Figure 1. SIPAD provides access to a database for professionals, family and recipients. Note that data, according to L.1111-8 2 of the Public Health Code, is hosted as Health Data Hosting which is the governmental French label to guarantee the security of 2 https://www.legifrance.gouv.fr/codes/article_lc/LEGIARTI 000033862549/ (in French) health data. Professionals, family and recipients have different levels of access depending on their status. Currently, only professional caregivers can rate the recipient's health. The approach of SIPAD is complementary to SHMS.

B. The available Data

SIPAD Connect is collecting several different types of data which are used by home care professionals to monitor recipient's health and to organize their interventions. The recipient and his family can access those data.

The first collected data are related to the tasks performed by caregivers, both professional and family. Those tasks are gathered in the so-called "logbook". This allows both information sharing between stakeholders and actions history. A total of 149 tasks is available. Each one of them is labelled as one of the following 15 categories: Another type of data is related to the recipient health. Each professional caregiver provides a quantitative multidimensional evaluation that is undertaken at each intervention. It is not a significant workload as the professional caregivers already have to assess it on paper. So, this is basically a digitalization of current practices. The evaluation is related to 7 items:

• Sleep • Appetite • Mood • Coherence • Serenity • Autonomy • Hydration
Each of these items are evaluated on a 4 points Likert scale. To be more intuitive, the possible answers are represented as weather icons: storm, rain, veiled sun, sun. We consider that sun is better than veiled sun which is better than rain which is better than storm. An even number of levels has been chosen to prevent users from choosing a central measure, as it would be similar to a neutral value. In addition, we consider that 4 levels is a decent compromise between sensitivity and reliability. Indeed, as the number of levels increase, the sensitivity will increase too, so we do not want to have many levels. Yet, if we decrease the number of levels then we decrease the reliability. Therefore, we have to choose a number of levels that is neither too high nor too low. This dilemma is similar to the well-known "Bias-variance tradeoff" in machine learning.

The 7 items enable professionals to monitor the recipient's health in order to prevent any dramatic decrease that could lead to event such as falls or hospitalizations. For convenience, we shall use henceforth a short term for these data, namely "weather".

Others features such as spotting needs and secured messaging service are available. These functionalities allow stakeholders, especially professionals, to exchange on any particular event. For instance, if the recipient needs a medical appointment or is absent for some reasons, then it is possible to prevent all the other stakeholders via the application.

All these data can be exploited through miscellaneous machine learning algorithms. For example, changepoint detection models are proposed for the weather data. It notifies the caregivers about the dramatic fall of an item, allowing both professionals and family to quickly react. Note that changepoint detection models also are used in SHMS [START_REF] Sprint | Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection[END_REF] despite home care is not considered.

C. The current limitations

To date, current limitations are multiple. First, there is no way for recipients to express their opinions directly in the application, especially disagreements. For example, if a recipient doesn't agree with the rating of a professional caregiver about sleep, then he cannot express his disapproval. In our example, the only way to do so for the recipient is to interact directly with the caregiver. That is why an a posteriori rating is needed, which will allow the recipient to express his opinion at any time.

Another limitation is that there is no way for the professional to give his opinion about the data quality. Indeed, a professional caregiver could be sceptical when he assesses one of the 7 items.

Finally, the current indicators are not put in perspective with the context of the recipient. It is known that the context has a direct impact on health [START_REF]The role of Human Factors in Home Health Care[END_REF]. For instance, consider an older people with no proper material to move around in home. If there is no information collected about the fact that he cannot move around freely, how to interpret an alert about his low moving activities? This alert makes sense only against the context. So, an alert has a particular significance regarding the global situation of the recipient.

IV. TOWARDS A SET OF TOOLS SUPPORTING ETHICAL REASONING

A. The general approach

In order to support an ethical reasoning several features have to be added. All the features must be directed towards an ethical processing and must provide a support for an ethical discussion. Indeed, we can distinguish an ethical processing, i.e. is the way the application collects and processes the data from an ethical discussion that is at the heart of nursing practices. To reach the goal of an ethical processing that cope with the considered ethical risks, we should keep in mind that:

1. All indicators must be explained to stakeholders.

Given that everyone has different understanding ability, it is essential to build a kind of manual explaining the computations. This can take the form of a switch where people disable graphics or indicators when they don't understand it. We aim at an inclusive tool, based on each participant's ability. Note that explaining computations lower the risk of a discrimination.

2.

No automatic decision has to be taken by an AI or the digital system. Every decision is associated with humans' consideration. The proposed approach is a decision aid that collects, centralizes and processes data. This is a way of fighting against the risk of dehumanisation and depersonalisation.

The General Data Protection Regulation is a regulation on data protection in the European

Union. In this context, law and ethics require to inform recipients about which data is collected and how the data is treated. Moreover, law and ethics require to collect the recipient's consent to provide an access for the family caregivers and to collect sensitive data.

B. New data and indicators

First, new data and indicators are proposed for the items. In additions to new assesses, opinion data and alerts are available. Figure 2 shows an example of dashboard for the sleep item. R Shiny 3 was used to develop it. In addition to the professional assess, the recipient and the family can give an assess for the sleep too. The opinion data enables each participant to give a rating for his own assess. The idea here is to avoid dehumanisation, and also disciplining. Indeed, a participant can express that he does not trust his assess or the way assesses are collected. Finally, alerts of conflict propose new indicators for detecting different kinds of inconsistencies.

As shown in Figure 2, the recipient can rate his own sleep on the same scale used by the professional caregiver. Thus, a disagreement can be observed. Participants also rate their confidence in their own assessment through a binary assess using in this example thumbs up and thumbs down 3 Winston Chang, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert and Barbara system. Confidence allows them to take some distance with the data. For example, the professional caregiver can be sceptical about his sleep rate for various reasons. All these elements then generate alerts when conflicts occur. Thus, advanced information sharing is available such as required to avoid redundancies in acts and to help discussion about sensitive care issues [START_REF] Yamashita | How Information Sharing about Care Recipients by Family Caregivers Impacts Family Communication[END_REF]. Indeed, the idea is not to track professionals but to propose a way for all stakeholders to provide better evidences for the value of data.

Second, new data take the context of a recipient into account including an "environmental context" and a "social context". The first one includes all elements regarding the home and its nearby: proximity to services like pharmacy or supermarkets, details about home such as the presence of medical equipment or the accessibility. The second kind of context includes details about cognitive abilities, family life, finances, or life style. From the context, we can then compute indicators such as a service density which could be the number of services in a given radius around recipient's home.

Third, cooperation data are proposed. The first level of cooperation is the ability for participants to give their ethical or care objectives. For example, a care objective of a recipient might be to eat with the caregiver. An ethical objective for the recipient might be to respect his will. A care objective for the caregivers might be to prepare the meal. An ethical objective for the caregivers could be to adjust to the disability of self-determination. Those two ethical objectives can be conflicting.

The ability of rating for recipients can be done on two levels. The first level is about rating the fulfilment of predefined objectives, i.e. rating if they consider that their objectives have been fulfilled. The second level is about trust about these data. In the same spirit, family and professional caregivers are allowed to rate objectives' fulfilment as well as their confidence with the data they enter. For the sake of brevity, this paper does not give a dashboard for the context and cooperation.

C. Computation of alerts 1) Alerts of conflicts between caregiver and recipient

The first kind of alert is between caregiver and recipient. After giving their opinions, the tool computes the level of disagreement between the recipient and his professional caregiver. This is obviously computed for each recipient with his own caregivers. It would be a nonsense to compare caregiver's agreement with recipient's one when they are not linked.

The discrepancy measure depends on the rating scales. If both agreements are on the same quantitative scale, then a traditional Euclidean distance or mean squared error are possible, among others. However, if they are free messages, then Natural Language Processing (NLP) is involved to extract one's opinion before computing discrepancies.

In our example shown in Figure 2, a caregiver rates sleep as "sun" while the recipient rates it as "rain". In this example the disagreement is quantified through stars as a normalized distance. Since professional and recipient provide two levels of difference, two full stars are displayed while one is empty. Moreover, this alert can open a discussion for analyzing the reasons of conflict in order to have a holistic approach.

2) Alerts of inconsistency between caregivers

Global statistics can be computed regarding caregivers' rates. We recall that we don't aim at rating professionals' habits nor to track them. Instead, we aim at creating discussions when anomalies occur.

The idea is to put in common all the evaluations from the caregivers, whatever the recipient is, to detect abnormal rates. The abnormality can be judged by the quantiles and other distribution's measures in a similar way to the philosophy of the p-value and type I error in statistical tests. In statistical tests, a 5% level is commonly considered significant because a p-value lower or equal has only a probability of 5% to occur. Note that the threshold is not fixed and can be different from 5%, depending on the acceptable risk one is ready to take on type I error.

Let's take an example related to home care. Consider a caregiver rating today's sleep as "sun" while only 2% of all caregivers rate "sun" for this recipient. In this case, rating sleep as "sun" has a probability of 2% to occur and yet it did. Given that 2% may be seen as a very low probability, there is a reason to notify about a possible inconsistency of caregiver's measure or at least a necessity to investigate. In our example displayed in Figure 2, 30% of all professional rates for this recipient's sleep are "sun". Given that today's measure is also "sun" it cannot be considered as inconsistent because it is not likely to be abnormal, unless 30% is considered by stakeholders to be an acceptable compromise for type I error.

3) Alerts of obsolescence of a component

This kind of alert is intended to warn stakeholders when a measure is inefficient or obsolete. The measure in our example in Figure 2 is sleep. Using the same logic that for inconsistency between caregivers, we study the distribution and other statistics.

If statistics show the stakeholders that the measure is mostly not informative or even missing, then the system shall send an alert to them. This is a logics of a continuous improvement process where tools are able to criticize themselves. It is possible to associate it with the opinions of the participants about the measure.

For our sleep example in Figure 2, the system computes an alert for that component. Family and professional caregivers have declared to not be confident about their sleep's rate of the day due to a high level of uncertainty. The fact that they are not confident is provided by the thumbs down system in the second tab in Figure 2.

In the third tab, two stars over three are filled. It is because two participants over three are not confident about their own assesses. The more stars are filled, the more today's measure is considered to be obsolete. The high level of uncertainty in today's sleep rate may either come from a misunderstanding of how to rate the recipient's sleep or even from a wish to not rate the sleep. This kind of alert leads next to a discussion between all stakeholders that will conduct to not rate the sleep anymore.

4) Discussion facilities

Discussion's tools already are existing in SIPAD Connect. Thus, the participants can discuss through a messaging system in accordance with the philosophy of a SaaS application. However, a deliberation tool can be added to support different kinds a reasoning like casuistic (study of similar cases), scenarios (with a vote system among at least 3 possibilities), evidential reasoning (evaluate multiple conflicting criteria) among other things. The idea is to have a pragmatic approach where all kind of thinking and reasoning can be supported. Another advantage of a deliberation tool is that it allows to have an history of decisions. It may be requested to justify the choices made afterwards or to compare with similar deliberations.

For instance, an alert opens an online deliberation about the disagreement between the recipient and the caregiver views about sleep. During this discussion, the caregiver obtains some medical information that explains why the recipient is used to sleep bad. In this case, the participants decide to not rate anymore the sleep due to this specific context.

V. DISCUSSION AND CONCLUSION

The growing number of older people and its associated loss of autonomy represents a challenge for the next decades. Many technologies have been developed based on sensors through the Smart Home Monitoring System but only a few tackle home care and its associated professionals. This paper proposes a new approach supporting ethical reasoning. This is an ongoing work where various computational statistics must be experimented on real data. It aims at collecting data related to home care that does not add excessive workload. Presented suggestions tend to have a P4 tool based on the definition proposed by Flores et al., i.e. (i) predictive, (ii) participatory, (iii) personalized and (iv) preventive while taking into account the "four ds" ethical risks [START_REF] Flores | P4 medicine: how systems medicine will transform the healthcare sector and society[END_REF], [START_REF] Rubeis | The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care[END_REF]. Furthermore, ethical statements such as transparent modelling and decision making are supported through a human control. Our purpose is to facilitate cooperation, discussion and exchange of information. The tool does not intend to unilaterally take decisions nor to say the truth. Instead, it takes data and computes indicators to help the participants in their discussions.

One risk of our approach is related to alerts. The main pitfall with the proposed alerts is to find an acceptable compromise. If the false positive rate is too high, a consequence is that people will be less likely to believe in future alarms. Which makes them useless on the long term. So, there is a dilemma of communicating vessels between too many false positive alarms and missing a real problem. Moreover, one should keep in mind that the choice of a discrepancy for computing alerts and distances is highly linked to a point of view. Depending on the choice, the measure highlights only a piece of information and seldom captures the whole underlying dynamic.

Another limitation with proposed alerts is that they are based on global behaviours to determine the cut-off between normality and abnormality for personal applications. The generalisation is the major trade off with AI in our context [START_REF] Safdar | Ethical considerations in artificial intelligence[END_REF]. That is why we suggest only using human-centred decisions.

Finally, our approach faces a problem of complexity in decision making with conflicting views and ethical objectives. The idea is to let the method of deliberation opened. Maybe in some situation an external method of deliberation can be used like a physical meeting. In such a case, the decision can be simply included in the system through a dedicated comment.

Figure 1 :

 1 Figure 1 : SIPAD functioning in a SaaS environment

Figure 2 :

 2 Figure 2 : example of dashboard and alerts using Shiny

Borges (2021). shiny: Web Application Framework for R. Rpackage version 1.7.1. https://CRAN.R-project.org/package=shiny


Amandine Lurette

Suzanne Touzeau

Pauline Ezanno

Thierry Hoch

Henri H Seegers

Christine Fourichon

Catherine C Belloc

† T Hoch

† H Seegers

† C Fourichon

Within-herd biosecurity and Salmonella seroprevalence in slaughter pigs: A simulation study

Keywords: batch-management system, farrow-to-finish herd, hygiene, modeling, Salmonella, swine

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Pork, after eggs and poultry meat, is a major source of human foodborne salmonellosis in the European Union [START_REF] Efsa | Opinion of the scientific panel on biological hazards on "risk assessment and mitigation options of Salmonella in pig production[END_REF]. Salmonella are ubiquitous bacteria that can enter and hence need to be controlled at each stage of the pork supply chain. When infected by Salmonella ingestion, pigs are asymptomatic carriers and shedders. Although they do not exhibit clinical signs of infection, they can intermittently shed bacteria in their environment. At the preslaughter level, reducing the number of shedding pigs at departure for slaughter limits the possible cross-contamination during transport and lairage. It can then also imply a reduction in the prob-ability of further contamination during the slaughter process [START_REF] Berends | Identification and quantification of risk factors in animal management and transport regarding Salmonella spp. in pigs[END_REF][START_REF] Lo Fo Wong | Herd-level risk factors for subclinical Salmonella infection in European finishing pig herds[END_REF]. Particular on-farm biosecurity measures have been recommended by the European Food Safety Authority [START_REF] Efsa | Opinion of the scientific panel on biological hazards on "risk assessment and mitigation options of Salmonella in pig production[END_REF]. The EFSA combines a strict all-in/allout batch-management system and a room decontamination process between 2 consecutive batches. However, these measures are difficult to fully implement under common farming conditions. As a consequence, the efficacy of decontamination process may vary in the field and depending on batch-management adaptations. Actually, mixing pigs from different batches creates direct contacts between animals from these batches, which can increase the risk of Salmonella transmission.

In terms of feasibility and costs, the effect of biosecurity-measure implementation on Salmonella spread within the pig herd can be tested advantageously by a modeling approach (van der [START_REF] Van Der Gaag | Cost-effectiveness of controlling Salmonella in the pork chain[END_REF]. A model describing the Salmonella spread within a farrow-to-finish pig herd (Lurette et al., 2008b) was previously developed. This model was used here to assess the impact on Salmonella prevalence in groups of slaughter pigs, of discrepancies between 1) a reference scenario for biosecurity-measure [i.e., close to the EFSA recommendations [START_REF] Efsa | Opinion of the scientific panel on biological hazards on "risk assessment and mitigation options of Salmonella in pig production[END_REF]] and 2) common practices of producers.

MATERIALS AND METHODS

Animal Care and Use Committee approval was not obtained for this study because only published data were used.

A model was developed to describe the dynamics and management of a farrow-to-finish pig herd (Lurette et al., 2008a). This model was then coupled to a Salmonella epidemiological model (Lurette et al., 2008b). The management system and assumptions that were implemented in the coupled model correspond to the reference scenario defined as the best one in terms of onfarm biosecurity measures. We first described the main features of the basic model, then how it was modified to implement the deteriorated scenarios and to test the impact of infection at weaning. Finally, we presented model outputs and the sensitivity analysis method used to compare the scenarios.

Basic Model Features Corresponding to the Reference Scenario

The basic model consists of 2 components: the population dynamics within a farrow-to-finish pig herd (reproductive sows and growing pigs) detailed in Lurette et al. (2008a), and the Salmonella transmission dynamics detailed in Lurette et al. (2008b). The components were easily coupled because the simulated infection is subclinical and therefore does not affect pig demography and growth rate. The main features are given below. The time step was a week and the modeling unit a batch.

Sow and Pig Population Model.

To represent the population of sows and pigs, animals were grouped in batches according to age (pigs) or reproductive status (sows). At each time step, a batch was associated to a room. The population dynamics component described the reproductive cycle of sows, including gilt recruitment, birth and the growth of pigs until slaughterhouse delivery, batch movements between rearing rooms, and animal outflows from the batch (mortality, culling, and slaughterhouse delivery). Sows in a batch were mated simultaneously. A 3-wk, between-mating interval was modeled. A batch of sows gave birth to a batch of piglets. A strict all-in/all-out batch-management system was implemented for pigs (with no pig mixing), so they remained in their batch until their slaughterhouse delivery. Each batch of sows occupied 3 successive rooms during each reproduction cycle: a mating room (4 wk), a gestating room (12 wk), and a farrowing room (5 wk).

In turn, each batch of pigs occupied 3 rooms: a farrowing room (4-wk suckling period), a postweaning room (8 wk), and a finishing room (around 14 wk). To account for pig growth variations, the time pigs remained in the finishing room varied within a batch and among batches. On average, pigs spent 25.5 wk (SD = 1.5 wk) in the herd to reach slaughter weight. This duration corresponded to the mean age at slaughter. For more details on the herd dynamics, see Lurette et al. (2008a).

Salmonella Transmission Model. To represent Salmonella transmission, animals in each batch were distributed into 4 exclusive and successive health states: susceptible, seronegative shedder, seropositive shedder, and seropositive carrier (no longer shedding). Shedding was intermittent and could be reactivated, so carriers could go back to the seropositive shedder state. For more details, see Figure 1 in Lurette et al. (2008b). The model implemented indirect transmission of Salmonella via the environment. Shedding pigs contaminated their rearing room, thereby exposing susceptible pigs of their own batch to the bacteria. A drop of 20% in the shedding quantity was assumed to occur after seroconversion, so seropositive shedders contribute less than seronegative shedders to the room contamination. The probability of infection within a batch depended on the quantity of bacteria per pig in the room. This quantity was updated at each time step by taking into account shedding, natural bacterial degradation, and room decontamination.

The decontamination process was performed after a batch left the room. The farrowing, postweaning, and finishing rooms were emptied between 2 consecutive batches, so the decontamination process was very efficient and reduced the quantity of bacteria by 99%. In contrast, the mating and gestating rooms were each occupied by several batches of sows at any time. As a Figure 1. Correlations (P < 0.0001) between the prevalence of shedding in batches at weaning and at the end of the finishing period under the reference scenario Mix0-Growth1-Decont99.9, which is described by no mixing of pigs, a mean age at slaughter of 25 wk, and a decontamination efficacy of 99.9%. Three hundred replications over a 400-wk simulation period were considered, corresponding to 60,000 groups of slaughter pigs.

consequence, these rooms could not be emptied and the decontamination efficacy was reduced there to 80%. For more details on the epidemiological model, see Lurette et al. (2008b).

Calibration and Initial Condition

Epidemiological variables used in the model are detailed in Table 1. Values of variables were chosen based on Salmonella Typhimurium because this serovar is the most frequently detected in European pig herds [START_REF] Efsa | Opinion of the scientific panel on biological hazards on "risk assessment and mitigation options of Salmonella in pig production[END_REF]. See Lurette et al. (2008a) for zootechnical variables.

The initial herd consisted of 7 batches of 20 sows and 10 batches of pigs. The size of a batch of pigs decreases from 180 pigs at birth to 30 pigs at the end of the finishing period (some pigs having already been delivered to the slaughterhouse). An annual replacement rate of 40% was obtained in the model, which took into account the recruitment of 4 gilts at each new reproductive cycle. At each gilt recruitment, 7% of the gilts were seropositive (shedding or not). The model was initialized with prevalences similar to the results obtained in Lurette et al. (2008b): a mean shedding prevalence of 21% in batches of sows and of 13% in groups of slaughter pigs.

Modifications of the Model and Biosecurity Scenarios Tested

The reference scenario and biosecurity-measures commonly implemented were compared by constructing 54 scenarios combining different levels of the fol-lowing 3 factors (see Table 2): batch-management system corresponding here only to the possible mixing of pigs from consecutive batches (Mix), mean age at slaughter (Growth), and decontamination efficacy in the rearing rooms (Decont). The reference scenario (Mix0-Growth1-Decont99.9) was the strictest scenario in terms of biosecurity obtained under field conditions.

Introduction of Pig Mixing. The model was modified to include the possibility of mixing pigs from different batches at the end of the postweaning period and at the end of the finishing period. Pig mixing was the only variable component of the batch-management system in the model. From here on, this practice will be called pig mixing in the text. At the end of the finishing period, if some pigs have not reached the market weight when their room needed emptying, producers may mix them with the next batch (3 wk younger) to avoid profit losses. Similarly, underweight piglets may be mixed with the next batch at the end of the postweaning period. However, pig mixing was limited by room capacity: 180 animals in a postweaning room and 150 in a finishing room. Three types of batch-management systems were then modeled: 1) a batch-management system with strict all-in/all-out without mixing (corresponding to the reference scenario and called Mix0), 2) a batch-management system with a strict all-in/all-out occupation of farm facilities, except at the end of the finishing period when pig mixing may occur (Mix1), and 3) a batch-management system where pigs may be mixed at the end of both the postweaning and the finishing periods (Mix2).

Mean Age at Slaughter. As end BW was fixed, increasing this variable allowed us to simulate a decrease The values chosen for variables used in the epidemiological model are estimated based on experimental data reported in the literature. in pig growth. Its reference value was 25 wk (Growth1). We tested mean ages of 27.5 wk (Growth2) and 30 wk (Growth3), keeping the same confidence interval as before (i.e., 1.5 wk).

Reduction of the Decontamination Efficacy. The reference value for the decontamination efficacy (Decont99.9: 99.99%) corresponded to an optimum under field conditions. Four other values were tested in rooms that can be emptied between consecutives batches (farrowing, postweaning, and finishing rooms), to represent the large range of variation observed under actual herd conditions: 99% (Decont99), 90% (De-cont90), 80% (Decont80), and 50% (Decont50). Although unrealistic, an extra 100% efficacy value (Decont100) was added to simulate extreme scenarios with no contamination between batches through the rearing rooms. The decontamination efficacy in mating and gestating rooms remained equal to 80%, except for scenario Decont50 in which it was reduced to 50%, as in the other rooms.

Modifications of Herd Supplying

In Lurette et al. (2008b), the mean shedding prevalence within batches of weaned pigs was 2.9% (5th percentile = 0; 95th percentile = 9.5%) with 68% of Salbatches in 68% of positive batches. A 0.507 correlation coefficient was observed between the prevalence of shedding at the end of the weaning and of the finishing periods under the reference scenario (Figure 1). Considering this result, to explore the impact of Salmonella infection in batches of weaned pigs on the prevalence at slaughterhouse delivery, we introduced in the herd batches of negative weaned piglets 100 wk after the beginning of the simulation for the 5 values of decontamination efficacy.

Simulation Outputs

Every 2 wk, pigs that had reached market weight or pigs with finishing rooms that needed to be emptied were delivered to the slaughterhouse. The pigs usually stemmed from several batches and constituted a group of slaughter pigs. First, the prevalence of shedding (i.e., pigs in the seronegative shedder and seropositive shedder states) in each group of slaughter pigs was calculated. Two outputs were derived from this variable: the mean prevalence and the cumulative frequency distribution (Figure 1A).

Second, pig mixing was studied in more detail by calculating, for scenarios that allowed pig mixing at the end of both the postweaning and the finishing periods (Mix2), 1) the frequency of batch in which pig mixing occurred (i.e., the number of batches receiving pigs from another batch divided by the total number of batches delivered to slaughter during the 520-wk simulation and for all repetitions), 2) the global mean number of pigs mixed per batch when mixing occurred, and 3) the prevalence of shedding pigs in group of mixed pigs (i.e., the number of shedding pigs on the total number of pigs mixed over all delivery time steps and all replications). The mixing frequencies were calculated independently at these 2 periods.

Results were obtained from 300 replications of each scenario tested over 400 wk. This number of replications provided a stable distribution of the simulated results. So, for each scenario, our results were based on 60,000 groups of slaughter pigs. Analysis of variance, followed by a Tukey test, was implemented to compare the results of shedding prevalence obtained from the different scenarios tested with the R software.

Sensitivity Analysis

To compare the contributions of pig mixing, mean age at slaughter, and decontamination efficacy with the frequency distribution of the prevalence of shedding, a sensitivity analysis method suitable for multivariate outputs was used [START_REF] Lamboni | Multivariate global sensitivity analysis title for dynamic crop models[END_REF]. This method is based on principal components analysis (PCA) and ANOVA. The cumulative distribution of prevalence of shedding in groups of slaughter pigs over time can be represented in a "No. × p" table, where No. was the number of scenarios, which was 54, and p was the number of 5% intervals from 0 to 100% of prevalence, which was 20. A PCA was performed on this table. The first component obtained was the linear combination of the 5% intervals that explained the maximum variabil- The factor modalities are combined to define the 54 simulation scenarios tested in this study. The reference scenario corresponds to Mix0-Growth1-Decont99.9.

ity between the scenarios. The second component was the second-best combination to explain the variability. Each scenario was given a score for each component, corresponding to its projection on the component. An ANOVA was then performed on the scores for each of the first 2 principal components to compare the influence of the 3 factors on the prevalence output. The regression model included all main effects and interactions. The sensitivity index associated with each factorial term (main effect or interaction) was defined as the ratio between the sum of squares associated with that term and the total sum of squares. It represented the impact of that term on the output variability. This analysis was performed with the R software (The R Project for Statistical Computing: http://www.r-project.org/).

RESULTS

Qualitative Comparison of the Biosecurity Scenarios

For the reference scenario (Mix0-Growth1-De-cont99.9), the mean prevalence of shedding in groups of slaughter pigs was 15.2%, the values ranging from 0 to 52% among groups (Figure 2A). Fifty percent of the groups of slaughter pigs exhibited a prevalence of shedding less than or equal to 9%.

The distribution of prevalence of shedding in groups of slaughter pigs for the 54 scenarios tested was highly variable. However, scenarios differing only by pig mixing exhibited similar patterns (results not shown), so we only illustrated the results obtained without pig mixing (Mix0) in Figure 2.

The greater the curve corresponding to a scenario, the less the mean prevalence of shedding in the groups of slaughter pigs. Clearly, the greatest curve was the one obtained for the scenario Mix0-Growth1-Decont100, but this curve was very close to the one obtained for the reference scenario Mix0-Growth1-Decont99.9. Results of cumulative frequency exhibited very similar patterns for decontamination efficacies equal to 100, 99.9, and 99%.

The lowest curve corresponded to the worst-case scenario Mix0-Growth3-Decont50, in which the prevalence was greater than 32% in 50% of the groups of slaughter pigs. Scenarios with Growth2 and Growth3 exhibited large numbers of slaughter groups with low prevalence and high prevalence, whereas all scenarios with Growth1 but the Decont50 exhibited a regular increase of the curve from 0% prevalence to 40% prevalence.

Contribution of Pig Mixing, Mean Age at Slaughter, and Decontamination Efficacy to the Distribution of the Prevalence of Shedding

The first component of the PCA explained 81.5% of the prevalence distribution variability. All scenarios con-tributed equally to this component (results not shown). The decontamination efficacy contributed 69.8% of the output variability for this component (Figure 3A). The mean age at slaughter contributed 15.1%, and the pig mixing contributed 0.9%. Interactions explained very little of the output variability.

The second component of the PCA explained 17.2% of the output variability. The scenarios that contributed most to this component included many extreme groups of pigs at slaughter, with either low or high prevalences of shedding (results not shown). These scenarios corresponded to the curves in Figure 2 that differed most from the median curve. The mean age at slaughter, decontamination efficacy, and corresponding interaction explained more than 95% of the output variability for this component (Figure 3B).

Effect of Biosecurity Measures on the Mean Prevalence of Shedding

Pig Mixing. The contribution of pig mixing to the mean prevalence of shedding in groups of slaughter pigs was small compared with the other factors. Figure 4 illustrates the effect of changes in pig mixing on the mean prevalence in groups of slaughter pigs. The difference in mean prevalence (P > 0.05) for scenarios combining the same values for mean age at slaughter and decontamination efficacy but differing in terms of pig mixing was smaller than 3 points (Figure 4). The difference between the batch-management systems Mix1 and Mix2 was smaller than the difference between Mix0 and Mix1. This suggests that pig mixing has less influence at the end of the postweaning period than at the end of the finishing period. Focusing on pig mixing at the end of the finishing period, the frequency of batches mixed with another batch varied between 63 and 73% for scenarios with batch-management Mix2, whatever the mean age at slaughter (Table 3). The global mean number of pigs mixed per batch concerned varied from 16.9 to 53.2 pigs when the mean age at slaughter increased from 25 to 30 wk. The scenarios tested showed little variation in the mean prevalence of shedding in the groups of mixed pigs (around 20 to 30% for Growth1 to Growth3).

Mean Age at Slaughter. For a strict all-in/all-out batch-management system without pig mixing (Mix0; Figure 4A), an increase in the mean age at slaughter (Growth1 to Growth3) induced an increase in the mean prevalence of shedding. The increase was much greater between 27.5 (Growth2) and 30 wk (Growth3) than between 25 (Growth1) and 27.5 wk (Growth2; 4 to 5 points vs. 1 to 2 points) except for the least decontamination efficacy for which shedding prevalences were very close (see on Figure 4 for significant differences; P < 0.05).

Decontamination Efficacy. For a strict all-in/ all-out batch-management system (Mix1), a progressive decrease in the decontamination efficacy (levels Decont100 to Decont50) led to a sharp increase in the mean prevalence of shedding in groups of slaughter pigs (Figure 4A). This increase was not linear. Whatever the batch-management system combined with a mean age at slaughter of 25 wk (Growth1), a decrease in the decontamination efficacy from C1 to C4 led to a significant increase (P < 0.001) in the mean prevalence of around 15 points (Figure 4).

Effect of Infection at Weaning Age. In the scenarios presented above, which differed in their biosecurity practices, the Salmonella prevalence at weaning varied only with different decontamination efficacies. The proportion of positive batches at weaning increased from 68% of positive batches for the greatest values of decontamination to 79% for Decont50. At this stage, the within batch prevalence (in positive batches) increased from 4.6% (5th percentile = 0.6%; 95th percentile = 12.4%) for scenarios with a 100% decontamination efficacy to 10.1% (5th percentile = 2.1%; 95th percentile = 19.4%) for scenarios with 50% decontamination (the only scenario in which decontamination of the rooms of the sows was decreased).

Introducing only negative batches of weaned pigs induced a dramatic decrease in the shedding prevalence in groups of delivered pigs. One hundred percent of the simulations led to a Salmonella-negative herd for the Decont100 and Decont90, 45% of the simulations for Decont80, and only 5% of the simulations for Decont50.

DISCUSSION

We showed in this study that a model coupling herd dynamics and infection dynamics as the one we previously proposed (Lurette et al., 2008b) was a suitable tool to assess the impact of biosecurity measures on Salmonella spread within a farrow-to-finish pig herd. The model we used took into account the main components of the pig management system and Salmonella epidemiology in a fairly detailed and realistic way. It can therefore be adapted to represent different scenarios, combining several batch-management systems, mean ages at slaughter, and rearing room decontamination efficacies. Cumulative frequency distribution of the Salmonella prevalence of shedding in groups of slaughter pigs according to several scenarios, with a strict all-in/all-out batch management (Mix0). Panel A: reference scenario (Mix0-Growth1-Decont99.9); panel B: scenarios with a mean age at slaughter of 25 wk (Growth1); panel C: scenarios with a mean age at slaughter of 27.5 wk (Growth2); and panel D: scenarios with a mean age at slaughter of 30 wk (Growth3). Decontamination efficacy (Decont) ranged from 100 to 50%. The distributions were calculated over 60,000 groups of slaughter pigs for each scenario (300 simulations performed over 400 wk). Mean and SD for the Salmonella prevalence of shedding in groups of slaughter pigs according to 54 scenarios. Panel A: scenarios with an all-in/all-out batch-management system (Mix0); Panel B: scenarios with pig mixing at the end of the finishing period (Mix1); Panel C: scenarios with pig mixing at the end of the postweaning and finishing periods (Mix2). The means and SD were calculated over 60,000 groups of slaughter pigs for each scenario (300 simulations performed over 400 wk). Definition of scenarios was based on the factor levels pig mixing (Mix), mean age at slaughter (Growth) ranging from 25 to 30 wk, and decontamination efficacy (Decont) ranging from 100 to 50%. a-e Within a panel, prevalences with the same letter are not significantly different (P > 0.05).

A thorough model validation would require prevalence data from herds with constant characteristics (e.g., batch management, hygiene, herd size), which were not available. Given that the seroprevalence distributions reported in the literature were built from several herds, only qualitative validation can be performed. However, consistent with observed data under field conditions, our model captured both the greater variations in prevalence between consecutive groups of pigs at slaughter and the existence of seronegative groups in infected herds.

In this study, Salmonella infection was estimated by the prevalence of shedding in groups of slaughter pigs, which included all shedding epidemiological states (seronegative and seropositive shedders). Indeed, this output seemed well suited to assess the potential risk to food safety because pigs shedding the bacteria are responsible for contamination during transport and lairage [START_REF] Swanenburg | Salmonella in the lairage of pig slaughterhouses[END_REF]. These stages are stressful and can increase shedding in infected animals and then promote new infections. Because the delay between infection and shedding can be very short (i.e., 2 to 6 h; [START_REF] Wood | Distribution of persistent Salmonella typhimurium infection in internal organs of swine[END_REF], these newly infected animals can participate to cross-contamination.

Salmonella in pig herds can be controlled by preventing the introduction of bacteria into the herds or by reducing within-herd transmission or both. However, the effect of control measures on Salmonella prevalence is difficult to evaluate in herds. Several modeling approaches have identified key factors, which should be targeted by potential control measures. van der [START_REF] Van Der Gaag | Cost-effectiveness of controlling Salmonella in the pork chain[END_REF] and Hill et al. (2007) highlighted the major role of transmission variables in the control of Salmonella prevalence in positive herds. These variables were tested at the animal level. Our study concentrated on 3 factors at the herd level: 1) herd management with or without the possibility pig mixing among batches, 2) mean age at slaughter, and 3) decontamination efficacy.

The levels of these factors used here were based on observations in actual pig herds. The 3 batch-management systems tested derived from the results of a survey of 36 farms in France [START_REF] Hébert | Batch farrowing implementation in pig herd and influence on contact among animals[END_REF], which indicated that pig mixing was frequent during postweaning and finishing periods. We used performance results to choose mean age at slaughter (IFIP, 2008) for the mean and the upper bound of the confidence interval and situations in which drastic increases might occur, for example, due to a disease affecting growth rate. Lastly, the decontamination efficacy levels tested were based on the literature. The in vitro efficacy of a disinfectant can produce a 5-log decrease in the quantity of bacteria present in a test sample. However, under farm conditions, the efficacy of disinfectants is likely to be less because of OM remaining after cleaning and low temperatures in the animal houses. Previous studies revealed highly variable and quite poor decontamination efficacy in real-life situations [less than 80% efficacy in poultry transport containers [START_REF] Ramesh | Evaluation of chemical disinfectants for the elimination of Salmonella biofilms from poultry transport containers[END_REF] and poultry houses [START_REF] Gradel | Monitoring the efficacy of steam and formaldehyde treatment of naturally Salmonella-infected layer houses[END_REF][START_REF] Wales | Assessment of cleaning and disinfection in Salmonella-contaminated poultry layer houses using qualitative and semi-quantitative culture techniques[END_REF]].

By comparing the relative contributions of pig mixing, room decontamination efficacy, and increased mean age at slaughter with the Salmonella prevalence in groups of slaughter pigs, our study showed that decontamination efficacy was the most influential factor. The decontamination and the implementation of a drying period in rearing rooms between 2 consecutive batches have been identified as protective factors in controlling Salmonella shedding by finishing pigs [START_REF] Lo Fo Wong | Herd-level risk factors for subclinical Salmonella infection in European finishing pig herds[END_REF][START_REF] Schmidt | The association between cleaning and disinfection of lairage pens and the prevalence of Salmonella enterica in swine at harvest[END_REF]. However, bacteriological methods are time-consuming and rarely used to monitor Salmonella in pig herds. Most published data focused on the number of samples that become negative after a decontamination process, but the quantity of bacteria eliminated is poorly documented. Additional data are needed to better understand and quantify the influence of the decontamination efficacy on Salmonella spread within pig herds.

With or without pig mixing, an increase in the mean age at slaughter induced a greater prevalence in pigs at slaughter. Indeed, such an increase led to a greater pig density in the finishing rooms, a longer exposure time for the susceptible pigs, and more Salmonella shed by infected pigs.

The minor effect of pig mixing on Salmonella prevalence was unexpected, especially when combined with greater values of mean age at slaughter, which exacerbated pig mixing occurrence (Lurette et al., 2008a). Batch-management system, as opposed to continuous flow, has been identified as a protective factor against Salmonella spread [START_REF] Stege | Risk factors for intestinal pathogens in Danish finishing pig herds[END_REF][START_REF] Lo Fo Wong | Herd-level risk factors for subclinical Salmonella infection in European finishing pig herds[END_REF]. Salmonella prevalence is highly variable between batches within a herd (e.g., in France; Corrégé and Guyomart, 2007), so mixing is expected to help spreading the bacteria. Several hypotheses can be formulated to explain the minimal impact of animal mixing in our study. The first hypothesis is that the number of infected pigs mixed was not great enough to induce a significant modification in the mean prevalence of shedding in groups of slaughter pigs. The rearing room capacity limited the number of pigs mixed. At the end of the postweaning period, even if almost 50% of the batches contained pigs from other batches, the number of mixed pigs remained small. At the end of the finishing period, the number of mixed pigs was relatively large, but these pigs stayed at most 2 wk in their new finishing room. The second hypothesis is linked to our representation of the infection probability, which depended on the quantity of bacteria per pig in a room. The inflow of infected pigs from another batch increased the global quantity of bacteria shed in the room, but also the number of pigs in the room. The prevalence increase was therefore limited. The third and last hypothesis is that the greater impact of batch management observed in previous studies was mainly due to the decontamination of rearing rooms between 2 consecutive batches, which cannot be implemented in continuous flow. This again emphasized the importance of the cleaning/disinfecting process.

Table 3. Proportion of batches from which pigs are mixed with another batch, mean number of mixed pigs per batch mixed, and prevalence of shedding in mixed pigs; these 3 outputs were computed at the end of the postweaning and finishing periods for the scenarios with pig mixing at the end of the 2 periods (Mix2) 1 This importance of the decontamination process was also highlighted by the fact that the introduction of negative batches of piglets in a contaminated herd can lead to Salmonella-free groups of slaughter pigs only for high decontamination efficacies. However, between 99 and 100%, the decontamination efficacy had the same impact on the prevalence of pigs at slaughter. Further studies should focus on the combinations of several actions to explore the probability of the infection extinction.

Scenario

Up to now, there are no direct incentives for French pork producers to reduce the Salmonella prevalence in their herds: neither control systems nor financial penalties for the delivery of contaminated slaughter pigs. Indirect incentives, such as the increased interest for food safety and the large competition in the pork (international) market, are gaining importance. At present, to maximize their income, producers prefer to avoid delivering underweight pigs to the slaughterhouse rather than to observe the recommended strict all-in/all-out management. In the context of Salmonella control, our simulation results showed that some flexibility in batch management is acceptable, provided that a good level of decontamination is ensured in the rearing rooms. An increase in the age at slaughter is usually due to growth problems that cannot be compensated because they are usually discovered quite late. However, producers should pay a particular attention to batches in which such problems occur, as they give rise to greater Salmonella prevalence.

A 3-wk-interval batch-management system is adopted in the majority of French pig farms and is increasingly applied in European countries [START_REF] Brown | Advantages and disadvantages of batch farrowing[END_REF]. A system with a 1-wk interval between 2 batches is developing in large pig herds. It is very similar to the 3-wk-interval system (e.g., room occupancy, decontamination process, drying period duration, succession of batches in the rearing rooms), but pig mixing can be more easily implemented due to the small age difference between consecutive batches. It would be interesting to explore the impact of such a management system on Salmonella spread.

In conclusion, our simulation results emphasized the need to place priority efforts on the cleaning-disinfecting process of rooms to limit the spread of Salmonella within farrow-to-finish pig herds. As a further perspective, the flexibility of our approach makes it a suitable tool to assess the impact of control measures implemented at the animal level (modification of the individual susceptibility by genetic selection or vaccination, for instance) in combination with herd management.

Figure 2 .

 2 Figure2. Cumulative frequency distribution of the Salmonella prevalence of shedding in groups of slaughter pigs according to several scenarios, with a strict all-in/all-out batch management (Mix0). Panel A: reference scenario (Mix0-Growth1-Decont99.9); panel B: scenarios with a mean age at slaughter of 25 wk (Growth1); panel C: scenarios with a mean age at slaughter of 27.5 wk (Growth2); and panel D: scenarios with a mean age at slaughter of 30 wk (Growth3). Decontamination efficacy (Decont) ranged from 100 to 50%. The distributions were calculated over 60,000 groups of slaughter pigs for each scenario (300 simulations performed over 400 wk).

Figure 3 .

 3 Figure 3. Contribution of 3 factors: pig mixing, mean age at slaughter, decontamination efficacy (Decont), and their interactions to the variability of the frequency distribution of the Salmonella prevalence of shedding in groups of slaughter pigs. Factors and their interactions are ranked in descending order of contribution. Sensitivity indices were computed according to a method based on principal components analysis (PCA) and ANOVA (in black: factor or interaction index; in white: cumulative contribution). Panel A: indices corresponding to the first component of PCA, which explained 81.5% of the prevalence distribution variability; panel B: indices corresponding to the second component of the PCA, which explained 17.2% of the variability.

Figure 4 .

 4 Figure 4. Mean and SD for the Salmonella prevalence of shedding in groups of slaughter pigs according to 54 scenarios. Panel A: scenarios with an all-in/all-out batch-management system (Mix0); Panel B: scenarios with pig mixing at the end of the finishing period (Mix1); Panel C: scenarios with pig mixing at the end of the postweaning and finishing periods (Mix2). The means and SD were calculated over 60,000 groups of slaughter pigs for each scenario (300 simulations performed over 400 wk). Definition of scenarios was based on the factor levels pig mixing (Mix), mean age at slaughter (Growth) ranging from 25 to 30 wk, and decontamination efficacy (Decont) ranging from 100 to 50%. a-e Within a panel, prevalences with the same letter are not significantly different (P > 0.05).

  was based on the factor levels pig mixing (Mix), mean age at slaughter (Growth) ranging from 25 to 30 wk, and decontamination efficacy (Decont) ranging from 100 to 50%.

Table 1 .

 1 Definition and values of the parameters used in the Salmonella epidemiological model (based on TableIinLurette et al., 2008b, issue 5; http://www.veterinaryresearch.org/)

	Description

Table 2 .

 2 Description and values of 3 factors (pig mixing, mean age at slaughter, decontamination efficacy) according to their implementation modalities (3 modalities for pig mixing and mean age at slaughter; 5 modalities for decontamination efficacy) within biosecurity measures 1

			Mix0	Mix1			Mix2
						Mixing of pigs at the end
				Mixing of pigs at the end	of the postweaning and
	Item	Description	No pig mixing	of the finishing period	the finishing periods
	Mean age at slaughter	Name	Growth1	Growth2	Growth3
		Value, wk	25	27.5	30
	Decontamination efficacy	Name	Decont100	Decont 99.9	Decont99	Decont80	Decont50
		Value, %	100	99.9	99	80	50
	1

Corresponding author: lurette@supagro.inra.fr Received February 15,

Accepted January 27,

Downloaded from https://academic.oup.com/jas/article/89/7/2210/4764348 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 23 January 2023

Herd biosecurity and Salmonella infectionDownloaded from https://academic.oup.com/jas/article/89/7/2210/4764348 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 23 January 2023

The values of parameters were calibrated from a sensitivity analysis performed inLurette et al., 2009.

Salmonella infectious unit is defined as the smallest quantity of Salmonella that can infect a susceptible pig.

Lurette et al. Downloaded from https://academic.oup.com/jas/article/89/7/2210/4764348 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 23 January 2023


Alexandre G De Brevern

Editorial -Infectious Diseases -Drug Targets (in silico special issue)

.. These researches had been carried out using more elaborated biochemical, biophysical and biological approaches.

However, with the explosion of genomic sequencing -815 complete genomes are made available for the scientific community (as of January 2009) [9]-complementing the

experimental information with the increasing power of computational facilities has given new opportunities to fight against infectious diseases and to identify pertinent drug targets with novel methodologies. The in silico approaches have been playing a prominent role in this research area during the last decade. This special issue presents the various views about the different in silico approaches by some of the best international research teams. Pr. Sowdhamini's group has compared different genomes from a group of enterobacteric pathogens known to share similar genomic content but having diverse host specificities and distinct disease symptoms [START_REF] Bhaduri | Conservation and Divergence among Salmonella enterica subspecies[END_REF]. The detailed cross-genome analysis of these subspecies provides an understanding of the diversity and unique attributes defined in the individual Salmonella enterica genomes. Pr. Srinivasan's group develops new approaches dedicated to Plasmodium falciparum analysis, the most important causative agent of malaria [START_REF] Nidhi | Evolutionary divergence of Plasmodium falciparum: Sequences, protein-protein interactions, pathways and processes[END_REF]. The latter has a very specific genome and thus needs to be studied thoroughly and specifically.

They present examples of protein-protein interactions across human and P. falciparum, potentially happening during pathogenesis. Pr. Deléage's work deals with Hepatitis C Virus (HCV). They have developed the European Hepatitis C Virus Database (euHCVdb, http://euhcvdb.ibcp.fr/), a collection of relevant structural models that can help in drug design , with strategies for combating resistance to drug treatment and to have a better understanding structural biology of the HCV [START_REF] Combet | The euHCVdb suite of in silico tools for investigating the structural impact of mutations in Hepatitis C virus proteins[END_REF]. They present some examples of the use of the database.

Within this new research field, an important axis of research concerns the transmembrane proteins, they represent about ~25% of proteins coded by genomes. Moreover, they serve as targets for about 2/3 rd of the marketed drugs out of which 50% specifically target a GPCR [START_REF] Klabunde | Drug design strategies for targeting G-protein-coupled receptors[END_REF]. As these proteins are embedded in a lipid membrane that constitutes a very specific environment, they represent only about ~1% of all the available structures, owing to the difficulties associated with their crystallization [START_REF] Fleishman | Transmembrane protein structures without X-rays[END_REF]. . Thus alternative approaches are required to obtain structural information. Consequently methods aiming at constructing 3D structural models are becoming an important area of research, for understanding biological mechanisms and interactions [START_REF] Radestock | Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring[END_REF]. Wang and Duan summarizes the recent computational researches done on CC-chemokine receptor 5 (CCR5), an essential co-receptor for HIV entry into the cells and show how the recently solved GPCR structures would provide new insights into the modeling of CCR5-inhibitor binding [START_REF] Wang | HIV co-receptor CCR5: structure and interactions with Inhibitors[END_REF]. Pr. Etchebest's group work focus on an unorthodox chemokine receptor, named DARC, which binds chemokines of both CC and CXC classes and do not couple to G proteins and activate their signaling pathways. DARC had also been associated to cancer progression, numerous inflammatory diseases, and possibly to AIDS. We show our recent development of the construction and analyzes of structural models of DARC [START_REF] De Brevern | A structural model of a seven-transmembrane helix receptor: the Duffy antigen/receptor for chemokine (DARC)[END_REF]. We underline the difficulty to propose pertinent structural models of transmembrane protein using comparative modeling process, and also highlight the use of other dedicated approaches like the analysis using Protein Blocks [START_REF] De Brevern | New assessment of a structural alphabet[END_REF][START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF][START_REF] Etchebest | A structural alphabet for local protein structures: improved prediction methods[END_REF]. Finally, we present the recent development of protein -protein docking carried out between DARC structural models and CXCL-8 structures using an innovative hierarchal search procedure, based on both rigid and flexible docking [START_REF] De Brevern | silico studies on DARC[END_REF].

Dealing with Plasmodium falciparum, Pr. Louw and co-workers describes the peculiarities of malaria proteins and various in silico strategies to select and allow descriptions of the molecular structures of potential drug target candidates. They also present the subsequent rational approaches that can be used for drug design. The uniqueness of this organism makes the in silico approaches for the specific design of an inhibitor drugs invaluable and as an economic and rational alternative to chemical library screening [START_REF] De Beer | Antimalarial drug discovery: in silico structural biology and rational drug design[END_REF]. Following the same approach, Murphy and Brown focus on another major global healthcare problem: the resurgence of drug resistant Mycobacterium tuberculosis. The identification of genes essential for the bacterium in its dormancy phase infections, is a key strategy in the development of new anti-Mycobacterium tuberculosis therapeutics. They present the applications of advanced computational analyses to predict potential drug targets and also highlight the difficulties associated with translating in silico predictions to effective clinical therapies [START_REF] Murphy | Computational Biology in Anti-Tuberculosis Drug Discovery[END_REF]. Dr.

Watowich's group works on the Dengue virus, one of the most important global pathogens which represent a global pandemic. The resurgence of Dengue virus since the 70's has occurred after the cessation of mosquito control measures. The authors summarize the biology of this Flaviviridae and especially its different proteins. They highlight the possibility of utilizing Dengue virus nonstructural proteins as potential drug targets and also give an extensive overview of computational methods for drug discovery like docking, HTS or ligand compound database [START_REF] Tomlinson | New Approaches to Structure-Based Discovery of Dengue Protease Inhibitors[END_REF].

To conclude this special issue, we present three papers that deal with impressive novel methodological developments. The first one is by Dr. Doppelt-Azeroual and co-workers. As protein structures are directly associated with their functions that often involves the binding of other proteins, ligands, nucleic acids or other compounds, the knowledge of 3D structures can explain biological mechanisms and help to design drugs. They have developed MED-SuMo [START_REF] Medit-Sa | [END_REF], an efficient method that can compare and detect binding sites. This tool can locate similar regions on macromolecular surfaces, associated to a defined chemical function. In this review, they present the recent improvements in MED-SuMo and its interest in several applications in biology, e.g., structural superimposition, pocket profiling, drug repurposing and an automated function binding sites classification. A new protocol combines a fragmentbased approach and local similarities of protein surfaces [START_REF] Doppelt-Azeroual | MED-SuMo Applications[END_REF]. Another one deals with the use of powerful Grid computing in the life science domain. Grid computing is an exciting new technology promising to revolutionize many services already offered by the internet. Grids are defined as a fully distributed, dynamically reconfigurable, scalable and autonomous infrastructure to provide location independent, pervasive, reliable, secure and efficient access to a coordinated set of services encapsulating and virtualizing resource [START_REF] Share | the journey: a European HealthGrid roadmap[END_REF]. Dr. Breton and collaborators have explored some innovative in silico approaches to better tackle avian flu, taking advantage of the very large computing resources available on international grid infrastructures. Grids are used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potential new leads active on mutated strains [START_REF] Breton | Innovative in silico approaches to address avian flu using grid technology[END_REF].

The third one concludes this issue by presenting the difficult problem of text mining. As nowadays, the number of publications has increased tremendously, it has a crucial issue. The solution proposed by Vellay and collaborators are implemented inside Pipeline Pilot [29]. This is a famous application designed to manipulate and analyze huge quantities of data in real time. This approach, known as "data pipelining", uses a data flow framework to describe the processing of data [START_REF]SciTegic data analysis and reporting platform[END_REF]. After presenting the core of the Bibliography Platform protocol, the authors gives a pertinent example of the relevance of their approach. In the context of the neglected disease Leishmaniasis, this interface presents an overview allowing a fast understanding of the topic, an appreciation of its growing trend, and an instant identification of the most prolific authors and most popular journals in the disease area [START_REF] Vellay | Interactive Text Mining with Pipeline Pilot: A bibliographic web-based tool for PubMed[END_REF].

The quality of articles in this special issue depends on the authors who have clearly done a terrific job, and also on those individuals who lend their expertise to review manuscripts. On behalf of myself and the authors of papers published in this issue, I express my sincere thanks and appreciation to the individuals who generously gave their time to review one or more manuscripts. I would also like to thanks people of my team, especially Catherine Etchebest, Agnel Praveen Joseph and Aurélie Bornot for their help.