After spreading in the Americas, West Nile virus was detected in Guadeloupe (French West Indies) for the first time in 2002. Ever since, several organizations have conducted research, serological surveys, and surveillance activities to detect the virus in horses, birds, mosquitoes, and humans. Organizations often carried them out independently, leading to knowledge gaps within the current virus' situation. Nearly 20 years after the first evidence of West Nile virus in the archipelago, it has not yet been isolated, its impact on human and animal populations is unknown, and its local epidemiological cycle is still poorly understood. Within the framework of a pilot project started in Guadeloupe in 2019, West Nile virus was chosen as a federative model to apply the “One Health” approach for zoonotic epidemiological surveillance and shift from a sectorial to an integrated surveillance system. Human, animal, and environmental health actors involved in both research and surveillance were considered. Semi-directed interviews and a Social Network Analysis were carried out to learn about the surveillance network structure and actors, analyze information flows, and identify communication challenges. An information system was developed to fill major gaps: users' needs and main functionalities were defined through a participatory process where actors also tested and validated the tool. Additionally, all actors shared their data, which were digitized, cataloged, and centralized, to be analyzed later. An R Shiny server was integrated into the information system, allowing an accessible and dynamic display of data showcasing all of the partners' information. Finally, a series of virtual workshops were organized among actors to discuss preliminary results and plan the next steps to improve West Nile Virus and vector-borne or emerging zoonosis surveillance. The actors are willing to build a more resilient and cooperative network in Guadeloupe with improved relevance, efficiency, and effectiveness of their work.

BACKGROUND:Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe.METHODS:We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance.RESULTS:The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level.CONCLUSIONS:The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R0 modelling of Culicoides-borne infections at a continental scale.

BACKGROUND:Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe.METHODS:We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present.RESULTS:The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups.CONCLUSIONS:The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain.

Background: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are vectors of bluetongue virus (BTV), African horse sickness virus and Schmallenberg virus (SBV). Outbreaks of both BTV and SBV have affected large parts of Europe. The spread of these diseases depends largely on vector distribution and abundance. The aim of this analysis was to identify and quantify major spatial patterns and temporal trends in the distribution and seasonal variation of observed Culicoides abundance in nine countries in Europe. Methods: We gathered existing Culicoides data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. In total, 31,429 Culicoides trap collections were available from 904 ruminant farms across these countries between 2007 and 2013. Results: The Obsoletus ensemble was distributed widely in Europe and accounted for 83% of all 8,842,998 Culicoides specimens in the dataset, with the highest mean monthly abundance recorded in France, Germany and southern Norway. The Pulicaris ensemble accounted for only 12% of the specimens and had a relatively southerly and easterly spatial distribution compared to the Obsoletus ensemble. Culicoides imicola Kieffer was only found in Spain and the southernmost part of France. There was a clear spatial trend in the accumulated annual abundance from southern to northern Europe, with the Obsoletus ensemble steadily increasing from 4000 per year in southern Europe to 500,000 in Scandinavia. The Pulicaris ensemble showed a very different pattern, with an increase in the accumulated annual abundance from 1600 in Spain, peaking at 41,000 in northern Germany and then decreasing again toward northern latitudes. For the two species ensembles and C. imicola, the season began between January and April, with later start dates and increasingly shorter vector seasons at more northerly latitudes. Conclusion: We present the first maps of seasonal Culicoides abundance in large parts of Europe covering a gradient from southern Spain to northern Scandinavia. The identified temporal trends and spatial patterns are useful for planning the allocation of resources for international prevention and surveillance programmes in the European Union.

Wild boars and domestic pigs belong to the same species (Sus scrofa). When sympatric populations of wild boars, feral pigs, and domestic pigs share the same environment, interactions between domestic and wild suids (IDWS) are suspected to facilitate the spread and maintenance of several pig pathogens which can impact on public health and pig production. However, information on the nature and factors facilitating those IDWS are rarely described in the literature. In order to understand the occurrence, nature, and the factors facilitating IDWS, a total of 85 semi-structured interviews were implemented face to face among 25 strict farmers, 20 strict hunters, and 40 hunting farmers in the main traditional pig-farming regions of Corsica, where IDWS are suspected to be common and widespread. Different forms of IDWS were described: those linked with sexual attraction of wild boars by domestic sows (including sexual interactions and fights between wild and domestic boars) were most frequently reported (by 61 and 44% of the respondents, respectively) in the autumn months and early winter. Foraging around common food or water was equally frequent (reported by 60% of the respondents) but spread all along the year except in winter. Spatially, IDWS were more frequent in higher altitude pastures were pig herds remain unattended during summer and autumn months with limited human presence. Abandonment of carcasses and carcass offal in the forest were equally frequent and efficient form of IDWS reported by 70% of the respondents. Certain traditional practices already implemented by hunters and farmers had the potential to mitigate IDWS in the local context. This study provided quantitative evidence of the nature of different IDWS in the context of extensive commercial outdoor pig farming in Corsica and identified their spatial and temporal trends. The identification of those trends is useful to target suitable times and locations to develop further ecological investigations of IDWS at a finer scale in order to better understand diseases transmission patterns between populations and promote adapted management strategies.

La veille en sante animale, et notamment la detection precoce d'emergences au niveau mondial d'agents pathogenes, est l'un des moyens permettant de prevenir l'introduction en France de dangers sanitaires (Paquet et al., 2006). Dans ce contexte, cet article presente une plateforme dediee a la veille automatique allant du recueil des donnees textuelles (depeches) jusqu'a la restitution synthetique des informations extraites.

Bushpigs (BPs) (Potamochoerus larvatus) and warthogs (WHs) (Phacochoerus africanus), which are widely distributed in Eastern Africa, are likely to cohabitate in the same environment with domestic pigs (DPs), facilitating the transmission of shared pathogens. However, potential interactions between BP, WH, and DP, and the resulting potential circulation of infectious diseases have rarely been investigated in Africa to date. In order to understand the dynamics of such interactions and the potential influence of human behavior and husbandry practices on them, individual interviews (n = 233) and participatory rural appraisals (n = 11) were carried out among Ugandan pig farmers at the edge of Murchison Falls National Park, northern Uganda. In addition, as an example of possible implications of wild and DP interactions, non-linear multivariate analysis (multiple correspondence analyses) was used to investigate the potential association between the aforementioned factors (interactions and human behavior and practices) and farmer reported African swine fever (ASF) outbreaks. No direct interactions between wild pigs (WPs) and DP were reported in our study area. However, indirect interactions were described by 83 (35.6%) of the participants and were identified to be more common at water sources during the dry season. Equally, eight (3.4%) farmers declared exposing their DP to raw hunting leftovers of WPs. The exploratory analysis performed suggested possible associations between the farmer reported ASF outbreaks and indirect interactions, free-range housing systems, dry season, and having a WH burrow less than 3 km from the household. Our study was useful to gather local knowledge and to identify knowledge gaps about potential interactions between wild and DP in this area. This information could be useful to facilitate the design of future observational studies to better understand the potential transmission of pathogens between wild and DPs.

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. The work in this paper is part of the methodology for monitoring the web that we currently develop for the French epidemic intelligence team in animal health. We focus on the new and exotic infectious animal diseases that occur worldwide and that are of potential threat to the animal health in France.In order to detect relevant information on the web, we present an innovative approach that retrieves documents using queries based on terms automatically extracted from a corpus of relevant documents and validated with a consensus of domain experts (Delphi method). As a decision support tool to domain experts we introduce a new measure for ranking of extracted terms in order to highlight the more relevant terms. To categorise documents retrieved from the web we use Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers.We evaluated our approach on documents on African swine fever (ASF) outbreaks for the period from 2011 to 2014, retrieved from the Google search engine and the PubMed database. From 2400 terms extracted from two corpora of relevant ASF documents, 135 terms were relevant to characterise ASF emergence. The domain experts identified as highly specific to characterise ASF emergence the terms which describe mortality, fever and haemorrhagic clinical signs in Suidae.The new ranking measure correctly ranked the ASF relevant terms until position 161 and fairly until position 227, with areas under ROC curves (AUCs) of 0.802 and 0.709 respectively.Both classifiers were accurate to classify a set of 545 ASF documents (NB of 0.747 and SVM of 0.725) into appropriate categories of relevant (disease outbreak) and irrelevant (economic and general) documents.Our results show that relevant documents can serve as a source of terms to detect infectious animal disease emergence on the web.Our method is generic and can be used both in animal and public health domain.

In a context of intensification of international trade and travels, the transboundary spread of emerging human or animal pathogens represents a growing concern. One of the missions of the national veterinary services is to implement international epidemiological intelligence for a timely and accurate detection of emerging animal infectious diseases (EAID) worldwide, and take early actions to prevent their introduction on the national territory. For this purpose, an efficient use of the information published on the web is essential. The authors present a comprehensive method for identification of relevant associations between terms describing clinical signs and hosts to build queries to monitor the web for early detection of EAID. Using text and web mining approaches, they present statistical measures for automatic selection of relevant associations between terms. In addition, expert elicitation is used to highlight the most relevant terms and associations among those automatically selected. The authors assessed the performance of the combination of the automatic approach and expert elicitation to monitor the web for a list of selected animal pathogens.

If wildlife is considered as a renewable natural resource, for many rural Africans the occurrence of human wildlife conflict (HWC) overshadows expected outcomes from conservation and co-management initiatives. To reduce the magnitude of HWC, modern approaches deal with problem animals that cause conflicts while increasing the level of tolerance in the affected human populations. Assessing the local impact of HWC is part of this mitigation package, the objective been to provide timely information to adapt strategies and actions as data indicates what works and why. Lack of communication and trust between wildlife authorities and people concerned by HWC makes the effectiveness of the reporting poor, which raises the question of selecting the most appropriate technology for a real-time monitoring scheme with the capacity to inform decision-makers and improve the understanding of conflicts. To explore the feasibility of HWC monitoring, a series of tests was conducted in central Africa with KoBoCollect, an application from the KoBoToolbox an open source of tools for data collection and analysis based on OpenDataKit. With this application, data were collected using Smartphone on and off-line then synchronized into a database. Involving a regional HWC working group the 5W&H method was chosen to develop data trees of the key information needed to understand HWC problems. The 30+ variables were selected to develop an electronic form and responses to questions been facilitated by multiple choice responses with checkbox options. After a 9 month field test from April to December 2015, more than 300 electronic submissions were collected from Congo (42%), DRC (28%), Gabon (19%) and Cameroun (7%). Not surprisingly the elephant is the species most often involved in HWC (51%) followed by the hippo (11%) and rodents (11%), the other 11 species involved in HWC playing a minor role. If human casualties were rare (2%), the most predominant impact was crop raiding (82%). Mitigation measures were assessed according to the set of solutions of an existing HWC toolbox. Only making noise (33%) or fire (26%) appeared to be solutions mainly applied by local communities. Tested also to monitor hunting pressure in the same region KoBoCollect appears to be an easy to use tool to collect data at low cost in remote areas but questions remain on how to promote and popularize such an approach to fulfill management needs at landscape, national and regional levels. (Texte integral)

Purpose: Corsica is a French Mediterranean Island with traditional extensive pig farming where free ranging pigs often interact with an abundant and widespread population of wild boars and feral pigs. Hunters and small-scale farmers in rural areas are often privileged observers of interactions between wild and domestic pigs (IWDP) and open questionnaires are a valid and easy way to obtain qualitative and quantitative information on the nature, duration and seasonality of those interactions. Methods: A total of 86 persons (25 strict farmers, 20 strict hunters and 41 hunters and farmers) were interviewed in this manner in the 6 main production areas of Corsica to obtain qualitative and quantitative information on IWDP, which are suspected to be very common. A principal component analysis allowed to determine the variables linked with the IWDP. According to these first results, correlation matrices allowed to confirm and quantify these relations. Results IWDP were highly seasonal and concentrated in the autumn months (mostly November). Most commonly reported direct interactions were mating (60% of farmers), fighting (56% of farmers) and foraging together (36% of farmers). Some farming and hunting practices such as fencing or hunting beat seemed to have a significant negative influence on the occurrence of IWDP. Men driven interactions through the availability of carcass offal from hunted or slaughtered domestic and wild pigs were commonly reported by 68% of farmers and 90% of hunters. High IWDP induced farmers to castrate their females. Conclusions: The use of semi-structured interviews proved to be a very efficient and cheap method to gather information about the occurrence of natural and men driven interactions between domestic and wild pigs that can be used to design awareness campaigns or to identify hot spot areas for infectious disease transmission between domestic and wild animals. Relevance: IWDP remain widespread and represent a serious constraint in the control and eradication of swine infectious diseases such as African Swine Fever present in the neighboring Sardinia since 1978, or Aujeszky disease which remains present in Corsica while eradicated from France mainland. (Texte integral)

La veille en sante animale, et notamment la detection precoce d'emergences au niveau mondial d'agents pathogenes, est l'un des moyens permettant de prevenir l'introduction en France de dangers sanitaires (Paquet et al., 2006). Cet article presente une plateforme dediee a la collecte de donnees (depeches) utiles pour la veille automatique. Le recueil des depeches s'appuie sur des requetes constituees de mots-cles de maladies, d'hotes et de symptomes appliquees a Google News. Une interface Web a ete developpee pour consulter les articles collectes et parametrer le processus de recueil en definissant de nouvelles combinaisons de mots-cles.

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. We propose a generic method to enrich and extend the use of different expressions as queries in order to improve the acquisition of relevant disease related pages on the web. Our method combines a text mining approach to extract terms from corpora of relevant disease outbreak documents, and domain expert elicitation (Delphi method) to propose expressions and to select relevant combinations between terms obtained with text mining. In this paper we evaluated the performance as queries of a number of expressions obtained with text mining and validated by a domain expert and expressions proposed by a panel of 21 domain experts. We used African swine fever as an infectious animal disease model. The expressions obtained with text mining outperformed as queries the expressions proposed by domain experts. However, domain experts proposed expressions not extracted automatically. Our method is simple to conduct and flexible to adapt to any other animal infectious disease and even in the public health domain.

Corsica is a French Mediterranean island with traditional extensive pig farming oriented towards the production of high quality cured meat products. The increasing success of these cured products in continental Europe has triggered the development and organisation of an extensive pig farming industry. However, these pig farming practices have seldom been described and analysed to understand the potential risk of introduction and spread of infectious diseases. We conducted a cross-sectional study in Corsica in 2013 to characterise the main pig management practices and to identify groups of farms with similar practices and therefore homogeneous risk of introduction and spread of infectious diseases. We interviewed 68 pig farmers and investigated different farm management practices which could lead to contact between herds, such as trading animals, sharing pastures, feed and reproduction management (direct contacts), slaughtering and carcass waste management, and contacts with people and vehicles (indirect contacts). The practices were described and the farms grouped by multiple factor and hierarchical clustering analyses. Results revealed interesting patterns in the introduction and spread of infectious disease, such as the seasonality of pig production, the potential local spread of diseases in pastures due to the presence of free-ranging boars, carcasses, and animal waste. Multivariate analyses identified four groups of farms with different levels of risk of the spread of infectious disease, illustrating changes in farmers' customs from free-range uncontrolled farming systems to more controlled systems aimed at the production of high quality pork products. These results will be useful to more realistically simulate the spread of infectious diseases among Corsican pig farms and highlight the need for awareness raising campaigns among the stakeholders to reduce risky practices.

Timeliness and precision in detecting exotic animal infectious disease outbreaks is crucial for preventing their spread. In 2013, the French national platform for animal disease surveillance has set up an international epidemiological intelligence team (so-called VSI team) aiming at detecting, verifying and monitoring signals of disease emergence from different sources of information, including the Internet. We propose an innovative method for monitoring disease emergence on the Internet. It is based on 3sequential steps:1) web crawling,2) automatic classification of disease outbreak documents by machine learning approaches,3) extraction of information from documents(e.g., disease, number of cases, location, etc.).To query the web, the choice of relevant terms is crucial. For this purpose, we used text mining together with a collective domain expertise following a Delphi method. This approach allowed highlighting the relevant terms to detect signals of disease emergence on the Internet. We have applied it to detect documents addressing African swine fever (ASF) outbreaks(i.e. 123 dispatches from Google, and 45 from PubMed) written in English language, obtained for the period 2011-2014 with the baseline query “African swine fever outbreak”. Based on 2400 terms extracted with the text-mining approach, our automatic search system associated with the collective domain expertise (i.e. evaluation of 20 groups of terms by 21 specialists) identified 3 groups of highly specific terms to detect signals of ASF emergence:1) haemorrhagic fever in Suidae, 2) mortality in Suidae and 3) swine fever. Implemented as complex queries, these groups of terms allowed finding previously undetected ASF outbreak articles with the baseline query (period 2011-14):3for each of groups 1 and 2, vs.54 for group 3.Monitoring disease emergence on the Internet is a promising method towards improved disease introduction risk assessment. Nevertheless, domain experts still play a central role. Our method is generic: we intend to evaluate it on data from other exotic infectious diseases and with real-time data stream. Should this evaluation be successful, the method might be routinely used by the VSI team. (Texte integral)

Human-wildlife conflicts (HWCs) have drastically increased around conservation areas in Africa in recent decades, thus undermining the peaceful cohabitation of wildlife populations and rural human settlements. Mitigation packages include HWC reporting, which is often ineffective since the information conveyed is generally scattered and useless. The booming mobile phone sector and the popular use of text messages (SMS) have provided an opportunity to assess the impact of real-time communication systems in HWC mitigation strategies. This paper presents the results of preliminary tests conducted in Mozambique and Zimbabwe with FrontlineSMS, a mobile data collection system. With sets of 52 wildlife playing cards, any wildlife events from patrol reports to HWCs were easily translated into explanatory variables listed on forms. Sending written information as text messages was hampered by IT problems linked with the use of commercial 3G USB modems. The overall system could be improved by using GPRD modems allowing a higher SMS flow and, at the informant level, by introducing ad-hoc SMS models to facilitate data capture on mobile phones. Once adopted, HWC early warning systems could be deployed at low cost.

Background and methods The appearance of bluetongue virus (BTV) in 2006 within northern Europe exposed a lack of expertise and resources available across this region to enable the accurate morphological identification of species of Culicoides Latreille biting midges, some of which are the major vectors of this pathogen. This work aims to organise extant Culicoides taxonomic knowledge into a database and to produce an interactive identification key for females of Culicoides in the Western Palaearctic (IIKC: Interactive identification key for Culicoides ). We then validated IIKC using a trial carried out by six entomologists based in this region with variable degrees of experience in identifying Culicoides . Results The current version of the key includes 98 Culicoides species with 10 morphological variants, 61 descriptors and 837 pictures and schemes. Validation was carried out by six entomologists as a blind trial with two users allocated to three classes of expertise (beginner, intermediate and advanced). Slides were identified using a median of seven steps and seven minutes and user confidence in the identification varied from 60% for failed identifications to a maximum of 80% for successful ones. By user class, the beginner group successfully identified 44.6% of slides, the intermediate 56.8% and the advanced 74.3%. Conclusions Structured as a multi-entry key, IIKC is a powerful database for the morphological identification of female Culicoides from the Western Palaearctic region. First developed for use as an interactive identification key, it was revealed to be a powerful back-up tool for training new taxonomists and to maintain expertise level. The development of tools for arthropod involvement in pathogen transmission will allow clearer insights into the ecology and dynamics of Culicoides and in turn assist in understanding arbovirus epidemiology.