Contributors: Service données, analyses, systèmes d'information, outils (OFB SOAD); Direction surveillance, évaluation,données (OFB - DSUED); Office français de la biodiversité (OFB)-Office français de la biodiversité (OFB); Service santé de la faune et fonctionnement des écosystèmes agricoles (OFB Service Santé Agri); Direction de la recherche et de l’appui scientifique (OFB - DRAS); Direction Régionale de l'Alimentation, de l'Agriculture et de la Forêt Aquitaine (DRAAF Aquitaine); Laboratoire de la rage et de la faune sauvage de Nancy (LRFSN); Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES); Direction Régionale de l'Alimentation, de l'Agriculture et de la Forêt de la région Bourgogne-Franche-Comté (DRAAF BFC)
نبذة مختصرة : The Sylvatub system is a national surveillance programme set up in 2011 in France to monitor in wild species the infection by Mycobacterium bovis , the main aetiologic agent for bovine tuberculosis. This participatory programme, involving both national and local stakeholders, allowed to monitor from 2013 to 2019 the progression of the infection in three badger populations located in clusters covering between 3222 km 2 and 7698 km 2 . In each cluster, badgers were trapped and tested for M. bovis . Our first aim was to describe the dynamics of the infection in these clusters. We developed a Bayesian model of the prevalence accounting for the spatial structure of the cases, the imperfect and variable sensitivity of the diagnostic tests, and the correlation of the infection status of badgers of the same commune caused by local factors (social structure, proximity to infected farms, etc.). This model revealed a prevalence increasing with time in one cluster (Dordogne/Charentes), decreasing in the second cluster (Burgundy), and remaining stable in the third cluster (Bearn). In all clusters, the infection was strongly spatially structured, whereas the mean correlation between the infection status of two animals trapped in the same commune was negligible. Our second aim was to develop indicators for the monitoring of the M. bovis infection by stakeholders of the programme. We used the fitted model to estimate, in each cluster, (i) the mean prevalence level at mid-period, and (ii) the proportion of the badger population becoming infected in one year. We then derived two indicators of these two key quantities from a much simpler regression model, and we showed how these two indicators could be easily used to monitor the infection in the three clusters. We showed with simulations that these two simpler indicators were a good approximation of these key quantities.
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