Bayesian modelling of hunting data may improve the understanding of host–parasite systems: Wild boar diseases and vaccination as an example

摘要: Abstract Wildlife diseases are often studied using hunting data. In such studies, inferences about made by comparing raw disease prevalence levels, ignoring complications like stochasticity in recruitment. We carried out a field trial to study the effectiveness of oral vaccination wild boar (Sus scrofa) against classical swine fever (CSF) Vosges mountains (Northeastern France) for 3 years (2008–2010). Since August 2004, hunters had three sessions per year spring, summer and autumn. During our period, we determined whether each hunted area was immunized or not CSF. used Bayesian approach model changes proportion vaccinated animals population young (i.e., 12 months old). This allowed disentangle effects birth peaks (leading decrease) both natural infection an increase) on this proportion. thus inferred, at individual level, probability that non-immunized animal became after particular session. There high between-year variability vaccination: observed patterns were similar 2008 2010, but 2009 characterized overall greater vaccination. Within year, spring session more effective than autumn session, probably because higher food availability render places less attractive animals. The rather low summer, except 2009, age identification error year. also highlighted immunisation occurring outside periods, which suggests either presence CSF virus area, consumption vaccine sessions. Finally, spatial indeed strongly related distribution forests area. highlights optimal effort 1.25 km2 maximize immune