A Bayesian zero-truncated approach for analysing capture–recapture count data from classical scrapie surveillance in France

Abstract

Capture–recapture (CR) methods are used to study populations that are monitored with imperfect observation processes. They have recently been applied to the monitoring of animal diseases to evaluate the number of infected units that remain undetected by the surveillance system. This paper proposes three Bayesian models to estimate the total number of scrapie-infected holdings in France from CR count data obtained from the French classical scrapie surveillance programme. We fitted two zero-truncated Poisson (ZTP) models (with and without holding size as a covariate) and a zero-truncated negative binomial (ZTNB) model to the 2006 national surveillance count dataset. We detected a large amount of heterogeneity in the count data, making the use of the simple ZTP model inappropriate. However, including holding size as a covariate did not bring any significant improvement over the simple ZTP model. The ZTNB model proved to be the best model, giving an estimation of 535 (CI95% 401–796) infected and detectable sheep holdings in 2006, although only 141 were effectively detected, resulting in a holding-level prevalence of 4.4‰ (CI95% 3.2–6.3) and a sensitivity of holding-level surveillance of 26% (CI95% 18–35). The main limitation of the present study was the small amount of data collected during the surveillance programme. It was therefore not possible to build complex models that would allow depicting more accurately the epidemiological and detection processes that generate the surveillance data. We discuss the perspectives of capture–recapture count models in the context of animal disease surveillance.