Abstract: Capture-recapture methods are generally used to describe populations when observation processes are imperfect. In the context of disease surveillance, they can be used simply for estimating the total size of the populations infected by a given pathogen, and hence, estimating quantitatively the sensitivity of the surveillance of this pathogen. Although they are widely used in public health, capture-recapture methods have been barely applied to the surveillance of animal diseases. Because the context of animal health is quite different from the context of public health, some questions remain concerning the benefits and the limitations of such methods for estimating the sensitivity of surveillance systems in animal health. For answering this research question, we identified four animal disease surveillance systems that differ by their complexity, their efficiency and their disease of interest. We selected the surveillance of foot-and-mouth disease in Cambodia, of highly pathogenic avian influenza (H5N1) in Egypt and Thailand, and of classical scrapie in France. For each surveillance system, we identified the most appropriate capture-recapture approach (respectively the two-source approach, the three-source approach, the zero-inflated approach and the zero-truncated approach). For each application, we estimated the total number of infected epidemiological units that remained undetected, and accessed an estimation of the sensitivity of each surveillance system. From these applications, we highlighted that these models are relatively easy to implement, and that they allow with little additional income to get an unbiased representation of the disease burden in a population when it is monitored with imperfect surveillance processes. However, it seems that practices used for the monitoring and controlling animal diseases tend to limit the applicability of these methods at the scale of the monitored unit. As a consequence, it is often necessary to enlarge the epidemiological unit (holding, commune, etc…) so that it comprises several monitored units. This enlargement introduces new constraints (abundance induced heterogeneity), that need to be taken into account in order not to bias final estimates. Finally, this work proposes surveillance perspectives for descriptive epidemiology, and methodological perspectives in statistics and modeling as well.
