A rationale to unify measurements of effectiveness for animal health surveillance.

Abstract

Surveillance systems produce data which, once analysed and interpreted, support decisions regarding disease management. While several performance measures for surveillance are in use, no theoretical framework has been proposed yet with a rationale for defining and estimating effectiveness measures of surveillance systems in a generic way. An effective surveillance system is a system whose data collection, analysis and interpretation processes lead to decisions that are appropriate given the true disease status of the target population. Accordingly, we developed a framework accounting for sampling, testing and data interpretation processes, to depict in a probabilistic way the direction and magnitude of the discrepancy between "decisions that would be made if the true state of a population was known" and the "decisions that are actually made upon the analysis and interpretation of surveillance data". The proposed framework provides a theoretical basis for standardised quantitative evaluation of the effectiveness of surveillance systems. We illustrate such approaches using hypothetical surveillance systems aimed at monitoring the prevalence of an endemic disease and at detecting an emerging disease as early as possible and with an empirical case study on a passive surveillance system aiming at detecting cases of Highly Pathogenic Avian Influenza cases in Vietnamese poultry.