A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems

1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.

2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).

3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained fr...