A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids

1. Surveillance of wildlife diseases is logistically difficult, and imperfect detection is a recurrent challenge for disease estimation. Using citizen science can increase sample sizes, but it is associated with a cost in terms of the anatomical type and quality of the sample. Additionally, biological tissue samples from remote areas lose quality due to autolysis. These challenges are faced in the case of emerging Chronic Wasting Disease (CWD) in cervids. 2. Here, we develop a stochastic scenario tree model of diagnostic sensitivity, allowing for a mixture of tissue sample types (lymph nodes and brain) and qualities while accounting for different detection probabilities during the CWD infection, lasting 2-3 years. We apply the diagnostic sensitivity in a Bayesian framework, enabling estimation of age-class-specific true prevalence, including the prevalence in latent, recently infected stages. We provide a simulation framework to estimate the sensitivity of the surveillance system (i.e.,...