Data from: 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.,
the probability of detecting the infection in a given population), when
detectability varies among individuals due to different disease
progression. 3. We demonstrate the utility of our framework by applying it
to the recent emergence of CWD in a European population of reindeer. We
estimated apparent CWD prevalence at 1.2 % of adults in the infected
population of wild reindeer, while the true prevalence was 1.6 %. The
sensitivity estimation of the CWD surveillance was performed in an
adjacent small (~500) and a large (~10,000) reindeer population,
demonstrating low certainty of CWD absence. 4. Our method has immediate
application to the mandatory testing for CWD in EU countries commencing in
2018. Similar approaches that account for latent stages and a serial
disease progression in various tissues with a temporal pattern of
diagnostic sensitivity may enhance the estimation of the prevalence of
wildlife diseases more generally.