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.