JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1191 10.6339/25-JDS1191 Statistical Data Science Estimating Disease Prevalence from Preferentially Sampled, Pooled Data https://orcid.org/0009-0002-3936-1332 PollockClinton P.clintonpollock@msu.montana.edu1∗ https://orcid.org/0000-0003-1176-4965 HoeghAndrew1 https://orcid.org/0000-0002-6426-940X IrvineKathryn M.2 https://orcid.org/0000-0002-3045-4017 de WitLuz A.3 https://orcid.org/0000-0002-9640-0695 ReichertBrian E.4 Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA Northern Rocky Mountain Science Center, U.S. Geological Survey, Bozeman, MT, USA Bat Conservational International, Austin, TX, USA Fort Collins Science Center, U.S. Geological Survey, Fort Collins, CO, USA Corresponding author. Email: clintonpollock@msu.montana.edu. 20251162025233542559311020243052025 2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2025 Open access article under the CC BY license.

After the onset of the COVID-19 pandemic, scientific interest in coronaviruses endemic in animal populations has increased dramatically. However, investigating the prevalence of disease in animal populations across the landscape, which requires finding and capturing animals can be difficult. Spatial random sampling over a grid could be extremely inefficient because animals can be hard to locate, and the total number of samples may be small. Alternatively, preferential sampling, using existing knowledge to inform sample location, can guarantee larger numbers of samples, but estimates derived from this sampling scheme may exhibit bias if there is a relationship between higher probability sampling locations and the disease prevalence. Sample specimens are commonly grouped and tested in pools which can also be an added challenge when combined with preferential sampling. Here we present a Bayesian method for estimating disease prevalence with preferential sampling in pooled presence-absence data motivated by estimating factors related to coronavirus infection among Mexican free-tailed bats (Tadarida brasiliensis) in California. We demonstrate the efficacy of our approach in a simulation study, where a naive model, not accounting for preferential sampling, returns biased estimates of parameter values; however, our model returns unbiased results regardless of the degree of preferential sampling. Our model framework is then applied to data from California to estimate factors related to coronavirus prevalence. After accounting for preferential sampling impacts, our model suggests small prevalence differences between male and female bats.

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