Title: Some results from my research career and recent work on a sample size problem for free-ranging wildlife.

Abstract: I will begin by briefly discussing some aspects of my Ph.D. dissertation and research at the University of Kentucky, which focused on an estimation technique for Markov processes. I will then review results from three papers I have co-authored involving, respectively, an epidemic model, the double bootstrap and generalized linear mixed models.

The main part of my talk concerns a recently proposed two-parameter model and a Bayesian statistical framework for estimating prevalence and determining sample size requirements for detecting disease in free-ranging wildlife. Well-known sample size formulas assume that animals contract disease independently, an assumption that is unlikely to hold in many practical settings. The presence of correlation has implications for sample size requirements and sampling design.

Booth et al. (2023) “Sample size for estimating disease prevalence in free-ranging wildlife populations: a Bayesian modeling approach”, Journal of Agricultural, Biological and Environmental Statistics, 29(3):438-454

Booth et al. (2025) “Management agencies can leverage animal social structure for wildlife disease surveillance”, Journal of Wildlife Diseases, 61(2):472-476