Sample Size for Estimating Disease Prevalence in Free-Ranging Wildlife Populations: A Bayesian Modeling Approach

James G. Booth (), Brenda J. Hanley, Florian H. Hodel, Christopher S. Jennelle, Joseph Guinness, Cara E. Them, Corey I. Mitchell, Md Sohel Ahmed and Krysten L. Schuler ()
Additional contact information
James G. Booth: Cornell University
Brenda J. Hanley: Cornell University
Florian H. Hodel: Michigan State University
Christopher S. Jennelle: Nongame Wildlife Program
Joseph Guinness: Cornell University
Cara E. Them: Cara Them Consulting, LLC
Corey I. Mitchell: Desert Centered Ecology, LLC
Md Sohel Ahmed: Cornell University
Krysten L. Schuler: Cornell University

Journal of Agricultural, Biological and Environmental Statistics, 2024, vol. 29, issue 3, No 2, 438-454

Abstract: Abstract A two-parameter model and a Bayesian statistical framework are proposed for estimating prevalence and determining sample size requirements for detecting disease in free-ranging wildlife. Current approaches tend to rely on random (ideal) sampling conditions or on highly specialized computer simulations. The model-based approach presented here can accommodate a range of different sampling schemes and allows for complications that arise in the free-ranging wildlife setting including the natural clustering of individuals on the landscape and correlation in disease status from transmission among individuals. Correlation between individuals and the sampling scheme have important consequences for the sample size requirements. Specifically, high within cluster correlations in disease status can reduce sample size requirements by reducing the effective population size. However, disproportionate sampling of small subsets of subjects from the greater target population, combined with high correlation of disease status, tends to inflate sample size requirements, because it increases the likelihood of sampling multiple animals within the same highly correlated clusters, resulting in little additional information gleaned from those samples. Our results are consistent with those generated using both previously established approaches and extend their ability to adapt to additional biological, epidemiological, or societal sampling complications specific to wildlife health.

Keywords: Beta-binomial; Correlation; Sampling design; Wildlife disease surveillance; Wildlife health (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13253-023-00578-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jagbes:v:29:y:2024:i:3:d:10.1007_s13253-023-00578-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/13253

DOI: 10.1007/s13253-023-00578-7

Access Statistics for this article

Journal of Agricultural, Biological and Environmental Statistics is currently edited by Stephen Buckland

More articles in Journal of Agricultural, Biological and Environmental Statistics from Springer, The International Biometric Society, American Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().