Estimating Disease Prevalence Using Inverse Binomial Pooled Testing

• Published in: Journal of agricultural, biological, and environmental statistics Vol. 16; no. 1; pp. 70 - 87
• Main Authors: Pritchard, Nicholas A., Tebbs, Joshua M.
• Format: Journal Article
• Language: English
• Published: New York International Biometric Society and the American Statistical Association 01.03.2011; Springer-Verlag; American Statistical Association; International Biometric Society; Springer
• Subjects: Agriculture; Agronomy. Soil science and plant productions; Analysis; Binomials; Biological and medical sciences; Biometrics, statistics, experimental designs, modeling, agricultural computer applications; Biostatistics; Confidence interval; Disease models; Disease prevalence rates; Estimation bias; Estimators; Foot and mouth disease; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; Health aspects; Health Sciences; Infection; Infections; Mathematics and Statistics; Maximum likelihood estimation; Medical research; Medicine; Medicine, Experimental; Microbiology; Monitoring/Environmental Analysis; Point estimators; Prevalence studies (Epidemiology); Replicative cycle, interference, host-virus relations, pathogenicity, miscellaneous strains; Risk assessment; Statistics; Statistics for Life Sciences; Virology; West Nile fever; Foot and mouth disease; Prevalence; Group testing; West Nile virus; Maximum likelihood; Geometric distribution; Negative binomial distribution; Virus; Disease
• ISSN: 1085-7117; 1537-2693
• DOI: 10.1007/s13253-010-0036-4

Monitoring populations of hosts as well as insect vectors is an important part of agricultural and public health risk assessment. In applications where pathogen prevalence is likely low, it is common to test pools of subjects for the presence of infection, rather than to test subjects individually. This technique is known as pooled (group) testing. In this paper, we revisit the problem of estimating the population prevalence p from pooled testing, but we consider applications where inverse binomial sampling is used. Our work is unlike previous research in pooled testing, which has largely assumed a binomial model. Inverse sampling is natural to implement when there is a need to report estimates early on in the data collection process and has been used in individual testing applications when disease incidence is low. We consider point and interval estimation procedures for p in this new pooled testing setting, and we use example data sets from the literature to describe and to illustrate our methods.