Bayesian inference for disease prevalence using negative binomial group testing.
Group testing, also known as pooled testing, and inverse sampling are both widely used methods of data collection when the goal is to estimate a small proportion. Taking a Bayesian approach, we consider the new problem of estimating disease prevalence from group testing when inverse (negative binomial) sampling is used. Using different distributions to incorporate prior knowledge of disease incidence and different loss functions, we derive closed form expressions for posterior distributions and resulting point and credible interval estimators. We then evaluate our new estimators, on Bayesian and classical grounds, and apply our methods to a West Nile Virus data set.
Department of Mathematics and Statistics, Coastal Carolina University, Conway, SC 29528, USA.
Nicholas A
NA
Pritchard
Joshua M
JM
Tebbs
D000818:Animals; D001499:Bayes Theorem; D055877:Biosurveillance / Q000379:methods; D009033:Culicidae; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D015233:Models, Statistical; D011156:Population Density; D015995:Prevalence; D018401:Sample Size; D014901:West Nile Fever / Q000453:epidemiology; D014902:West Nile virus
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