Bayesian Prevalence Estimation from Pooled and Individual Data

TL;DR

This paper introduces an analytical method for estimating disease prevalence using combined pooled and individual testing data, providing a more efficient alternative to Monte Carlo methods.

Contribution

It presents a novel analytical solution for prevalence estimation from mixed testing data, improving accuracy and computational efficiency over existing Monte Carlo approaches.

Findings

Analytical solution accurately estimates prevalence across various sampling conditions.

Method performs well with different test sensitivities and specificities.

Provides insights into optimal sampling strategies for disease prevalence studies.

Abstract

Pooled and individual disease testing are common methods for determining the population prevalences of diseases. Recently, researchers have used Monte Carlo Markov Chain methods to estimate population prevalence from the combined streams of these two types of testing data. We propose an analytical solution for estimating population prevalence from combined individual and pooled binary sampling data. We also use simulated sampling data to characterize these posterior distributions under a variety of sampling conditions, including a range of true prevalences, variable numbers of pooled and individual tests, variable number of individual samples per pooled sample, and a range of values for test sensitivity and specificity.