Pool samples to efficiently estimate pathogen prevalence dynamics

TL;DR

This paper introduces a pooling method combined with a hierarchical Bayesian model to efficiently estimate disease prevalence dynamics, reducing testing costs while maintaining accuracy in population health assessments.

Contribution

It presents a novel pooling strategy and Bayesian inference model that improve prevalence estimation efficiency and accuracy over traditional individual testing methods.

Findings

Pooling reduces testing costs significantly.

The Bayesian model provides accurate prevalence estimates.

Method performs well in synthetic and real case studies.

Abstract

Estimating the prevalence of a disease is necessary for evaluating and mitigating risks of its transmission within or between populations. Estimates that consider how prevalence changes with time provide more information about these risks but are difficult to obtain due to the necessary sampling intensity and commensurate testing costs. We propose pooling and jointly testing multiple samples to reduce testing costs and use a novel nonparametric, hierarchical Bayesian model to infer population prevalence from the pooled test results. This approach is shown to reduce uncertainty compared to individual testing at the same budget and to produce similar estimates compared to individual testing at a much higher budget through two synthetic studies and two case studies of natural infection data.