PoolTestR: An R package for estimating prevalence and regression modelling with pooled samples

TL;DR

PoolTestR is an R package designed for estimating disease prevalence and performing regression analysis on pooled testing data, accommodating complex survey designs in both Bayesian and frequentist frameworks.

Contribution

It introduces a flexible R package that handles prevalence estimation and regression modeling for pooled samples, including hierarchical survey structures.

Findings

Successfully applied to synthetic MX survey data

Supports fixed- and mixed-effect models in Bayesian and frequentist frameworks

Facilitates analysis of large, complex pooled testing datasets

Abstract

Pooled testing (also known as group testing), where diagnostic tests are performed on pooled samples, has broad applications in the surveillance of diseases in animals and humans. An increasingly common use case is molecular xenomonitoring (MX), where surveillance of vector-borne diseases is conducted by capturing and testing large numbers of vectors (e.g. mosquitoes). The R package PoolTestR was developed to meet the needs of increasingly large and complex molecular xenomonitoring surveys but can be applied to analyse any data involving pooled testing. PoolTestR includes simple and flexible tools to estimate prevalence and fit fixed- and mixed-effect generalised linear models for pooled data in frequentist and Bayesian frameworks. Mixed-effect models allow users to account for the hierarchical sampling designs that are often employed in surveys, including MX. We demonstrate the utility of PoolTestR by applying it to a large synthetic dataset that emulates a MX survey with a hierarchical sampling design.