PoolTestR: An R package for estimating prevalence and regression modelling for molecular xenomonitoring and other applications with pooled samples. (November 2021)

• Record Type: Journal Article
• Authors: McLure, Angus; O'Neill, Ben; Mayfield, Helen; Lau, Colleen; McPherson, Brady
• Is Part Of: Environmental modelling & software. Volume 145(2021)
• Publication Date: 2021-11
• Subjects: R -- Group testing -- Molecular xenomonitoring -- Open source software -- Pooled testing -- Mixed effect regression; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105158
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: PoolTestR is an R package for analysing pooled testing (group testing) data. PoolTestR can estimate prevalence and fit flexible mixed-effect regression models. PoolTestR is flexible, extensible, and easy-to-use with small or large datasets. PoolTestR is suited for hierarchical sampling designs, e. g molecular xenomonitoring.