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Pooled testing (also known as group testing), where diagnostic tests areperformed on pooled samples, has broad applications in the surveillance ofdiseases in animals and humans. An increasingly common use case is molecularxenomonitoring (MX), where surveillance of vector-borne diseases is conductedby capturing and testing large numbers of vectors (e.g. mosquitoes). The Rpackage PoolTestR was developed to meet the needs of increasingly large andcomplex molecular xenomonitoring surveys but can be applied to analyse any datainvolving pooled testing. PoolTestR includes simple and flexible tools toestimate prevalence and fit fixed- and mixed-effect generalised linear modelsfor pooled data in frequentist and Bayesian frameworks. Mixed-effect modelsallow users to account for the hierarchical sampling designs that are oftenemployed in surveys, including MX. We demonstrate the utility of PoolTestR byapplying it to a large synthetic dataset that emulates a MX survey with ahierarchical sampling design.

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