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

  title={PoolTestR: An R package for estimating prevalence and regression modelling with pooled samples.},
  author={Angus McLure and Ben O'Neill and Helen J. Mayfield and Colleen L. Lau and Brady McPherson},
  journal={arXiv: Computation},

Figures from this paper

Evaluating Molecular Xenomonitoring as a Tool for Lymphatic Filariasis Surveillance in Samoa, 2018–2019

This study compared prevalence of infected mosquitoes pre- and post-MDA (2018 and 2019) in 35 primary sampling units (PSUs) in Samoa, and investigated associations between the presence of PCR-positive mosquitoes and Ag-positive humans.

A One Health view of the West Nile virus outbreak in Andalusia (Spain) in 2020

Serology of wild birds confirmed WNV circulation inside the affected villages, that transmission to humans also occurred in urban settings and suggests that virus circulation was geographically more widespread than disease cases in humans or horses may indicate.

Dynamics of the Emerging Genogroup of Infectious Bursal Disease Virus Infection in Broiler Farms in South Korea: A Nationwide Study

Infectious bursal disease (IBD), caused by IBD virus (IBDV), threatens the health of the poultry industry. Recently, a subtype of genogroup (G) 2 IBDV named G2d has brought a new threat to the

High prevalence of Schistosoma mansoni infection and stunting among school age children in communities along the Albert-Nile, Northern Uganda: A cross sectional study

High prevalence of S. mansoni infection in the region calls for more frequent mass drug administration with praziquantel and there is a need for improved nutrition among the children in the area.



Evaluation of a Frequentist Hierarchical Model to Estimate Prevalence When Sampling from a Large Geographic Area Using Pool Screening

A frequentist Bernoulli-Beta hierarchical model is presented to relax the constant prevalence assumption underlying the traditional prevalence estimation approach based on pooled data, which is called into question when sampling from a large geographic area.

Important experimental parameters for determining infection rates in arthropod vectors using pool screening approaches.

It is shown that, under most conditions in which one would want to use group testing, most of the error results from sampling and not the pooling process, and the meaning of confidence intervals associated with prevalence estimates and the appropriate interpretation of these intervals are discussed.

Confidence intervals for proportions estimated by group testing with groups of unequal size

Group testing, in which units are pooled together and tested as a group for the presence of an attribute, has been used in many fields of study, including blood testing, plant disease assessment,

Assessment of arbovirus vector infection rates using variable size pooling

It is concluded that variable pool size coupled with MLE is critical for accurate estimates of mosquito infection rates in WNV epidemic seasons.

Pooled Testing for Expanding COVID-19 Mass Surveillance

Pool testing is shown to be able to detect positive samples with sufficient accuracy and can easily be used with existing equipment and personnel for population-wide screening.

Using group testing to estimate a proportion, and to test the binomial model.

Group testing has been extensively studied as an efficient way to classify units as defective or satisfactory when the proportion (p) of defectives is small. It can also be used to estimate p, often

Estimating prevalence by group testing using generalized linear models.

A method is described for estimating prevalence by group testing using generalized linear models and existing methodology to correct for overdispersion using quasi-likelihoods is applied to the group testing model.

Estimation of infection rates in population of organisms using pools of variable size.

A method is given for estimating the infection rate in a population of organisms when variably sized sample pools are analyzed, a common situation in practice but not one which can be dealt with by

Fitting Linear Mixed-Effects Models Using lme4

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most