An accurate model for SARS-CoV-2 pooled RT-PCR test errors

Abstract

Pooling is a method of simultaneously testing multiple samples for the presence of pathogens. Pooling of SARS-CoV-2 tests is increasing in popularity, due to its high testing throughput. A popular pooling scheme is Dorfman pooling: test N individuals simultaneously, if the test is positive, each individual is then tested separately; otherwise, all are declared negative. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel and parsimonious probabilistic model for the outcomes of pooled tests. As an application, we analyse the false-negative rate (i.e. the probability of a negative result for an infected individual) of Dorfman pooling. We show that the false-negative rates under Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition when Dorfman pooling achieves highest throughput efficiency. We therefore urge the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests’ error rates.

DOPE: D-Optimal Pooling Experimental design with application for SARS-CoV-2 screening

“…Error rates are usually not taken into account in the development of most pooling strategies, and hence such strategies are not adaptive to varying error rates. 7,8,[17][18][19] The Bayesian formulation also allows DOPE to readily incorporate any prior knowledge obtained with regards to infection probabilities of different sub-populations. Although we have only considered connectivity of sub-populations in this manuscript, other covariates can potentially also be incorporated (eg, prior data of the likelihood of infection based on symptoms, age groups, etc).…”

“…9,11 Current studies of pooling in the context of SARS-CoV-2 also employ similar assumptions. 19,20 Specifically, these studies assume the same probability of a negative result for a pool with either a single or multiple samples originating from infected individuals. However, in a previous study, we have shown that this assumption does not align with experimen-tal data.…”

Refuting Causal Relations in Epidemiological Time Series

“…neuroscience [5], geoscience [6], and infectious disease epidemiology [7]. The latter field has particularly grown in interest since the COVID-19 pandemic, as understanding causal relations during outbreaks may facilitate implementations of interventions such as vaccination [8][9][10][11], concentrated testing efforts [12][13][14][15][16] and other non-pharmaceutical interventions [17].…”