Estimating the duration of RT-PCR positivity for SARS-CoV-2 from doubly interval censored data with undetected infections

TL;DR

This paper develops a Bayesian nonparametric method to estimate the distribution of SARS-CoV-2 RT-PCR positivity duration from complex censored data, providing the first such estimate in a general population.

Contribution

It introduces a novel Bayesian survival analysis approach that handles doubly interval censored data, undetected infections, and false negatives in estimating infection duration.

Findings

First estimate of SARS-CoV-2 RT-PCR positivity duration distribution in a general population

Validated methodology through simulation demonstrating robustness to model misspecification

Method applicable to other infectious disease duration estimations

Abstract

Monitoring the incidence of new infections during a pandemic is critical for an effective public health response. General population prevalence surveys for SARS-CoV-2 can provide high-quality data to estimate incidence. However, estimation relies on understanding the distribution of the duration that infections remain detectable. This study addresses this need using data from the Coronavirus Infection Survey (CIS), a long-term, longitudinal, general population survey conducted in the UK. Analyzing these data presents unique challenges, such as doubly interval censoring, undetected infections, and false negatives. We propose a Bayesian nonparametric survival analysis approach, estimating a discrete-time distribution of durations and integrating prior information derived from a complementary study. Our methodology is validated through a simulation study, including its resilience to model misspecification, and then applied to the CIS dataset. This results in the first estimate of the full duration distribution in a general population, as well as methodology that could be transferred to new contexts.