Incorporating testing volume into estimation of effective reproduction number dynamics

TL;DR

This paper introduces a new model that integrates testing volume into the estimation of the effective reproduction number, improving accuracy during infectious disease outbreaks by accounting for fluctuations in case data.

Contribution

The authors develop a novel model that incorporates testing volume as a covariate, enhancing the estimation of the reproduction number from case counts.

Findings

Incorporating testing volume improves estimation accuracy.

Model outperforms existing methods on simulated data.

Application to COVID-19 data demonstrates practical utility.

Abstract

Branching process inspired models are widely used to estimate the effective reproduction number -- a useful summary statistic describing an infectious disease outbreak -- using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a new model that incorporates the number of diagnostic tests as a surveillance model covariate. Using simulated data and data from the SARS-CoV-2 pandemic in California, we demonstrate that incorporating tests leads to improved performance over the state-of-the-art.