Incorporating testing volume into estimation of effective reproduction number dynamics

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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.
Funding Information
  • NIH (R01AI147336, R01AI029168)

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