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Branching process inspired models are widely used to estimate the effectivereproduction number -- a useful summary statistic describing an infectiousdisease outbreak -- using counts of new cases. Case data is a real-timeindicator of changes in the reproduction number, but is challenging to workwith because cases fluctuate due to factors unrelated to the number of newinfections. We develop a new model that incorporates the number of diagnostictests as a surveillance model covariate. Using simulated data and data from theSARS-CoV-2 pandemic in California, we demonstrate that incorporating testsleads to improved performance over the state-of-the-art.

Incorporating testing volume into estimation of effective reproduction number dynamics