  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.
