Incorporating testing volume into estimation of effective reproduction number dynamics
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY(2024)
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. Keywords: Infectious Diseases; Nowcasting; Bayesian Inference; COVID-19
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Key words
Bayesian inference,COVID-19,infectious diseases,nowcasting
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