Incorporating testing volume into estimation of effective reproduction number dynamics
提出一种将诊断检测数量作为协变量的新模型，用于更准确地估计有效再生数，在模拟数据和加州新冠数据中表现优于现有方法。
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.