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.

翻译：受分支过程启发的模型被广泛用于通过新增病例数估计有效再生数——一种描述传染病暴发概况的有用统计量。病例数据是再生数变化的实时指标，但由于病例数会受与新增感染数量无关的因素影响而波动，使用这些数据颇具挑战性。我们开发了一种新模型，将诊断检测数量作为监测模型协变量纳入分析。利用模拟数据及加利福尼亚州SARS-CoV-2大流行期间的实证数据，我们证明纳入检测数据后，模型性能较当前最优方法有所提升。