During an infectious disease outbreak, public health decision-makers require real-time monitoring of disease transmission to respond quickly and intelligently. In these settings, a key measure of transmission is the instantaneous time-varying reproduction number, $R_t$. Estimation of this number using a Time-Since-Infection model relies on case-notification data and the distribution of the serial interval on the target population. However, in practice, case-notification data may contain measurement error due to variation in case reporting while available serial interval estimates may come from studies on non-representative populations. We propose a new data-driven method that accounts for certain forms of case-reporting measurement error and can incorporate multiple partially representative serial interval estimates into the transmission estimation process. In addition, we provide practical tools for automatically identifying measurement error patterns and determining when measurement error may not be adequately accounted for. We illustrate the potential bias undertaken by methods that ignore these practical concerns through a variety of simulated outbreaks. We then demonstrate the use of our method on data from the COVID-19 pandemic to estimate transmission and explore the relationships between social distancing, temperature, and transmission.

翻译：在传染病暴发期间，公共卫生决策者需要实时监测疾病传播态势，以便做出快速且明智的应对。在此类场景中，关键传播指标为瞬时时变再生数$R_t$。基于感染时间模型的该数值估计依赖于病例报告数据以及目标人群的序列间隔分布。然而实际应用中，病例报告数据可能因报告变异而存在测量误差，同时可获取的序列间隔估计可能源自非代表性人群的研究。我们提出了一种新型数据驱动方法，该方法能应对特定形式的病例报告测量误差，并可将多个部分代表性的序列间隔估计纳入传播估算流程。此外，我们提供了自动识别测量误差模式及判定测量误差未充分校正情形的实用工具。通过多种模拟暴发场景，我们揭示了忽视这些实际问题的方法可能产生的潜在偏倚。最后，我们利用COVID-19大流行数据演示了该方法的应用，以估算传播强度并探索社交距离、温度与传播之间的关联。