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 particular 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：在传染病爆发期间，公共卫生决策者需要实时监测疾病传播以做出快速且明智的响应。在此类场景下，传播动态的关键指标为瞬时时变再生数$R_t$。基于感染时间模型的估计依赖于病例通报数据及目标人群的序列间隔分布。然而，实际病例通报数据可能因病例报告变异产生测量误差，而现有序列间隔估计常源于非代表性人群的研究。我们提出一种新型数据驱动方法，该方法能处理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计流程。此外，我们提供实用工具以实现测量误差模式的自动识别，并在误差无法充分校正时进行判定。通过模拟多种爆发场景，我们揭示了忽略这些实际问题的估计方法所引入的潜在偏倚。最后，我们应用该方法分析COVID-19大流行数据，旨在估计传播强度并探究社交距离、温度与传播之间的关联。