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:在传染病爆发期间,公共卫生决策者需要实时监测疾病传播情况,以便迅速和明智地作出反应。在这些环境中,传播的一个关键尺度是瞬时时间变化的复制编号($R_t$)。使用时间-感染模型估计这个数字取决于病例通知数据和目标人群的序列间隔分布。然而,在实际中,病例通知数据可能包含由于报告情况差异造成的测量错误,而现有序列间隔估计数可能来自关于非代表性人口的研究。我们建议采用新的数据驱动方法,说明某些形式的个案报告计量错误,并将多个部分具有代表性的序列间隔估计数纳入传输估计过程。此外,我们提供实用工具,自动确定测量错误模式,并确定在何时可能无法充分计算计量错误。我们说明通过各种模拟爆发而忽略这些实际关切的方法可能存在偏差。我们随后展示了我们使用COVID-19流行病数据的方法来估计传播和探索社会分化、温度和传输之间的关系。