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Abstract：
During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (Rt), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of Rt varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals\' contact networks. While it is possible to estimate a single population-wide Rt, this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on Rt estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate Rt, made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect Rt estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate Rt. This article is part of the theme issue \'Technical challenges of modelling real-life epidemics and examples of overcoming these\'.
摘要：
在传染病爆发期间，推断表征传输的汇总统计数据对于计划干预至关重要。一个重要的度量是时间相关的再现数(Rt)，代表每个受感染的个体在其感染期间产生的二次病例的预期数量。在疫情期间，由于人群免疫力的变化和干预措施的变化等因素，Rt的值存在差异，包括那些影响个人联系网络的人。虽然可以估计一个单一的全人口Rt，这可能掩盖了人群中亚组之间传播的差异。这里,我们探讨了这种异质性对Rt估计值的影响。具体来说,我们考虑两组受感染的宿主:那些在当地人群之外感染的宿主(进口病例)，以及当地感染的人(当地病例)。我们使用贝叶斯方法来估计Rt，通过在线工具供其他人使用，这说明了这些群体中个人向前传播风险的差异。使用来自全球不同地区的COVID-19数据，我们表明，关于进口案例和本地案例之间的相对传播风险的不同假设会显著影响Rt估计，对干预有影响。这凸显了在爆发期间收集描述不同受感染宿主之间传播的异质性的数据的必要性。并在估计Rt的方法中考虑这些异质性。本文是主题问题的一部分\“建模现实生活中的流行病的技术挑战和克服这些问题的例子”。
