论文标题
以统计和数学模型来了解传染病暴发：Covid-19作为一个例子
Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
论文作者
论文摘要
在传染病爆发中，基本动力学的数据和复杂性偏见在数学上对爆发和设计政策进行建模时构成了重大挑战。除了对Covid-19的持续反应，我们提供了一个统计和数学模型的工具包，除了简单的Sir-Type微分方程模型，用于分析爆发和评估干预措施的早期阶段。特别是，我们关注数据中存在已知偏差的参数估计，以及在封闭的亚群（例如家庭和养老院）中非药物干预措施的影响。我们通过将这些方法应用于Covid-19-19大流行来说明这些方法。
During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
