论文标题
估计流行病学过程基础的时变繁殖数：COVID-19大流行的新统计工具
Estimation of time-varying reproduction numbers underlying epidemiological processes: a new statistical tool for the COVID-19 pandemic
论文作者
论文摘要
冠状病毒大流行已迅速发展为前所未有的危机。易感感染的（SIR）模型及其变体已用于建模大流行。但是，经典模型中的时间无关参数可能不会捕获受流行病各个阶段采取的病毒遏制策略控制的动态传播和去除过程。此外，很少有模型解释了报告案件的可能不准确性。我们提出了一个具有时间依赖性传播和去除速率的泊松模型，以说明报告和估计时间依赖性疾病繁殖数的可能随机错误，该数字可用于评估病毒控制策略的有效性。我们采用我们的方法来研究几个受影响的严重影响国家的大流行，并分析和预测冠状病毒的不断发展的传播。我们已经开发了一个交互式Web应用程序，以促进读者对我们的方法的使用。
The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, very few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may be used to assess the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers' use of our method.
