论文标题

估计SARS-COV-2感染率的统计技术

Statistical techniques to estimate the SARS-CoV-2 infection fatality rate

论文作者

Mieskolainen, Mikael, Bainbridge, Robert, Buchmueller, Oliver, Lyons, Louis, Wardle, Nicholas

论文摘要

新型SARS-COV-2冠状病毒的感染死亡率(IFR)的确定是许多目前正在响应大流行的现场研究的关键目的。 IFR与基本复制数$ r_0 $一起是分别描述病毒严重性和可传播性的主要流行参数。 IFR还可以用作估计和监视人群中受感染者数量的基础,后来可以用来为与公共卫生干预措施和锁定策略有关的政策决策提供信息。 IFR测量的解释需要计算置信区间。我们提出了许多在这种情况下相关的统计方法,并开发出反相关的问题,以确定校正因子以减轻时间依赖性效应,从而导致IFR估计。我们还回顾了许多方法来结合多个独立研究的IFR估计,在本注释中提供示例计算,并以摘要和“最佳实践”建议得出结论。开发的代码可在线提供。

The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number $R_0$, are the main epidemic parameters describing severity and transmissibility of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and "best practice" recommendations. The developed code is available online.

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