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

M. Mieskolainen, R. Bainbridge, O. Buchmueller, L. Lyons, N. Wardle
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引用次数: 1

Abstract

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 R0, 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.
估计SARS-CoV-2感染致死率的统计技术
确定新型SARS-CoV-2冠状病毒的感染致死率(IFR)是目前为应对大流行正在进行的许多实地研究的一个关键目标。IFR和基本繁殖数R0分别是描述病毒严重程度和传播力的主要流行参数。IFR还可作为估计和监测人群中受感染人数的基础,随后可用于为与公共卫生干预措施和封锁战略有关的政策决定提供信息。IFR测量值的解释需要计算置信区间。我们提出了许多与此相关的统计方法,并开发了一个反问题公式来确定校正因子,以减轻可能导致有偏差的IFR估计的时间依赖性影响。我们还审查了一些方法,将多个独立研究的IFR估计结合起来,在本说明中提供示例计算,并以摘要和“最佳实践”建议结束。开发的代码可在网上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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