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利用时间偏移分布分析估计 COVID-19 的病死率。

Estimating the case fatality ratio for COVID-19 using a time-shifted distribution analysis.

机构信息

Curtin University, School of Electrical Engineering, Computing and Mathematical Sciences, Perth, Australia.

出版信息

Epidemiol Infect. 2021 Jul 19;149:e197. doi: 10.1017/S0950268821001436.

Abstract

Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the CFR using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the CFR. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID-19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2% to 20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.

摘要

估算 COVID-19 的病死率(CFR)是公共卫生的一个重要方面。然而,由于疾病的时间进程不确定以及病例诊断和报告困难,在新疾病爆发的早期,准确计算 CFR 是有问题的。在这项工作中,我们提出了一种使用仅随时间推移的公共病例和死亡数据计算 CFR 的简单方法,利用了病例和死亡时间分布之间的对应关系。时间移位分布(TSD)分析生成两个感兴趣的参数:报告病例和死亡之间的延迟时间和 CFR。一旦指数增长阶段结束,这些参数就会随着时间的推移可靠地收敛。对许多国家的早期 COVID-19 爆发进行了分析,并讨论了使用超额死亡和血清流行率数据对 CFR 值进行校正以估计感染病死率(IFR)的问题。虽然不同国家的 CFR 值范围在 0.2%到 20%之间,但对于经历中度爆发的国家,IFR 的估计值大多在 0.5-0.8%之间,对于严重爆发的国家,IFR 的估计值在 1-3%之间。TSD 分析的简单性和透明度增强了其在描述新疾病以及健康和报告系统状态方面的有用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8db/8438516/3ca766019e5d/S0950268821001436_fig1.jpg

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