How Deadly is Covid-19? Understanding the Difficulties with Estimation of its Fatality Rate

A. Atkeson
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引用次数: 98

Abstract

To understand how best to combat COVID-19, we must understand how deadly is the disease. There is a substantial debate in the epidemiological lit- erature as to whether the fatality rate is 1% or 0.1% or somewhere in between. In this note, I use an SIR model to examine why it is difficult to estimate the fatality rate from the disease and how long we might have to wait to resolve this question absent a large-scale randomized testing program. I focus on un- certainty over the joint distribution of the fatality rate and the initial number of active cases at the start of the epidemic around January 15, 2020. I show how the model with a high initial number of active cases and a low fatality rate gives the same predictions for the evolution of the number of deaths in the early stages of the pandemic as the same model with a low initial number of active cases and a high fatality rate. The problem of distinguishing these two parameterizations of the model becomes more severe in the presence of effective mitigation measures. As discussed by many, this uncertainty could be resolved now with large-scale randomized testing.
Covid-19有多致命?了解估算其致死率的困难
要了解如何最好地抗击COVID-19,我们必须了解这种疾病的致命性。在流行病学文献中,关于致死率是1%还是0.1%或介于两者之间存在大量争论。在本文中,我使用SIR模型来检验为什么难以估计该疾病的致死率,以及在没有大规模随机测试计划的情况下,我们可能需要等待多长时间才能解决这个问题。我重点关注2020年1月15日左右疫情开始时病死率和初始活跃病例数联合分布的不确定性。我展示了具有高初始活跃病例数和低死亡率的模型如何与具有低初始活跃病例数和高死亡率的相同模型对大流行早期阶段死亡人数的演变给出了相同的预测。在存在有效缓解措施的情况下,区分模型的这两种参数化的问题变得更加严重。正如许多人所讨论的那样,这种不确定性现在可以通过大规模随机测试来解决。
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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