论文标题
估计COVID-19感染率：推理问题的解剖结构
Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
论文作者
论文摘要
由于缺少有关感染测试的数据和测试不完美的准确性，SARS COV-2病毒的人群感染率低于实际感染率。因此，报道以感染为条件的严重疾病率高于实际率。由于缺乏可信和信息丰富的感染率的界限，了解了Covid-19-19大流行的时间路径受到了阻碍。本文解释了使用伊利诺伊州，纽约和意大利的数据来界定这些费率并报告说明性发现的逻辑问题。我们将数据与未经测试人群中感染率的假设以及在当前情况下看起来可信的测试的准确性相结合。我们发现感染率可能大大高于报道。我们还发现，意大利的感染死亡率大大低于报道。
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
