论文标题
我们可以从测试和医院数据中了解SARS-COV-2患病率什么？
What can we learn about SARS-CoV-2 prevalence from testing and hospital data?
论文作者
论文摘要
很难测量普通人群中活性SARS-COV-2感染的患病率，因为对人群的少量和非随机段进行了测试。但是，由于非循环原因，他们被送往医院的人以很高的速度进行了测试，即使他们似乎没有感染风险较高。该亚人口可能会提供有关普通人群患病率的有价值的证据。我们使用印第安纳州的住院记录中的印第安纳州数据与SARS-COV-2病毒学测试有关的医院住院记录中的数据，我们估计了普通人群中病毒患病率的上限和下限。非卵路医院人口的测试频率是普通人群的五十倍，在患病率上产生了更严格的范围。我们提供和测试条件，在该条件下，这种非杂化住院约束对普通人群有效的条件。临床测试数据和医院记录的结合可能包含有关流行病状态的更多信息，而不是以前所欣赏。在许多其他州，我们计算的印第安纳州的界限可以以相对较低的成本建造。
Measuring the prevalence of active SARS-CoV-2 infections in the general population is difficult because tests are conducted on a small and non-random segment of the population. However, people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population, yielding much tighter bounds on prevalence. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states.
