摘要翻译：
测量普通人群中活动性新型冠状病毒感染的流行率是困难的，因为测试是在人口中的一小部分和非随机部分进行的。然而，因非Covid原因住院的人接受检测的比例非常高，尽管他们似乎没有增加感染的风险。这一亚人群可能提供关于普通人群流行率的宝贵证据。我们使用印第安纳州与新型冠状病毒病毒学测试有关的医院住院记录数据，在对谁接受测试的薄弱假设下，估计了普通人群和非Covid医院患者人群中病毒流行率的上下界限。非Covid医院人群接受检测的频率是普通人群的50倍，对患病率的限制要严格得多。我们提供并测试这种非Covid住院约束对普通人群有效的条件。临床检测数据和医院记录的结合可能包含比以前更多的关于疫情状态的信息。我们为印第安纳州计算的界限可以在许多其他州以相对较低的成本构建。
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英文标题：
《What can we learn about SARS-CoV-2 prevalence from testing and hospital
  data?》
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作者：
Daniel W. Sacks, Nir Menachemi, Peter Embi, Coady Wing
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2008.00298