摘要翻译：
我从检测率和检测产量的信息中提出了新的感染流行的部分识别界限。该方法利用用户指定的边界(i)测试精度和(ii)测试的目标范围，形式化为对真实感染状态对获得测试的几率比的影响的限制，从而可嵌入到logit规范中。令人振奋的应用是针对新冠肺炎疫情，但这种策略在其他地方也可能有用。根据疫情早期阶段的数据评估，即使是最弱的新界限也有合理的信息。值得注意的是，与当时广泛报道的猜测相反，他们认为到4月中旬，意大利的感染死亡率远高于流感死亡率。
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英文标题：
《Bounding Infection Prevalence by Bounding Selectivity and Accuracy of
  Tests: With Application to Early COVID-19》
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作者：
J\"org Stoye
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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：
  I propose novel partial identification bounds on infection prevalence from information on test rate and test yield. The approach utilizes user-specified bounds on (i) test accuracy and (ii) the extent to which tests are targeted, formalized as restriction on the effect of true infection status on the odds ratio of getting tested and thereby embeddable in logit specifications. The motivating application is to the COVID-19 pandemic but the strategy may also be useful elsewhere.   Evaluated on data from the pandemic's early stage, even the weakest of the novel bounds are reasonably informative. Notably, and in contrast to speculations that were widely reported at the time, they place the infection fatality rate for Italy well above the one of influenza by mid-April.
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PDF链接：
https://arxiv.org/pdf/2008.06178