通过限定检测选择性和检测准确性来界定感染率：应用于COVID-19早期
Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-19
AI总结：
本文提出利用检测准确性与检测选择性约束构建感染率的部分识别界，并在COVID-19早期数据上表明其可有效推断意大利感染病死率高于流感。
摘要：
我利用检测率和检测产出方面的信息，提出了关于感染率的新型部分识别界。该方法采用用户指定的两类界：（i）检测准确性，以及（ii）检测被定向投放的程度；后者被形式化为对真实感染状态在接受检测的比值比上的效应所施加的限制，因此可嵌入 logit 设定。其推动性应用是 COVID-19 疫情，但该策略在其他场景中也可能有用。基于疫情早期阶段的数据进行评估，即使是这些新界中最弱的界也具有合理的信息量。值得注意的是，与当时广泛报道的猜测相反，这些界表明，到4月中旬，意大利的感染病死率远高于流感。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
