论文标题
通过界定测试的选择性和准确性来界限感染率：应用于早期Covid-19
Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-19
论文作者
论文摘要
我提出了有关感染率的新型部分识别范围，从测试率和测试产量的信息中。该方法在（i）测试准确性上利用用户指定的界限，以及（ii）对测试的靶向程度，正式化为对真实感染状态对被测试和可嵌入Logit规范的优势比的影响的限制。激励的应用是对19日大流行的，但该策略在其他地方也可能很有用。 对大流行早期的数据进行了评估，即使是新颖的界限，也是合理的信息。值得注意的是，与当时广泛报道的猜测相反，他们在4月中旬将意大利的感染死亡率置于远高于流感的一种。
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.
