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.

中文翻译：

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通过限制选择性和测试准确性限制感染率：适用于早期 COVID-19

我根据测试率和测试率的信息提出了感染流行率的新部分识别界限。该方法利用用户指定的 (i) 测试准确性和 (ii) 测试的针对性范围，形式化为对真实感染状态对获得测试的优势比的影响的限制，从而可嵌入到 logit 规范中。激励应用是针对 COVID-19 大流行，但该策略在其他地方也可能有用。根据大流行早期的数据评估，即使是最薄弱的新界限也能提供合理的信息。值得注意的是，与当时广泛报道的猜测相反，他们将意大利的感染致死率置于 4 月中旬之前远高于流感的致死率。