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.
翻译:我提议从试验率和试验收成的资料中确定关于感染流行程度的新的部分识别界限。这种方法使用用户指定的界限,其内容是:(一) 测试精确度和(二) 测试目标的范围,正式确定为对真正感染状况对接受检测的概率比的影响的限制,从而可以嵌入记录规格。激励性应用适用于COVID-19大流行,但这一战略在别处也可能有用。根据该流行病早期的数据评估,即使是最弱的新型界限也具有合理的信息。 值得注意的是,与当时广泛报道的推测相反,它们将意大利的感染率在4月中旬前大大高于流感的死亡率。