Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-19

TL;DR

This paper develops novel bounds on infection prevalence using test rate and yield data, accounting for test accuracy and targeting, with applications to early COVID-19 data showing the bounds are informative and challenge some early speculations.

Contribution

It introduces a new partial identification method to bound infection prevalence considering test accuracy and targeting, applicable to COVID-19 and other contexts.

Findings

Bounds suggest COVID-19 infection fatality rate in Italy was higher than influenza by mid-April.

Even weak bounds are reasonably informative in early pandemic data.

Contradicts early speculations about COVID-19 severity in Italy.

Abstract

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.