On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate

TL;DR

This paper examines biases affecting COVID-19 case fatality rate estimates, analyzes their impact, and proposes a partially corrected estimator, emphasizing the importance of randomized testing to reduce bias.

Contribution

It introduces a partially corrected estimator for COVID-19 CFR that accounts for reporting biases and lag effects, and highlights randomized contact testing as a bias mitigation strategy.

Findings

Biases significantly affect CFR estimates.

Partially corrected estimator improves accuracy.

Randomized testing reduces bias covariance.

Abstract

The relative case fatality rates (CFRs) between groups and countries are key measures of relative risk that guide policy decisions regarding scarce medical resource allocation during the ongoing COVID-19 pandemic. In the middle of an active outbreak when surveillance data is the primary source of information, estimating these quantities involves compensating for competing biases in time series of deaths, cases, and recoveries. These include time- and severity- dependent reporting of cases as well as time lags in observed patient outcomes. In the context of COVID-19 CFR estimation, we survey such biases and their potential significance. Further, we analyze theoretically the effect of certain biases, like preferential reporting of fatal cases, on naive estimators of CFR. We provide a partially corrected estimator of these naive estimates that accounts for time lag and imperfect reporting of deaths and recoveries. We show that collection of randomized data by testing the contacts of infectious individuals regardless of the presence of symptoms would mitigate bias by limiting the covariance between diagnosis and death. Our analysis is supplemented by theoretical and numerical results and a simple and fast open-source codebase at https://github.com/aangelopoulos/cfr-covid-19 .