Computing the Death Rate of COVID-19

TL;DR

This paper presents a novel method to estimate COVID-19's infection fatality rate by modeling daily infections and incorporating testing data, revealing a decreasing IFR over time across multiple countries.

Contribution

The paper introduces a new approach that estimates the IFR by modeling infection sequences with variable lag times and integrating testing data, improving accuracy over prior methods.

Findings

Estimated IFR in the US decreased from 0.68% to 0.24%.

The method captures IFR changes over time.

Applied to multiple countries with consistent decreasing IFR trends.

Abstract

The Infection Fatality Rate (IFR) of COVID-19 is difficult to estimate because the number of infections is unknown and there is a lag between each infection and the potentially subsequent death. We introduce a new approach for estimating the IFR by first estimating the entire sequence of daily infections. Unlike prior approaches, we incorporate existing data on the number of daily COVID-19 tests into our estimation; knowing the test rates helps us estimate the ratio between the number of cases and the number of infections. Also unlike prior approaches, rather than determining a constant lag from studying a group of patients, we treat the lag as a random variable, whose parameters we determine empirically by fitting our infections sequence to the sequence of deaths. Our approach allows us to narrow our estimation to smaller time intervals in order to observe how the IFR changes over time. We analyze a 250 day period starting on March 1, 2020. We estimate that the IFR in the U.S. decreases from a high of $0.68\%$ down to $0.24\%$ over the course of this time period. We also provide IFR and lag estimates for Italy, Denmark, and the Netherlands, all of which also exhibit decreasing IFRs but to different degrees.