Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them

Abstract

Different ways of calculating mortality ratios during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. We formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality ratios. The key parameters that affect the dynamics of the different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. We stress that none of these ratios are accurately represented by the often misinterpreted case fatality ratio (CFR), the number of deaths to date divided by the total number of confirmed infected cases to date. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality ratios and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality ratios during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data.