Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities

TL;DR

This paper integrates excess death data, testing statistics, and epidemic modeling to refine COVID-19 mortality estimates, revealing significant regional variations and underreporting in many areas.

Contribution

It introduces a combined approach using historical data, testing, and epidemic models to improve mortality estimates during COVID-19, accounting for underreporting and regional differences.

Findings

US excess deaths are 13% higher than reported COVID-19 deaths.

New York City experienced weekly deaths eight times higher than previous years.

Significant excess deaths in Peru, Ecuador, Mexico, and Spain; negligible or negative in Denmark, Germany, Norway.

Abstract

Factors such as non-uniform definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US is 13$\%$ higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find negligible or negative excess deaths for part and all of 2020 for Denmark, Germany, and Norway.