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

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.

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Cited by in corpus (6)

• Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
• Some Challenges in Monitoring Epidemics
• Impact of random and targeted disruptions on information diffusion during outbreaks
• Aggregating multiple test results to improve medical decision-making
• On the accuracy of short-term COVID-19 fatality forecasts
• A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors