Abstract
To develop a mathematical model to characterize age-specific case-fatality rates (CFR) of COVID-19. Based on 2 large-scale Chinese and Italian CFR data, a logistic model was derived to provide quantitative insight on the dynamics between CFR and age. We inferred that CFR increased faster in Italy than in China, as well as in females over males. In addition, while CFR increased with age, the rate of growth eventually slowed down, with a predicted theoretical upper limit for males (32%), females (21%), and the general population (23%). Our logistic model provided quantitative insight on the dynamics of CFR.
Highlights
Recent studies showed that COVID-19 case-fatality rates (CFR) increased with age, with elder people at higher risk of fatality than younger ones[1,2,3]
By deriving a logistic model to characterize age-specific CFR data, we were able to uncover novel quantitative insight on the dynamics of CFR
We found that a logistic model could characterize the mathematical relationship between CFR
Summary
AND SIGNIFICANCE Recent studies showed that COVID-19 case-fatality rates (CFR) increased with age, with elder people at higher risk of fatality than younger ones[1,2,3]. Those studies made important observations that COVID-19 CFR were age dependent, their results were limited by descriptive data analysis. We presented a quantitative mathematical modeling approach to reanalyze those published data. By deriving a logistic model to characterize age-specific CFR data, we were able to uncover novel quantitative insight on the dynamics of CFR
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