A Logistic Model for Age-Specific COVID-19 Case-Fatality Rates
Abstract
Objectives
To develop a mathematical model to characterize age-specific case-fatality rates (CFR) of COVID-19.
Materials and Method
Based on two large-scale Chinese and Italian CFR data, a logistic model was derived to provide quantitative insight on the dynamics between CFR and age.
Results and Discussion
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%).
Conclusion
Our logistic model provided quantitative insight on the dynamics of CFR.
Lay Summary
Recently published studies have qualitatively shown that the COVID-19 case-fatality rates increased with age, with elder people at higher risk of fatality than younger ones. In our study, we presented a quantitative mathematical modeling approach to re-analyze those published data. Specifically, we were able to derive a logistic model to characterize age-specific CFRs. The derived mathematical model uncovered novel quantitative insights on the dynamics between CFR and age. Specifically, we inferred from the model that while CFR increased with age, the rate of growth eventually slowed down, with a predicted theoretical upper limit of 23% for the general population.
Related articles
Related articles are currently not available for this article.