A Logistic Model for Age-Specific COVID-19 Case-Fatality Rates
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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.
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SciScore for 10.1101/2020.06.12.20129908: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this …
SciScore for 10.1101/2020.06.12.20129908: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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