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A Quantitative Evaluation of COVID-19 Epidemiological Models
Osman N Yogurtcu1, Marisabel Rodriguez Messan1, Richard C Gerkin2
1Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US FDA, 10903 New Hampshire Ave, Silver Spring, 20993, Maryland, USA.
Medrxiv : the Preprint Server for Health Sciences
|February 10, 2021
Summary
Evaluating COVID-19 epidemiological models helps improve public health decisions. This study scores model accuracy, creating ensemble models to aid policymakers in balancing health and economic impacts.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Accurate forecasting of COVID-19 cases and deaths is crucial for informing public health strategies.
- Understanding the predictive accuracy of existing epidemiological models is essential for their effective use.
Approach:
- Analyzed and scored publicly available COVID-19 epidemiological models from the COVID-19 Forecast Hub.
- Developed a scoring system using log-likelihood on held-out data for cumulative case and death forecasts.
- Continuously updated scores and tracked model performance over time as new data emerged.
Key Points:
- Established a quantitative framework to assess the predictive performance of COVID-19 epidemiological models.
- Utilized model scores to build ensemble models based on historical accuracy.
- Provided a continuously updated performance evaluation for ongoing model assessment.
Conclusions:
- The developed framework aids in improving epidemiological modeling for public health.
- Assists policymakers in selecting appropriate modeling approaches to balance economic and health considerations.
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