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Accuracy of US CDC COVID-19 forecasting models.
Aviral Chharia1,2,3, Govind Jeevan1,2, Rajat Aayush Jha1,2
1Global Health Research Collective, Academics for the Future of Science, Cambridge, MA, United States.
Frontiers in Public Health
|July 11, 2024
Summary
Many COVID-19 forecasting models fail to outperform simple baselines. Pandemic modeling accuracy has not improved over time, raising concerns about their use in policy decisions.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health Policy
Background:
- Accurate pandemic forecasting is crucial for resource allocation and policy.
- Numerous COVID-19 case prediction models exist, but their performance over time and by type is not well understood.
Purpose of the Study:
- To systematically analyze the accuracy of US Centers for Disease Control and Prevention (CDC) COVID-19 forecasting models.
- To compare model performance against government data, baseline models, and each other.
Main Methods:
- Categorization of US CDC COVID-19 forecasting models.
- Calculation of mean absolute percent error (MAPE) wave-wise and overall.
- Comparison with static and linear trend baseline models.
Main Results:
- Two-thirds of models did not outperform a static case baseline.
- One-third of models did not outperform a linear trend forecast.
- No single modeling approach consistently outperformed others; errors increased over time.
Conclusions:
- Many pandemic forecasting models lack predictive accuracy.
- Concerns exist regarding the reliability of models hosted on official public health platforms.
- A universal evaluation method is needed to drive the development of improved pandemic forecasting models.
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