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Predictive performance of international COVID-19 mortality forecasting models
Medrxiv : the Preprint Server for Health Sciences
|November 25, 2020
Summary
Evaluating COVID-19 mortality forecasts revealed varying predictive accuracy. Global models showed surprising performance, with median absolute percent error around 10% at six weeks, despite complex real-world factors.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Accurate forecasting of COVID-19 mortality is crucial for policy and decision-making.
- Assessing the predictive performance of available models is essential for reliable guidance.
Approach:
- Reviewed 386 public COVID-19 forecasting models, focusing on 8 global, publicly available, date-versioned forecasts.
- Analyzed median absolute percent error (MAPE) against observed mortality, stratified by extrapolation duration, region, and estimation month.
- Evaluated models' ability to predict the timing of peak daily mortality.
Key Points:
- MAPE increased with extrapolation time, from 1.8% at one week to 24.6% at twelve weeks for models released in July.
- Six-week MAPE was highest in Sub-Saharan Africa (34.8%) and lowest in high-income countries (6.3%).
- Several global models achieved approximately 10% MAPE at six weeks, demonstrating robust performance.
Conclusions:
- A framework and codebase are provided for ongoing comparison and evaluation of COVID-19 prediction models.
- The study highlights the importance of regional stratification and extrapolation duration in assessing forecast accuracy.
- Despite challenges, some global models offer valuable insights into COVID-19 mortality trends.
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