Testing the predictive accuracy of COVID-19 forecasts

Laura Coroneo, Fabrizio Iacone, Alessia Paccagnini and Paulo Santos Monteiro

International Journal of Forecasting, 2023, vol. 39, issue 2, 606-622

Abstract: We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1 week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3 and 4 week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.

Keywords: Forecast evaluation; Forecasting tests; Epidemic; COVID-19 Infectious diseases; Mortality Time series methods (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Testing the predictive accuracy of COVID-19 forecasts (2021)
Working Paper: Testing the predictive accuracy of COVID-19 forecasts (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:606-622

DOI: 10.1016/j.ijforecast.2022.01.005

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