Coroneo, Laura orcid.org/0000-0001-5740-9315, Iacone, Fabrizio orcid.org/0000-0002-2681-9036, Paccagnini, Alessia et al. (1 more author) (2023) Testing the predictive accuracy of COVID-19 forecasts. International journal of forecasting. pp. 606-622. ISSN 0169-2070
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 out performs 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Forecast evaluation, Forecasting tests, Epidemic |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 17 Jan 2022 11:50 |
Last Modified: | 04 Mar 2025 01:13 |
Published Version: | https://doi.org/10.1016/j.ijforecast.2022.01.005 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.ijforecast.2022.01.005 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182617 |