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Evaluating epidemic forecasts in an interval format

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  • Johannes Bracher
  • Evan L Ray
  • Tilmann Gneiting
  • Nicholas G Reich

Abstract

For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction.Author summary: During the COVID-19 pandemic, model-based probabilistic forecasts of case, hospitalization, and death numbers can help to improve situational awareness and guide public health interventions. The COVID-19 Forecast Hub (https://covid19forecasthub.org/) collects such forecasts from numerous national and international groups. Systematic and statistically sound evaluation of forecasts is an important prerequisite to revise and improve models and to combine different forecasts into ensemble predictions. We provide an intuitive introduction to scoring methods, which are suitable for the interval/quantile-based format used in the Forecast Hub, and compare them to other commonly used performance measures.

Suggested Citation

  • Johannes Bracher & Evan L Ray & Tilmann Gneiting & Nicholas G Reich, 2021. "Evaluating epidemic forecasts in an interval format," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-15, February.
    • Handle: RePEc:plo:pcbi00:1008618
    DOI: 10.1371/journal.pcbi.1008618
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    References listed on IDEAS

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    1. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    Cited by:

    1. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    2. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    3. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    4. Luis A. Barboza & Shu Wei Chou Chen & Marcela Alfaro Córdoba & Eric J. Alfaro & Hugo G. Hidalgo, 2023. "Spatio‐temporal downscaling emulator for regional climate models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    5. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    6. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    7. Kathryn S Taylor & James W Taylor, 2022. "Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-25, March.
    8. Chiang, Wen-Hao & Liu, Xueying & Mohler, George, 2022. "Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates," International Journal of Forecasting, Elsevier, vol. 38(2), pages 505-520.
    9. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
    10. Jurgen A. Doornik & Jennifer L. Castle & David F. Hendry, 2021. "Modeling and forecasting the COVID‐19 pandemic time‐series data," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2070-2087, September.

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