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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

Author

Listed:
  • 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
  • Wang, Yijin
  • Zorn, Martha
  • Tibshirani, Ryan J.
  • Reich, Nicholas G.

Abstract

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:3:p:1366-1383
    DOI: 10.1016/j.ijforecast.2022.06.005
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    as
    1. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Nicholas G Reich & Stephen A Lauer & Krzysztof Sakrejda & Sopon Iamsirithaworn & Soawapak Hinjoy & Paphanij Suangtho & Suthanun Suthachana & Hannah E Clapham & Henrik Salje & Derek A T Cummings & Just, 2016. "Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(6), pages 1-17, June.
    3. Stephen C. Hora & Benjamin R. Fransen & Natasha Hawkins & Irving Susel, 2013. "Median Aggregation of Distribution Functions," Decision Analysis, INFORMS, vol. 10(4), pages 279-291, December.
    4. Nicholas G Reich & Craig J McGowan & Teresa K Yamana & Abhinav Tushar & Evan L Ray & Dave Osthus & Sasikiran Kandula & Logan C Brooks & Willow Crawford-Crudell & Graham Casey Gibson & Evan Moore & Reb, 2019. "Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-19, November.
    5. Felipe J Colón-González & Leonardo Soares Bastos & Barbara Hofmann & Alison Hopkin & Quillon Harpham & Tom Crocker & Rosanna Amato & Iacopo Ferrario & Francesca Moschini & Samuel James & Sajni Malde &, 2021. "Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles," PLOS Medicine, Public Library of Science, vol. 18(3), pages 1-30, March.
    6. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
    7. Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016. "The forecast combination puzzle: A simple theoretical explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
    8. 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.
    9. Roopesh Ranjan & Tilmann Gneiting, 2010. "Combining probability forecasts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 71-91, January.
    10. Simon Pollett & Michael A Johansson & Nicholas G Reich & David Brett-Major & Sara Y Del Valle & Srinivasan Venkatramanan & Rachel Lowe & Travis Porco & Irina Maljkovic Berry & Alina Deshpande & Moritz, 2021. "Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines," PLOS Medicine, Public Library of Science, vol. 18(10), pages 1-12, October.
    11. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2018. "Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-29, June.
    12. Anil Gaba & Ilia Tsetlin & Robert L. Winkler, 2017. "Combining Interval Forecasts," Decision Analysis, INFORMS, vol. 14(1), pages 1-20, March.
    13. James Turtle & Pete Riley & Michal Ben-Nun & Steven Riley, 2021. "Accurate influenza forecasts using type-specific incidence data for small geographic units," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-20, July.
    14. A. Agosto & Alexandra Campmas & P. Giudici & A. Renda, 2021. "Monitoring COVID-19 contagion growth," Post-Print hal-03407115, HAL.
    15. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    16. 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.
    17. Dave Osthus & Kelly R. Moran, 2021. "Multiscale influenza forecasting," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    18. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Robert L. Winkler, 2013. "Is It Better to Average Probabilities or Quantiles?," Management Science, INFORMS, vol. 59(7), pages 1594-1611, July.
    19. J. Bracher & D. Wolffram & J. Deuschel & K. Görgen & J. L. Ketterer & A. Ullrich & S. Abbott & M. V. Barbarossa & D. Bertsimas & S. Bhatia & M. Bodych & N. I. Bosse & J. P. Burgard & L. Castro & G. Fa, 2021. "A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
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