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A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave

Author

Listed:
  • J. Bracher

    (Karlsruhe Institute of Technology (KIT)
    Heidelberg Institute for Theoretical Studies (HITS))

  • D. Wolffram

    (Karlsruhe Institute of Technology (KIT)
    Heidelberg Institute for Theoretical Studies (HITS))

  • J. Deuschel

    (Karlsruhe Institute of Technology (KIT))

  • K. Görgen

    (Karlsruhe Institute of Technology (KIT))

  • J. L. Ketterer

    (Karlsruhe Institute of Technology (KIT))

  • A. Ullrich

    (Robert Koch Institute (RKI))

  • S. Abbott

    (London School of Hygiene and Tropical Medicine)

  • M. V. Barbarossa

    (Frankfurt Institute for Advanced Studies)

  • D. Bertsimas

    (Massachusetts Institute of Technology)

  • S. Bhatia

    (Imperial College London)

  • M. Bodych

    (Wroclaw University of Science and Technology)

  • N. I. Bosse

    (London School of Hygiene and Tropical Medicine)

  • J. P. Burgard

    (University of Trier)

  • L. Castro

    (Los Alamos National Laboratory)

  • G. Fairchild

    (Los Alamos National Laboratory)

  • J. Fuhrmann

    (Frankfurt Institute for Advanced Studies
    Forschungszentrum Jülich)

  • S. Funk

    (London School of Hygiene and Tropical Medicine)

  • K. Gogolewski

    (University of Warsaw)

  • Q. Gu

    (University of California)

  • S. Heyder

    (Technische Universität Ilmenau)

  • T. Hotz

    (Technische Universität Ilmenau)

  • Y. Kheifetz

    (University of Leipzig)

  • H. Kirsten

    (University of Leipzig)

  • T. Krueger

    (Wroclaw University of Science and Technology)

  • E. Krymova

    (ETH Zurich and EPFL)

  • M. L. Li

    (Massachusetts Institute of Technology)

  • J. H. Meinke

    (Forschungszentrum Jülich)

  • I. J. Michaud

    (Los Alamos National Laboratory)

  • K. Niedzielewski

    (University of Warsaw)

  • T. Ożański

    (Wroclaw University of Science and Technology)

  • F. Rakowski

    (University of Warsaw)

  • M. Scholz

    (University of Leipzig)

  • S. Soni

    (Massachusetts Institute of Technology)

  • A. Srivastava

    (University of Southern California)

  • J. Zieliński

    (University of Warsaw)

  • D. Zou

    (University of California)

  • T. Gneiting

    (Heidelberg Institute for Theoretical Studies (HITS)
    Karlsruhe Institute of Technology (KIT))

  • M. Schienle

    (Karlsruhe Institute of Technology (KIT))

Abstract

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25207-0
    DOI: 10.1038/s41467-021-25207-0
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    Cited by:

    1. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. 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.

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