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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.
Forecasting models have been used extensively to inform decision making during the COVID-19 pandemic. In this preregistered and prospective study, the authors evaluated 14 short-term models for Germany and Poland, finding considerable heterogeneity in predictions and highlighting the benefits of combined forecasts.
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1 Karlsruhe Institute of Technology (KIT), Chair of Statistics and Econometrics, Karlsruhe, Germany (GRID:grid.7892.4) (ISNI:0000 0001 0075 5874); Heidelberg Institute for Theoretical Studies (HITS), Computational Statistics Group, Heidelberg, Germany (GRID:grid.424699.4) (ISNI:0000 0001 2275 2842)
2 Karlsruhe Institute of Technology (KIT), Chair of Statistics and Econometrics, Karlsruhe, Germany (GRID:grid.7892.4) (ISNI:0000 0001 0075 5874)
3 Robert Koch Institute (RKI), Berlin, Germany (GRID:grid.13652.33) (ISNI:0000 0001 0940 3744)
4 London School of Hygiene and Tropical Medicine, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X)
5 Frankfurt Institute for Advanced Studies, Frankfurt, Germany (GRID:grid.417999.b)
6 Massachusetts Institute of Technology, Sloan School of Management, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)
7 Imperial College London, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), London, UK (GRID:grid.7445.2) (ISNI:0000 0001 2113 8111)
8 Wroclaw University of Science and Technology, Wroclaw, Poland (GRID:grid.7005.2) (ISNI:0000 0000 9805 3178)
9 University of Trier, Economic and Social Statistics Department, Trier, Germany (GRID:grid.12391.38) (ISNI:0000 0001 2289 1527)
10 Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, USA (GRID:grid.148313.c) (ISNI:0000 0004 0428 3079)
11 Frankfurt Institute for Advanced Studies, Frankfurt, Germany (GRID:grid.417999.b); Forschungszentrum Jülich, Jülich Supercomputing Centre, Jülich, Germany (GRID:grid.8385.6) (ISNI:0000 0001 2297 375X)
12 University of Warsaw, Institute of Informatics, Warsaw, Poland (GRID:grid.12847.38) (ISNI:0000 0004 1937 1290)
13 University of California, Department of Computer Science, Los Angeles, USA (GRID:grid.19006.3e) (ISNI:0000 0000 9632 6718)
14 Technische Universität Ilmenau, Institute of Mathematics, Ilmenau, Germany (GRID:grid.6553.5) (ISNI:0000 0001 1087 7453)
15 University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany (GRID:grid.9647.c) (ISNI:0000 0004 7669 9786)
16 ETH Zurich and EPFL, Swiss Data Science Center, Lausanne, Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780)
17 Massachusetts Institute of Technology, Operations Research Center, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)
18 Forschungszentrum Jülich, Jülich Supercomputing Centre, Jülich, Germany (GRID:grid.8385.6) (ISNI:0000 0001 2297 375X)
19 Los Alamos National Laboratory, Statistical Sciences Group, Los Alamos, USA (GRID:grid.148313.c) (ISNI:0000 0004 0428 3079)
20 University of Warsaw, Interdisciplinary Centre for Mathematical and Computational Modeling, Warsaw, Poland (GRID:grid.12847.38) (ISNI:0000 0004 1937 1290)
21 University of Southern California, Ming Hsieh Department of Computer and Electrical Engineering, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853)
22 Heidelberg Institute for Theoretical Studies (HITS), Computational Statistics Group, Heidelberg, Germany (GRID:grid.424699.4) (ISNI:0000 0001 2275 2842); Karlsruhe Institute of Technology (KIT), Institute for Stochastics, Karlsruhe, Germany (GRID:grid.7892.4) (ISNI:0000 0001 0075 5874)
23 University of Warsaw, Interdisciplinary Centre for Mathematical and Computational Modeling, Warsaw, Poland (GRID:grid.12847.38) (ISNI:0000 0004 1937 1290); John Paul II Catholic University of Lublin, Institute of Psychology, Lublin, Poland (GRID:grid.37179.3b) (ISNI:0000 0001 0664 8391)
24 University of Warsaw, Interdisciplinary Centre for Mathematical and Computational Modeling, Warsaw, Poland (GRID:grid.12847.38) (ISNI:0000 0004 1937 1290); University of Białystok, Faculty of Physics, Białystok, Poland (GRID:grid.25588.32) (ISNI:0000 0004 0620 6106)
25 Warsaw University of Technology, Warsaw, Poland (GRID:grid.1035.7) (ISNI:0000000099214842)
26 Polish National Institute of Public Health—National Institute of Hygiene, Wroclaw, Poland (GRID:grid.12847.38)
27 Wroclaw University of Science and Technology, Wroclaw, Poland (GRID:grid.7005.2) (ISNI:0000 0000 9805 3178); Nokia Solutions and Networks, Wroclaw, Poland (GRID:grid.7005.2)
28 University of Geneva, Institute of Global Health, Faculty of Medicine, Geneva, Switzerland (GRID:grid.8591.5) (ISNI:0000 0001 2322 4988)
29 EPFL, Center for Intelligent Systems, Lausanne, Switzerland (GRID:grid.5333.6) (ISNI:0000000121839049)
30 University of Southern California, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853)