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National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021
(Springer Nature, 2022) Bracher, Johannes; Wolffram, Danie; Deuschel, Jannik; Görgen, Konstantin; Ketterer, Jakob L.; Ullrich, Alexander; Abbott, Sam; Barbarossa, Maria V.; Bertsimas, Dimitris; Bhatia, Sangeeta; Bodych, Marcin; Bosse, Nikos I.; Burgard, Jan Pablo; Castro, Lauren; Fairchild, Geoffrey; Fiedler, Jochen; Fuhrmann, Jan; Funk, Sebastian; Gambin, Anna; Gogolewski, Krzysztof; Heyder, Stefan; Hotz, Thomas; Kheifetz, Yuri; Kirsten, Holger; Krueger, Tyll; Krymova, Ekaterina; Leithäuser, Neele; Li, Michael L.; Meinke, Jan H.; Miasojedow, Błażej; Michaud, Isaac J.; Mohring, Jan; Nouvellet, Pierre; Nowosielski, Jędrzej M.; Ozanski, Tomasz; Radwan, Maciej; Rakowski, Franciszek; Scholz, Markus; Soni, Saksham; Srivastava, Ajitesh; Gneiting, Tilmann; Schienle, Melanie; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Germany; Computational Statistics Group,Heidelberg Institute for Theoretical Studies (HITS), Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany; Robert Koch Institute (RKI), Berlin, Germany; London School of Hygiene and Tropical Medicine, UK; Frankfurt Institute for Advanced Studies, Germany; Sloan School of Management, Massachusetts Institute of Technology,Cambridge, MA, USA; MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK; Wroclaw University of Science and Technology, Poland; Economic and Social StatisticsDepartment, University of Trier, Germany; Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA; Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany; Institute for Applied Mathematics, University of Heidelberg, Germany; Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw; Institute of Mathematics,Technische Universität Ilmenau, Germany; Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germany; Swiss Data Science Center, ETH Zürich and EPF Lausanne, Zürich, Switzerland; Operations Research Center, MassachusettsInstitute of Technology, Cambridge, MA, USA; Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany; Statistical Sciences Group, Los Alamos National Laboratory, USA; School of Life Sciences, University of Sussex, Brighton, UK; InterdisciplinaryCentre for Mathematical and Computational Modelling, University of Warsaw; Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, USA; Institute for Stochastics, Karlsruhe Institute of Technology (KIT), Germany
Background: During the COVID-19 pandemic there has been a strong interest in forecasts ofthe short-term development of epidemiological indicators to inform decision makers. In thisstudy we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021.Methods: We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. Results: We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions morestraightforward than in previous periods. However, major trend changes in reported cases,like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Conclusions: Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

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Subject: Germany License: Uznanie autorstwa 4.0 Międzynarodowe Author: Barbarossa, Maria V.
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