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Tytuł pozycji:

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

Tytuł:
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave
Autorzy:
Gogolewski, Krzysztof
Bracher, Johannes
Fairchild, Geoffrey
Li, Michael Lingzhi
Schienle, Melanie
Funk, Sebastian
Scholz, Markus
Srivastava, Ajitesh
Kirsten, Holger
Heyder, Stefan
Zielinski, Jakub
Deuschel, Jannik
Soni, Saksham
Görgen, Konstantin
Bosse, Nikos I.
Meinke, Jan H.
Niedzielewski, Karol
Wolffram, Daniel
Krymova, Ekaterina
Ullrich, Alexander
Krueger, Tyll
Castro, Lauren
Bertsimas, Dimitris
Ożański, Tomasz
Ketterer, Jakob L.
Bodych, Marcin
Hotz, Thomas
Gneiting, Tilmann
Fuhrmann, Jan
Michaud, Isaac
Bhatia, Sangeeta
Barbarossa, Maria Vittoria
Rakowski, Franciszek
Burgard, Jan Pablo
Gu, Quanquan
Abbott, Sam
Kheifetz, Yuri
Zou, Difan
Współwytwórcy:
Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, USA
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw
Institute of Mathematics, Technische Universität Ilmenau, Germany
Swiss Data Science Center, ETH Zurich and EPFL, Lausanne, Switzerland
Economic and Social Statistics Department, University of Trier, Germany
Department of Computer Science, University of California, Los Angeles, USA
Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, USA
Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, USA
Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
Institute of Informatics, University of Warsaw, Poland
Wroclaw University of Science and Technology, Wroclaw, Poland
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Computational Statistics Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
Robert Koch Institute (RKI), Berlin, Germany
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, USA
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germany
London School of Hygiene and Tropical Medicine, London, UK
Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, UK
Operations Research Center, Massachusetts Institute of Technology, Cambridge, USA
Frankfurt Institute for Advanced Studies, Frankfurt, Germany
Słowa kluczowe:
disease modelling
COVID-19
Data publikacji:
2021
Wydawca:
Springer Nature
ISBN, ISSN:
20411723
Język:
angielski
Linki:
https://depot.ceon.pl/handle/123456789/21244  Link otwiera się w nowym oknie
Prawa:
http://creativecommons.org/licenses/by/4.0/legalcode
Dostawca treści:
Repozytorium Centrum Otwartej Nauki
Inne
  Przejdź do źródła  Link otwiera się w nowym oknie
The online version contains supplementary materialavailable athttps://doi.org/10.1038/s41467-021-25207-0.

Open Access funding enabled and organized by Projekt DEAL

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.

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