National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

Journal Article · Mon Oct 31 00:00:00 EDT 2022 · Communications Medicine

DOI:https://doi.org/10.1038/s43856-022-00191-8· OSTI ID:1974933

Bracher, Johannes ^[1]; Wolffram, Daniel ^[2]; Deuschel, Jannik ^[3]; Görgen, Konstantin ^[3]; Ketterer, Jakob L. ^[3]; Ullrich, Alexander ^[4]; Abbott, Sam ^[5]; Barbarossa, Maria V. ^[6]; Bertsimas, Dimitris ^[7]; Bhatia, Sangeeta ^[8]; Bodych, Marcin ^[9]; Bosse, Nikos I. ^[5]; Burgard, Jan Pablo ^[10]; Castro, Lauren ^[11]; Fairchild, Geoffrey ^[11]; Fiedler, Jochen ^[12]; Fuhrmann, Jan ^[13]; Funk, Sebastian ^[5]; Gambin, Anna ^[14]; Gogolewski, Krzysztof ^[14] ; Heyder, Stefan ^[15]; Hotz, Thomas ^[15]; Kheifetz, Yuri ^[16]; Kirsten, Holger ^[16]; Krueger, Tyll ^[9]; Krymova, Ekaterina ^[17]; Leithäuser, Neele ^[12]; Li, Michael L. ^[7]; Meinke, Jan H. ^[18]; Miasojedow, Błażej ^[14]; Michaud, Isaac J. ^[11]; Mohring, Jan ^[12]; Nouvellet, Pierre ^[19]; Nowosielski, Jedrzej M. ^[14]; Ozanski, Tomasz ^[9]; Radwan, Maciej ^[14]; Rakowski, Franciszek ^[14]; Scholz, Markus ^[16]; Soni, Saksham ^[7]; Srivastava, Ajitesh ^[20]; Gneiting, Tilmann ^[21]; Schienle, Melanie ^[1]

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1. Karlsruhe Institute of Technology (KIT) (Germany); Heidelberg Information and Data Science School for Theoretical Studies, Karlsruhe/Heidelberg (Germany)
2. Karlsruhe Institute of Technology (KIT) (Germany); Heidelberg Information and Data Science School for Theoretical Studies, Karlsruhe/Heidelberg (Germany); Heidelberg Information and Data Science School for Health, Karlsruhe/Heidelberg (Germany)
3. Karlsruhe Institute of Technology (KIT) (Germany)
4. Robert Koch Institue (RKI), Berlin (Germany)
5. London School of Hygiene and Tropical Medicine, London (United Kingdom)
6. Frankfurt Institute for Advanced Studies, Frankfurt (Germany)
7. Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
8. Imperial College, London (United Kingdom)
9. Wroclaw University of Science and Technology (Poland)
10. University of Trier (Germany)
11. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
12. Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern (Germany)
13. University of Heidelberg (Germany)
14. University of Warsaw (Poland)
15. Technische Universität Ilmenau (Germany)
16. University of Leipzig (Germany)
17. Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland); Ecole Polytechnique Federale Lausanne (EPFL) (Switzerland)
18. Jülich Supercomputing Centre, Forschungszentrum Jülich (Germany)
19. University of Sussex, Brighton (United Kingdom)
20. University of Southern California, Los Angeles, CA (United States)
21. Heidelberg Information and Data Science School for Theoretical Studies, Karlsruhe/Heidelberg (Germany); Karlsruhe Institute of Technology (KIT) (Germany)

During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study 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. 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. 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 more straightforward 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. 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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1974933
• Report Number(s): LA-UR-22-20551
• Journal Information: Communications Medicine, Journal Name: Communications Medicine Journal Issue: 1 Vol. 2; ISSN 2730-664X
• Publisher: Springer
• Country of Publication: United States
• Language: English

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60 APPLIED LIFE SCIENCES