| Autor: |
Bracher, J, Wolffram, D, Deuschel, J, Görgen, K, Ketterer, JL, Ullrich, A, Abbott, S, Barbarossa, MV, Bertsimas, D, Bhatia, S, Bodych, M, Bosse, NI, Burgard, JP, Castro, L, Fairchild, G, Fuhrmann, J, Funk, S, Gogolewski, K, Gu, Q, Heyder, S, Hotz, T, Kheifetz, Y, Kirsten, H, Krueger, T, Krymova, E, Li, ML, Meinke, JH, Michaud, IJ, Niedzielewski, K, Ożański, T, Rakowski, F, Scholz, M, Soni, S, Srivastava, A, Zieliński, J, Zou, D, Gneiting, T, Schienle, M, List of Contributors by Team |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| ISSN: |
2041-1723 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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