TY - JOUR
T1 - A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave
AU - List of Contributors by Team
AU - CovidAnalytics-DELPHI
AU - epiforecasts-EpiExpert and epiforecasts-EpiNow2
AU - FIAS FZJ-Epi1Ger
AU - German and Polish Forecast Hub Coordination Team
AU - ICM-agent
AU - Imperial-ensemble2
AU - ITWW-county repro
AU - LANL-GrowthRate
AU - LeipzigIMISE-SECIR
AU - MIMUW-StochSEIR
AU - MOCOS-agent1
AU - SDSC ISG-TrendModel
AU - UCLA-SuEIR
AU - USC-SIkJalpha
AU - Bracher, Johannes
AU - Wolffram, Daniel
AU - Deuschel, Jannik
AU - Görgen, Konstantin
AU - Ketterer, Jakob L.
AU - Ullrich, Alexander
AU - Abbott, Sam
AU - Barbarossa, Maria Vittoria
AU - Bertsimas, Dimitris
AU - Bhatia, Sangeeta
AU - Bodych, Marcin
AU - Bosse, Nikos I.
AU - Burgard, Jan Pablo
AU - Castro, Lauren
AU - Fairchild, Geoffrey
AU - Fuhrmann, Jan
AU - Funk, Sebastian
AU - Gogolewski, Krzysztof
AU - Gu, Quanquan
AU - Heyder, Stefan
AU - Hotz, Thomas
AU - Kheifetz, Yuri
AU - Kirsten, Holger
AU - Krueger, Tyll
AU - Krymova, Ekaterina
AU - Li, Michael Lingzhi
AU - Meinke, Jan H.
AU - Michaud, Isaac J.
AU - Niedzielewski, Karol
AU - Ożański, T.
AU - Rakowski, Franciszek
AU - Scholz, Markus
AU - Soni, Saksham
AU - Srivastava, Ajitesh
AU - Zieliński, Jakub
AU - Zou, Difan
AU - Gneiting, Tilmann
AU - Schienle, Melanie
AU - Bouardi, Hamza Tazi
AU - Lami, Omar Skali
AU - Górski, Model Łukasz
AU - Gruziel-Słomka, Magdalena
AU - Kaczorek, Artur
AU - Moszyński, Antoni
AU - Nowosielski, Jedrzej
AU - Radwan, Maciej
AU - Semeniuk, Marcin
AU - Bartczuk, Rafał
AU - Kisielewski, Jan
AU - Xu, Frost Tianjian
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85115347481
UR - https://www.scopus.com/pages/publications/85115347481#tab=citedBy
U2 - 10.1038/s41467-021-25207-0
DO - 10.1038/s41467-021-25207-0
M3 - Article
C2 - 34453047
AN - SCOPUS:85115347481
SN - 2041-1723
VL - 12
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 5173
ER -