ArticleNature communications2021
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy.Scientific reports · 2022Pooled it
- Basic Baseline model design choices can substantially influence performance in collaborative forecast hubs.medRxiv : the preprint server for health sciences · 2026Article
- Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- A modular approach to forecasting COVID-19 hospital bed occupancy.Communications medicine · 2025Article
- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- Modeling the Complete Dynamics of the SARS-CoV-2 Pandemic of Germany and Its Federal States Using Multiple Levels of Data.Viruses · 2025Article
- Hybrid metapopulation agent-based epidemiological models for efficient insight on the individual scale: A contribution to green computing.Infectious Disease Modelling · 2025Article
- Post-processing and weighted combination of infectious disease nowcasts.PLoS computational biology · 2025Article
- Empirical prediction intervals applied to short term mortality forecasts and excess deaths.Population health metrics · 2024Article
- Characterising information gains and losses when collecting multiple epidemic model outputs.Epidemics · 2024Article
- Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.Scientific reports · 2024Article
- Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.Scientific reports · 2024Article
- Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.PLoS computational biology · 2024Article
- Effect of testing criteria for infectious disease surveillance: The case of COVID-19 in Norway.PloS one · 2024Article
- Scoring epidemiological forecasts on transformed scales.PLoS computational biology · 2023Article
- Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.PLoS computational biology · 2023Article
- Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.eLife · 2023Article
- [Forecasting models to guide intensive care COVID-19 capacities in Germany].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2023Article
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Authors and funding
39 authors.
Funding
Abstract
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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