ArticleCommunications medicine2022
Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Optimising pandemic response through vaccination strategies using neural networks.Scientific reports · 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
- A filtering approach for statistical inference in a stochastic SIR model with an application to Covid-19 data.Biostatistics (Oxford, England) · 2025Article
- Modelling the Variability in Immunity Build-Up and Waning Following RNA-Based Vaccination.Viruses · 2025Article
- Comparison between simulated scenarios and Swedish COVID-19 cases throughout the pandemic.Scientific reports · 2025Article
- The utility of infectious disease modelling in informing decisions for outbreak response: A scoping review.PLOS global public health · 2025Article
- Complex systems perspective in assessing risks in artificial intelligence.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Launching Austria's One Health network: paving the way for transdisciplinary collaborations.One health outlook · 2024Article
- Article
- Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.eLife · 2023Article
- A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19.Heliyon · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks.
methodsWe consolidated the output of three epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds.
resultsWe report on three key contributions by which our forecasting and reporting system has helped shaping Austria's policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities.
conclusionsComplex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.