ArticleComputational mechanics2020
Memory-based meso-scale modeling of Covid-19: County-resolved timelines in Germany.
Article in Computational mechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Stability analysis and numerical simulation of nonlocal extended epidemic models using positivity-preserving scheme.Scientific reports · 2026Article
- Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany.BMC infectious diseases · 2022Article
- Differential evolution and particle swarm optimization against COVID-19.Artificial intelligence review · 2022Article
- Modeling the Waves of Covid-19.Acta biotheoretica · 2021Article
- Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany.PloS one · 2021Article
- Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What-If Analyses of Localized and Dynamic Effects.Journal of the Indian Institute of Science · 2021Review
- Data-driven modeling of COVID-19-Lessons learned.Extreme Mechanics Letters · 2020Review
- Is it safe to lift COVID-19 travel bans? The Newfoundland story.medRxiv : the preprint server for health sciences · 2020Article
- Is it safe to lift COVID-19 travel bans? The Newfoundland story.Computational mechanics · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has led to an unprecedented world-wide effort to gather data, model, and understand the viral spread. Entire societies and economies are desperate to recover and get back to normality. However, to this end accurate models are of essence that capture both the viral spread and the courses of disease in space and time at reasonable resolution. Here, we combine a spatially resolved county-level infection model for Germany with a memory-based integro-differential approach capable of directly including medical data on the course of disease, which is not possible when using traditional SIR-type models. We calibrate our model with data on cumulative detected infections and deaths from the Robert-Koch Institute and demonstrate how the model can be used to obtain county- or even city-level estimates on the number of new infections, hospitality rates and demands on intensive care units. We believe that the present work may help guide decision makers to locally fine-tune their expedient response to potential new outbreaks in the near future.
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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.