Memory-based meso-scale modeling of Covid-19: County-resolved timelines in Germany.
COVID-19
SIR and integro-differential models
Spatio-temporal meso-scale outbreak dynamics
Journal
Computational mechanics
ISSN: 0178-7675
Titre abrégé: Comput Mech
Pays: Germany
ID NLM: 9883520
Informations de publication
Date de publication:
2020
2020
Historique:
received:
07
07
2020
accepted:
09
07
2020
pubmed:
25
8
2020
medline:
25
8
2020
entrez:
25
8
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32836600
doi: 10.1007/s00466-020-01883-5
pii: 1883
pmc: PMC7398641
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1069-1079Informations de copyright
© The Author(s) 2020.
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