Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution.
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Authors
Kühn, Martin JAbele, Daniel
Mitra, Tanmay
Koslow, Wadim
Abedi, Majid
Rack, Kathrin
Siggel, Martin
Khailaie, Sahamoddin
Klitz, Margrit
Binder, Sebastian
Spataro, Luca
Gilg, Jonas
Kleinert, Jan
Häberle, Matthias
Plötzke, Lena
Spinner, Christoph D
Stecher, Melanie
Zhu, Xiao Xiang
Basermann, Achim
Meyer-Hermann, Michael
Issue Date
2021-06-30
Metadata
Show full item recordAbstract
on-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.Citation
Math Biosci. 2021 Sep;339:108648. doi: 10.1016/j.mbs.2021.108648. Epub 2021 Jun 30. PMID: 34216635.Affiliation
BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.Publisher
ElsevierJournal
Mathematical biosciencesPubMed ID
34216635Type
ArticleLanguage
enEISSN
1879-3134ae974a485f413a2113503eed53cd6c53
10.1016/j.mbs.2021.108648
Scopus Count
The following license files are associated with this item:
- Creative Commons
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