Kuhn, Martin J., Abele, Daniel ORCID: 0000-0001-7021-1573, Mitra, Tanmay ORCID: 0000-0002-4136-4834, Koslow, Wadim, Abedi, Majid, Rack, Kathrin ORCID: 0000-0002-5794-5705, Siggel, Martin ORCID: 0000-0002-3952-4659, Khailaie, Sahamoddin, Klitz, Margrit, Binder, Sebastian, Spataro, Luca, Gilg, Jonas, Kleinert, Jan, Haberle, Matthias, Plotzke, Lena, Spinner, Christoph D., Stecher, Melanie, Zhu, Xiao Xiang, Basermann, Achim and Meyer-Hermann, Michael (2021). Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution. Math. Biosci., 339. NEW YORK: ELSEVIER SCIENCE INC. ISSN 1879-3134

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Abstract

Non-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.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Kuhn, Martin J.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Abele, DanielUNSPECIFIEDorcid.org/0000-0001-7021-1573UNSPECIFIED
Mitra, TanmayUNSPECIFIEDorcid.org/0000-0002-4136-4834UNSPECIFIED
Koslow, WadimUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Abedi, MajidUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rack, KathrinUNSPECIFIEDorcid.org/0000-0002-5794-5705UNSPECIFIED
Siggel, MartinUNSPECIFIEDorcid.org/0000-0002-3952-4659UNSPECIFIED
Khailaie, SahamoddinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Klitz, MargritUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Binder, SebastianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Spataro, LucaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Gilg, JonasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kleinert, JanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Haberle, MatthiasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Plotzke, LenaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Spinner, Christoph D.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Stecher, MelanieUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Basermann, AchimUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Meyer-Hermann, MichaelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-591481
DOI: 10.1016/j.mbs.2021.108648
Journal or Publication Title: Math. Biosci.
Volume: 339
Date: 2021
Publisher: ELSEVIER SCIENCE INC
Place of Publication: NEW YORK
ISSN: 1879-3134
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
Biology; Mathematical & Computational BiologyMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/59148

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