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Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution.
Kühn, Martin J; Abele, 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.
  • Kühn MJ; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany. Electronic address: Martin.Kuehn@DLR.de.
  • Abele D; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Mitra T; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
  • Koslow W; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Abedi M; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
  • Rack K; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Siggel M; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Khailaie S; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
  • Klitz M; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Binder S; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
  • Spataro L; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Gilg J; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Kleinert J; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Häberle M; Earth Observation Center, Department EO Data Science, German Aerospace Center, Weßling, Germany.
  • Plötzke L; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
  • Spinner CD; Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, Munich, Germany.
  • Stecher M; University Hospital of Cologne, Department I for Internal Medicine, University of Cologne; German Center for Infection Research (DZIF), Cologne, Germany.
  • Zhu XX; Earth Observation Center, Department EO Data Science, German Aerospace Center, Weßling, Germany.
  • Basermann A; Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany. Electronic address: Achim.Basermann@DLR.de.
  • Meyer-Hermann M; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany. Electronic address: MMH@Theoretical-Biology.de.
Math Biosci ; 339: 108648, 2021 09.
Article in English | MEDLINE | ID: covidwho-1294054
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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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Disease Control / Models, Statistical / Spatial Analysis / Social Network Analysis / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Math Biosci Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Disease Control / Models, Statistical / Spatial Analysis / Social Network Analysis / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Math Biosci Year: 2021 Document Type: Article