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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
Martin J. Kuehn; Daniel Abele; Tanmay Mitra; Wadim Koslow; Majid Abedi; Kathrin Rack; Martin Siggel; Sahamoddin Khailaie; Margrit Klitz; Sebastian Binder; Luca Spataro; Jonas Gilg; Jan Kleinert; Matthias Haeberle; Lena Ploetzke; Christoph D Spinner; Melanie Stecher; Xiao Xiang Zhu; Achim Basermann; Michael Meyer-Hermann.
Afiliação
  • Martin J. Kuehn; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Daniel Abele; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Tanmay Mitra; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
  • Wadim Koslow; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Majid Abedi; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
  • Kathrin Rack; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Martin Siggel; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Sahamoddin Khailaie; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
  • Margrit Klitz; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Sebastian Binder; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
  • Luca Spataro; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Jonas Gilg; German Aerospace Center, Institute for Software Technology, Software for Space Systems and Interactive Visualization
  • Jan Kleinert; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Matthias Haeberle; Earth Observation Center, Department EO Data Science, German Aerospace Center, Wessling, Germany
  • Lena Ploetzke; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Christoph D Spinner; Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, Munich, Germany
  • Melanie Stecher; University Hospital of Cologne, Department I for Internal Medicine, University of Cologne, Cologne, Germany; German Center for Infection Research (DZIF), Cologn
  • Xiao Xiang Zhu; Earth Observation Center, Department EO Data Science, German Aerospace Center, Wessling, Germany
  • Achim Basermann; German Aerospace Center, Institute for Software Technology, High-Performance Computing
  • Michael Meyer-Hermann; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20248509
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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 Coron-avirus 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 pre-pandemic 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. Mathematics Subject Classification (2010) 00A72 {middle dot} 65L05 {middle dot} 68U20
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo prognóstico / Rct Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo prognóstico / Rct Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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