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10.1016-j.mbs.2021.108648 |
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|a 00255564 (ISSN)
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|a 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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|b Elsevier Inc.
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1016/j.mbs.2021.108648
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|a 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. © 2021
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|a age
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|a Age Factors
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|a Article
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|a assessment method
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|a asymptomatic infection
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|a communicable disease control
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|a Communicable Disease Control
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|a Coronavirus
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|a Coronavirus disease
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|a coronavirus disease 2019
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|a Covid-19
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|a COVID-19
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|a COVID-19
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|a death
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|a Demographic information
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|a demographic trend
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|a demography
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|a disease spread
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|a disease transmission
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|a Diseases
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|a epidemic
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|a Forecast
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|a Germany
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|a Germany
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|a Germany
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|a High quality information
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|a hospitalization
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|a human
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|a Humans
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|a Image resolution
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|a incidence
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|a infection rate
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|a Infectious disease
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|a intensive care
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|a intensive care unit
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|a lockdown
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|a mathematical model
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|a Medical treatment
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|a mitigation
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|a Mitigation
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|a Models, Statistical
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|a Non-pharmaceutical interventions
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|a Non-pharmaceutical interventions
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|a pandemic
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|a patient isolation
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|a polymerase chain reaction
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|a prediction
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|a prediction
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|a prevention and control
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|a procedures
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|a quarantine
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|a SARS coronavirus
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|a SARS-CoV-2
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|a season
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|a Severe acute respiratory syndrome coronavirus
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|a Severe acute respiratory syndrome coronavirus 2
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|a simulation
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|a Social Network Analysis
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|a spatial analysis
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|a Spatial Analysis
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|a Spatial heterogeneity
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|a Spatial resolution
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|a spatiotemporal analysis
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|a statistical model
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|a vaccination
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|a Abedi, M.
|e author
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|a Abele, D.
|e author
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|a Basermann, A.
|e author
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|a Binder, S.
|e author
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|a Gilg, J.
|e author
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|a Häberle, M.
|e author
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|a Khailaie, S.
|e author
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|a Kleinert, J.
|e author
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|a Klitz, M.
|e author
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|a Koslow, W.
|e author
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|a Kühn, M.J.
|e author
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|a Meyer-Hermann, M.
|e author
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|a Mitra, T.
|e author
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|a Plötzke, L.
|e author
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|a Rack, K.
|e author
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|a Siggel, M.
|e author
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|a Spataro, L.
|e author
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|a Spinner, C.D.
|e author
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|a Stecher, M.
|e author
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|a Zhu, X.X.
|e author
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773 |
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|t Mathematical Biosciences
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