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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Spatial-temporal epidemiologic models of Covid-19
 Schubert, Richard ; Ředina, Richard (referee) ; Mézl, Martin (advisor)
This work aims to establish a fundamental framework for studying spatially diffusive models that describe the dynamics of infectious disease spread with constant parameters in a homogeneous domain. Initially, compartmental models and their extension to spatial domains are examined, followed by the theory of metapopulation models, where the degree of coupling between populations and the overall reproductive number R0 is discussed. Furthermore, the relationship between R0 and the shape of the spatial distribution of infected individuals in a simple diffusive SIR model is modeled. The influence of Neumann boundary conditions versus Dirichlet boundary conditions on R0 is demonstrated. In the second part of the work, selected findings and conclusions of studies that applied models in the spatiotemporal domain to analyze and predict the COVID-19 pandemic are summarized. In the third part of the work, a model with diffusive and metapopulation elements is fitted to epidemiological data from Lombardy in 2020, and the suitability of this approach is discussed.
Detailed record
Spatial-temporal epidemiologic models of Covid-19
 Schubert, Richard ; Ředina, Richard (referee) ; Mézl, Martin (advisor)
This work aims to establish a fundamental framework for studying spatially diffusive models that describe the dynamics of infectious disease spread with constant parameters in a homogeneous domain. Initially, compartmental models and their extension to spatial domains are examined, followed by the theory of metapopulation models, where the degree of coupling between populations and the overall reproductive number R0 is discussed. Furthermore, the relationship between R0 and the shape of the spatial distribution of infected individuals in a simple diffusive SIR model is modeled. The influence of Neumann boundary conditions versus Dirichlet boundary conditions on R0 is demonstrated. In the second part of the work, selected findings and conclusions of studies that applied models in the spatiotemporal domain to analyze and predict the COVID-19 pandemic are summarized. In the third part of the work, a model with diffusive and metapopulation elements is fitted to epidemiological data from Lombardy in 2020, and the suitability of this approach is discussed.
Detailed record

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