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Stochastic spatial model for Coronavirus spread
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Introduces the metapopulation framework for the modelling of infectious diseases, by outlining the various types of metapopulation framework, such as cross-coupled and mobility models, which can be viewed as assemblage of SIR type compartmental models applied to spatial/regional distribution of population.
It then discusses the deterministic and stochastic versions of the metapopulation models, and then focusses on a stochastic metapopulation model (Danon-House-Keeling, A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing) that has been used to model the spread of the coronavirus (COVID-19) in the UK. This model forms part of the so called scientific evidence considered by the SAGE (scientific advisory group for emergencies) in formulating the UK lockdown strategy. The model code and data are freely available, so this is a perfect opportunity to see how the epidemics are modelled by practitioners. And as the model parameters were calibrated based on early evidence, there is an opportunity to update the parameters and assumptions based on more recent evidence, and contribute to informing the lockdown exit strategy. Many thanks for reading the description!
It then discusses the deterministic and stochastic versions of the metapopulation models, and then focusses on a stochastic metapopulation model (Danon-House-Keeling, A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing) that has been used to model the spread of the coronavirus (COVID-19) in the UK. This model forms part of the so called scientific evidence considered by the SAGE (scientific advisory group for emergencies) in formulating the UK lockdown strategy. The model code and data are freely available, so this is a perfect opportunity to see how the epidemics are modelled by practitioners. And as the model parameters were calibrated based on early evidence, there is an opportunity to update the parameters and assumptions based on more recent evidence, and contribute to informing the lockdown exit strategy. Many thanks for reading the description!
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