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dc.contributor.authorMashanova, Alla
dc.contributor.authorMashanov, Gregory
dc.date.accessioned2020-06-16T00:08:37Z
dc.date.available2020-06-16T00:08:37Z
dc.date.issued2020-04-29
dc.identifier.citationMashanova , A & Mashanov , G 2020 ' The role of spatial structure in the infection spread models: population density map of England example ' medRxiv . https://doi.org/10.1101/2020.04.24.20077289
dc.identifier.otherPURE: 22040031
dc.identifier.otherPURE UUID: 1b4af3d7-0ae5-4a05-93be-33e6127be4c4
dc.identifier.otherORCID: /0000-0003-3273-8184/work/75948207
dc.identifier.urihttp://hdl.handle.net/2299/22858
dc.descriptionThe copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license: https://creativecommons.org/licenses/by-nc-nd/4.0/.
dc.description.abstractIn the current situation of a pandemic caused by COVID-19 developing models accurately predicting the dynamics of the outbreaks in time and space became extremely important. Individual-based models (IBM) simulating the spread of infection in a population have a few advantages compared to classical equation-based approach. First, they use individuals as units, which represent the population, and reflect the local variations happening in real life. Second, the simplicity of modelling the interactions between the individuals, which may not be the case when using differential equations.We propose to use freely available population density maps to simulate the infection spread in the human population on the scale of an individual country or a city. We explore the effect of social distancing and show that it can reduce the outbreak when applied before or during peak time, but it can also inflict the second wave when relaxed after the peak. This can be explained by a large proportion of susceptible individuals, even in the large cities, after the first wave.The model can be adapted to any spatial scale from a single hospital to multiple countries.en
dc.format.extent14
dc.language.isoeng
dc.publishermedRxiv
dc.titleThe role of spatial structure in the infection spread models: population density map of England exampleen
dc.contributor.institutionSchool of Life and Medical Sciences
dc.contributor.institutionDepartment of Biological and Environmental Sciences
dc.contributor.institutionAgriculture, Veterinary and Food Sciences
dc.contributor.institutionGeography, Environment and Agriculture
dc.contributor.institutionCrop and Environmental Protection
dc.contributor.institutionEcology
dc.contributor.institutionCentre for Agriculture, Food and Environmental Management Research
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1101/2020.04.24.20077289
rioxxterms.typeWorking paper
herts.preservation.rarelyaccessedtrue


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