Modeling COVID-19 infection in high-risk settings and low-risk settings

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dc.contributor.authorNdlovu, Meshach
dc.contributor.authorMpofu, Mqhelewenkosi A.
dc.contributor.authorMoyo, Rodwell G.
dc.date.accessioned2023-03-02T14:14:10Z
dc.date.available2023-03-02T14:14:10Z
dc.date.issued2022-11-02
dc.description.abstractIn this research paper we present a mathematical model for COVID-19 in high-risk settings and low-risk settings which might be infection dynamics between hotspots and less risky communities. The main idea was to couple the SIR model with alternating risk levels from the two different settings high and low-risk settings. Therefore, building from this model we partition the infected class into two categories, the symptomatic and the asymptomatic. Using this approach we simulated COVID-19 dynamics in low and high-risk settings with auto-switching risk settings. Again, the model was analyzed using both analytic methods and numerical methods. The results of this study suggest that switching risk levels in different settings plays a pivotal role in COVID-19 progression dynamics. Hence, population reaction time to adhere to preventative measures and interventions ought to be implemented with flash speed targeting first the high-risk setting while containing the dynamics in low-risk settings.en_US
dc.identifier.citationMeshach Ndlovu, Mqhelewenkosi A. Mpofu, Rodwell G. Moyo, Modeling COVID-19 infection in high-risk settings and low-risk settings, Physics and Chemistry of the Earth, Parts A/B/C, Volume 128, 2022, 103288, ISSN 1474-7065, https://doi.org/10.1016/j.pce.2022.103288. (https://www.sciencedirect.com/science/article/pii/S1474706522001814)en_US
dc.identifier.urihttps://doi.org/10.1016/j.pce.2022.103288
dc.identifier.urihttp://ir.gsu.ac.zw:8080/xmlui/handle/123456789/93
dc.language.isoenen_US
dc.publisherElsevier, Science Directen_US
dc.relation.ispartofseriesPhysics and Chemistry of the Earth, Parts A/B/C;Volume 128, December 2022, 103288
dc.subjectCOVID-19, Risk settings, Alternating risk levels, Population dynamicsen_US
dc.titleModeling COVID-19 infection in high-risk settings and low-risk settingsen_US
dc.typeArticleen_US

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