Modelling COVID-19 infection with seasonality in Zimbabwe

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dc.contributor.authorNdlovu, Meshach
dc.contributor.authorMoyo, Rodwell
dc.contributor.authorMpofu, Mqhelewenkosi
dc.date.accessioned2022-10-26T18:02:38Z
dc.date.available2022-10-26T18:02:38Z
dc.date.issued2022-05-25
dc.description.abstractThis paper presents evidence and the existence of seasonality in current existing COVID-19 datasets for three different countries namely Zimbabwe, South Africa, and Botswana. Therefore, we modified the SVIR model through factoring in the seasonality effect by incorporating moving averages and signal processing techniques to the disease transmission rate. The simulation results strongly established the existence of seasonality in COVID-19 dynamics with a correlation of 0.746 between models with seasonality effect at 0.001 significance level. Finally, the model was used to predict the magnitude and occurrence of the fourth wave.en_US
dc.identifier.urihttps://doi.org/10.1016/j.pce.2022.103167
dc.identifier.urihttp://ir.gsu.ac.zw:8080/xmlui/handle/123456789/65
dc.language.isoenen_US
dc.publisherElsevier, Science Directen_US
dc.relation.ispartofseriesPhysics and Chemistry of the Earth;127 (2022) 103167
dc.subjectCOVID-19, Seasonality effect, Mathematical modelling, Dynamics, Environmental factors, Zimbabween_US
dc.titleModelling COVID-19 infection with seasonality in Zimbabween_US
dc.typeArticleen_US

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