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Modelling COVID-19 infection with seasonality in Zimbabwe

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dc.contributor.author Ndlovu, Meshach
dc.contributor.author Moyo, Rodwell
dc.contributor.author Mpofu, Mqhelewenkosi
dc.date.accessioned 2022-10-26T18:02:38Z
dc.date.available 2022-10-26T18:02:38Z
dc.date.issued 2022-05-25
dc.identifier.uri https://doi.org/10.1016/j.pce.2022.103167
dc.identifier.uri http://ir.gsu.ac.zw:8080/xmlui/handle/123456789/65
dc.description.abstract This 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.language.iso en en_US
dc.publisher Elsevier, Science Direct en_US
dc.relation.ispartofseries Physics and Chemistry of the Earth;127 (2022) 103167
dc.subject COVID-19, Seasonality effect, Mathematical modelling, Dynamics, Environmental factors, Zimbabwe en_US
dc.title Modelling COVID-19 infection with seasonality in Zimbabwe en_US
dc.type Article en_US

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