Modelling COVID-19 infection with seasonality in Zimbabwe

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