Modelling COVID-19 infection with seasonality in Zimbabwe

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...

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Detalles Bibliográficos
Autores principales: Ndlovu, Meshach, Moyo, Rodwell, Mpofu, Mqhelewenkosi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132494/
https://www.ncbi.nlm.nih.gov/pubmed/35642222
http://dx.doi.org/10.1016/j.pce.2022.103167
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author Ndlovu, Meshach
Moyo, Rodwell
Mpofu, Mqhelewenkosi
author_facet Ndlovu, Meshach
Moyo, Rodwell
Mpofu, Mqhelewenkosi
author_sort Ndlovu, Meshach
collection PubMed
description 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.
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spelling pubmed-91324942022-05-26 Modelling COVID-19 infection with seasonality in Zimbabwe Ndlovu, Meshach Moyo, Rodwell Mpofu, Mqhelewenkosi Phys Chem Earth (2002) Article 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. Elsevier Ltd. 2022-10 2022-05-25 /pmc/articles/PMC9132494/ /pubmed/35642222 http://dx.doi.org/10.1016/j.pce.2022.103167 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Ndlovu, Meshach
Moyo, Rodwell
Mpofu, Mqhelewenkosi
Modelling COVID-19 infection with seasonality in Zimbabwe
title Modelling COVID-19 infection with seasonality in Zimbabwe
title_full Modelling COVID-19 infection with seasonality in Zimbabwe
title_fullStr Modelling COVID-19 infection with seasonality in Zimbabwe
title_full_unstemmed Modelling COVID-19 infection with seasonality in Zimbabwe
title_short Modelling COVID-19 infection with seasonality in Zimbabwe
title_sort modelling covid-19 infection with seasonality in zimbabwe
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132494/
https://www.ncbi.nlm.nih.gov/pubmed/35642222
http://dx.doi.org/10.1016/j.pce.2022.103167
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