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ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION
BACKGROUND: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
African Traditional Herbal Medicine Supporters Initiative (ATHMSI)
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885024/ https://www.ncbi.nlm.nih.gov/pubmed/36756487 http://dx.doi.org/10.21010/Ajidv17i1.1 |
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author | Claris, SHOKO Peter, NJUHO |
author_facet | Claris, SHOKO Peter, NJUHO |
author_sort | Claris, SHOKO |
collection | PubMed |
description | BACKGROUND: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. METHODS: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. RESULTS: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box–Ljung test. The ARIMA(11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. CONCLUSION: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population. |
format | Online Article Text |
id | pubmed-9885024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | African Traditional Herbal Medicine Supporters Initiative (ATHMSI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-98850242023-02-07 ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION Claris, SHOKO Peter, NJUHO Afr J Infect Dis Article BACKGROUND: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. METHODS: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. RESULTS: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box–Ljung test. The ARIMA(11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. CONCLUSION: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population. African Traditional Herbal Medicine Supporters Initiative (ATHMSI) 2022-12-22 /pmc/articles/PMC9885024/ /pubmed/36756487 http://dx.doi.org/10.21010/Ajidv17i1.1 Text en Copyright: © 2023 Afr. J. Infect. Diseases https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License |
spellingShingle | Article Claris, SHOKO Peter, NJUHO ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION |
title | ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION |
title_full | ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION |
title_fullStr | ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION |
title_full_unstemmed | ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION |
title_short | ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION |
title_sort | arima model in predicting of covid-19 epidemic for the southern africa region |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885024/ https://www.ncbi.nlm.nih.gov/pubmed/36756487 http://dx.doi.org/10.21010/Ajidv17i1.1 |
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