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Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region

BACKGROUND: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other a...

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Autores principales: Shoko, Claris, Sigauke, Caston, Njuho, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Makerere Medical School 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117507/
https://www.ncbi.nlm.nih.gov/pubmed/37092045
http://dx.doi.org/10.4314/ahs.v22i4.60
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author Shoko, Claris
Sigauke, Caston
Njuho, Peter
author_facet Shoko, Claris
Sigauke, Caston
Njuho, Peter
author_sort Shoko, Claris
collection PubMed
description BACKGROUND: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem. OBJECTIVE: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region. METHODS: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM). RESULTS: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278). CONCLUSION: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach.
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spelling pubmed-101175072023-04-21 Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region Shoko, Claris Sigauke, Caston Njuho, Peter Afr Health Sci Articles BACKGROUND: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem. OBJECTIVE: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region. METHODS: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM). RESULTS: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278). CONCLUSION: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach. Makerere Medical School 2022-12 /pmc/articles/PMC10117507/ /pubmed/37092045 http://dx.doi.org/10.4314/ahs.v22i4.60 Text en © 2022 Shoko C et al. https://creativecommons.org/licenses/by/4.0/Licensee African Health Sciences. This is an Open Access article distributed under the terms of the Creative commons Attribution License (https://creativecommons.org/licenses/BY/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Shoko, Claris
Sigauke, Caston
Njuho, Peter
Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region
title Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region
title_full Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region
title_fullStr Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region
title_full_unstemmed Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region
title_short Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region
title_sort short-term forecasting of confirmed daily covid-19 cases in the southern african development community region
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117507/
https://www.ncbi.nlm.nih.gov/pubmed/37092045
http://dx.doi.org/10.4314/ahs.v22i4.60
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