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Modelling monthly influenza cases in Malaysia

The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically...

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Autores principales: Norrulashikin, Muhammad Adam, Yusof, Fadhilah, Hanafiah, Nur Hanani Mohd, Norrulashikin, Siti Mariam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294481/
https://www.ncbi.nlm.nih.gov/pubmed/34288925
http://dx.doi.org/10.1371/journal.pone.0254137
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author Norrulashikin, Muhammad Adam
Yusof, Fadhilah
Hanafiah, Nur Hanani Mohd
Norrulashikin, Siti Mariam
author_facet Norrulashikin, Muhammad Adam
Yusof, Fadhilah
Hanafiah, Nur Hanani Mohd
Norrulashikin, Siti Mariam
author_sort Norrulashikin, Muhammad Adam
collection PubMed
description The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.
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spelling pubmed-82944812021-07-31 Modelling monthly influenza cases in Malaysia Norrulashikin, Muhammad Adam Yusof, Fadhilah Hanafiah, Nur Hanani Mohd Norrulashikin, Siti Mariam PLoS One Research Article The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model. Public Library of Science 2021-07-21 /pmc/articles/PMC8294481/ /pubmed/34288925 http://dx.doi.org/10.1371/journal.pone.0254137 Text en © 2021 Norrulashikin et al https://creativecommons.org/licenses/by/4.0/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 author and source are credited.
spellingShingle Research Article
Norrulashikin, Muhammad Adam
Yusof, Fadhilah
Hanafiah, Nur Hanani Mohd
Norrulashikin, Siti Mariam
Modelling monthly influenza cases in Malaysia
title Modelling monthly influenza cases in Malaysia
title_full Modelling monthly influenza cases in Malaysia
title_fullStr Modelling monthly influenza cases in Malaysia
title_full_unstemmed Modelling monthly influenza cases in Malaysia
title_short Modelling monthly influenza cases in Malaysia
title_sort modelling monthly influenza cases in malaysia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294481/
https://www.ncbi.nlm.nih.gov/pubmed/34288925
http://dx.doi.org/10.1371/journal.pone.0254137
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