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:	Muhammad Adam Norrulashikin, Fadhilah Yusof, Nur Hanani Mohd Hanafiah, Siti Mariam Norrulashikin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/318c2a339c4243868ae81c06c55e606a
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spelling	oai:doaj.org-article:318c2a339c4243868ae81c06c55e606a2021-12-02T20:06:45ZModelling monthly influenza cases in Malaysia.1932-620310.1371/journal.pone.0254137https://doaj.org/article/318c2a339c4243868ae81c06c55e606a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254137https://doaj.org/toc/1932-6203The 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.Muhammad Adam NorrulashikinFadhilah YusofNur Hanani Mohd HanafiahSiti Mariam NorrulashikinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254137 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad Adam Norrulashikin Fadhilah Yusof Nur Hanani Mohd Hanafiah Siti Mariam Norrulashikin Modelling monthly influenza cases in Malaysia.
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.
format	article
author	Muhammad Adam Norrulashikin Fadhilah Yusof Nur Hanani Mohd Hanafiah Siti Mariam Norrulashikin
author_facet	Muhammad Adam Norrulashikin Fadhilah Yusof Nur Hanani Mohd Hanafiah Siti Mariam Norrulashikin
author_sort	Muhammad Adam Norrulashikin
title	Modelling monthly influenza cases in Malaysia.
title_short	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_sort	modelling monthly influenza cases in malaysia.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/318c2a339c4243868ae81c06c55e606a
work_keys_str_mv	AT muhammadadamnorrulashikin modellingmonthlyinfluenzacasesinmalaysia AT fadhilahyusof modellingmonthlyinfluenzacasesinmalaysia AT nurhananimohdhanafiah modellingmonthlyinfluenzacasesinmalaysia AT sitimariamnorrulashikin modellingmonthlyinfluenzacasesinmalaysia
_version_	1718375353217449984

Modelling monthly influenza cases in Malaysia.

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