Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. On...

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Autores principales:	Titi Purwandari, Solichatus Zahroh, Yuyun Hidayat, Sukonob Sukonob, Mustafa Mamat, Jumadil Saputra
Formato:	article
Lenguaje:	EN
Publicado:	Growing Science 2022
Materias:	Analysis QA299.6-433 Business mathematics. Commercial arithmetic. Including tables, etc. HF5691-5716
Acceso en línea:	https://doaj.org/article/8d1424cb59a941748a5a8593e9aa20ff
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spelling	oai:doaj.org-article:8d1424cb59a941748a5a8593e9aa20ff2021-11-12T06:26:20ZForecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network1929-58041929-581210.5267/j.dsl.2021.10.001https://doaj.org/article/8d1424cb59a941748a5a8593e9aa20ff2022-01-01T00:00:00Zhttp://www.growingscience.com/dsl/Vol11/dsl_2021_34.pdfhttps://doaj.org/toc/1929-5804https://doaj.org/toc/1929-5812 COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.Titi PurwandariSolichatus ZahrohYuyun HidayatSukonob SukonobMustafa MamatJumadil SaputraGrowing SciencearticleAnalysisQA299.6-433Business mathematics. Commercial arithmetic. Including tables, etc.HF5691-5716ENDecision Science Letters, Vol 11, Iss 1, Pp 35-42 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Analysis QA299.6-433 Business mathematics. Commercial arithmetic. Including tables, etc. HF5691-5716
spellingShingle	Analysis QA299.6-433 Business mathematics. Commercial arithmetic. Including tables, etc. HF5691-5716 Titi Purwandari Solichatus Zahroh Yuyun Hidayat Sukonob Sukonob Mustafa Mamat Jumadil Saputra Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network
description	COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.
format	article
author	Titi Purwandari Solichatus Zahroh Yuyun Hidayat Sukonob Sukonob Mustafa Mamat Jumadil Saputra
author_facet	Titi Purwandari Solichatus Zahroh Yuyun Hidayat Sukonob Sukonob Mustafa Mamat Jumadil Saputra
author_sort	Titi Purwandari
title	Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network
title_short	Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network
title_full	Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network
title_fullStr	Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network
title_full_unstemmed	Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network
title_sort	forecasting model of covid-19 pandemic in malaysia: an application of time series approach using neural network
publisher	Growing Science
publishDate	2022
url	https://doaj.org/article/8d1424cb59a941748a5a8593e9aa20ff
work_keys_str_mv	AT titipurwandari forecastingmodelofcovid19pandemicinmalaysiaanapplicationoftimeseriesapproachusingneuralnetwork AT solichatuszahroh forecastingmodelofcovid19pandemicinmalaysiaanapplicationoftimeseriesapproachusingneuralnetwork AT yuyunhidayat forecastingmodelofcovid19pandemicinmalaysiaanapplicationoftimeseriesapproachusingneuralnetwork AT sukonobsukonob forecastingmodelofcovid19pandemicinmalaysiaanapplicationoftimeseriesapproachusingneuralnetwork AT mustafamamat forecastingmodelofcovid19pandemicinmalaysiaanapplicationoftimeseriesapproachusingneuralnetwork AT jumadilsaputra forecastingmodelofcovid19pandemicinmalaysiaanapplicationoftimeseriesapproachusingneuralnetwork
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Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

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