Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models

Currently, no vaccines or specific treatments are available to treat or prevent the increasing incidence of dengue worldwide. Therefore, an accurate prediction model is needed to support the anti-dengue control strategy. The primary objective of this study is to develop the most accurate model to pr...

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Published in:International Journal of Advanced and Applied Sciences
Main Author: Mustaffa N.A.; Zahari S.M.; Farhana N.A.; Nasir N.; Azil A.H.
Format: Article
Language:English
Published: Institute of Advanced Science Extension (IASE) 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188053097&doi=10.21833%2fijaas.2024.01.003&partnerID=40&md5=462635136c4dd6a657168d4c558d855a
id 2-s2.0-85188053097
spelling 2-s2.0-85188053097
Mustaffa N.A.; Zahari S.M.; Farhana N.A.; Nasir N.; Azil A.H.
Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
2024
International Journal of Advanced and Applied Sciences
11
1
10.21833/ijaas.2024.01.003
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188053097&doi=10.21833%2fijaas.2024.01.003&partnerID=40&md5=462635136c4dd6a657168d4c558d855a
Currently, no vaccines or specific treatments are available to treat or prevent the increasing incidence of dengue worldwide. Therefore, an accurate prediction model is needed to support the anti-dengue control strategy. The primary objective of this study is to develop the most accurate model to predict future dengue cases in the Malaysian environment. This study uses secondary data collected from the weekly reports of the Ministry of Health Malaysia (MOH) website over six years, from 2017 to 2022. Three forecasting techniques, including seasonal autoregressive integrated moving average (SARIMA), dynamic harmonic regression (DHR), and neural network autoregressive model (NNAR), were first fitted to the estimation part of the data. First, several SARIMA models were run, and the best seasonal model identified was SARIMA (0, 1, 2) (1, 1, 1)52. The best DHR model was obtained with a Fourier term of 2, as this corresponds to the lowest Akaike Information Criteria (AIC) value. The NNAR (9, 1, 6)52 was considered the best choice among the NNAR models due to its superior performance in terms of the lowest error measures. The comparison among the three techniques revealed that the DHR model was the best due to its lowest MAPE and RMSE values. Thus, the DHR model was used to generate future forecasts of weekly dengue cases in Malaysia until 2023. The results showed that the model predicted more than a thousand dengue cases around weeks 27 to 32. The results showed an increase in dengue cases after the end of the monsoon season, which lasted about five months. This technique is proving to be valuable for health administrators in improving preparedness. © 2023 The Authors. Published by IASE.
Institute of Advanced Science Extension (IASE)
2313626X
English
Article
All Open Access; Gold Open Access
author Mustaffa N.A.; Zahari S.M.; Farhana N.A.; Nasir N.; Azil A.H.
spellingShingle Mustaffa N.A.; Zahari S.M.; Farhana N.A.; Nasir N.; Azil A.H.
Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
author_facet Mustaffa N.A.; Zahari S.M.; Farhana N.A.; Nasir N.; Azil A.H.
author_sort Mustaffa N.A.; Zahari S.M.; Farhana N.A.; Nasir N.; Azil A.H.
title Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
title_short Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
title_full Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
title_fullStr Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
title_full_unstemmed Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
title_sort Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models
publishDate 2024
container_title International Journal of Advanced and Applied Sciences
container_volume 11
container_issue 1
doi_str_mv 10.21833/ijaas.2024.01.003
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188053097&doi=10.21833%2fijaas.2024.01.003&partnerID=40&md5=462635136c4dd6a657168d4c558d855a
description Currently, no vaccines or specific treatments are available to treat or prevent the increasing incidence of dengue worldwide. Therefore, an accurate prediction model is needed to support the anti-dengue control strategy. The primary objective of this study is to develop the most accurate model to predict future dengue cases in the Malaysian environment. This study uses secondary data collected from the weekly reports of the Ministry of Health Malaysia (MOH) website over six years, from 2017 to 2022. Three forecasting techniques, including seasonal autoregressive integrated moving average (SARIMA), dynamic harmonic regression (DHR), and neural network autoregressive model (NNAR), were first fitted to the estimation part of the data. First, several SARIMA models were run, and the best seasonal model identified was SARIMA (0, 1, 2) (1, 1, 1)52. The best DHR model was obtained with a Fourier term of 2, as this corresponds to the lowest Akaike Information Criteria (AIC) value. The NNAR (9, 1, 6)52 was considered the best choice among the NNAR models due to its superior performance in terms of the lowest error measures. The comparison among the three techniques revealed that the DHR model was the best due to its lowest MAPE and RMSE values. Thus, the DHR model was used to generate future forecasts of weekly dengue cases in Malaysia until 2023. The results showed that the model predicted more than a thousand dengue cases around weeks 27 to 32. The results showed an increase in dengue cases after the end of the monsoon season, which lasted about five months. This technique is proving to be valuable for health administrators in improving preparedness. © 2023 The Authors. Published by IASE.
publisher Institute of Advanced Science Extension (IASE)
issn 2313626X
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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