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The practicality of Malaysia dengue outbreak forecasting model as an early warning system

Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a deng...

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Autores principales: Ismail, Suzilah, Fildes, Robert, Ahmad, Rohani, Wan Mohamad Ali, Wan Najdah, Omar, Topek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418377/
https://www.ncbi.nlm.nih.gov/pubmed/36091345
http://dx.doi.org/10.1016/j.idm.2022.07.008
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author Ismail, Suzilah
Fildes, Robert
Ahmad, Rohani
Wan Mohamad Ali, Wan Najdah
Omar, Topek
author_facet Ismail, Suzilah
Fildes, Robert
Ahmad, Rohani
Wan Mohamad Ali, Wan Najdah
Omar, Topek
author_sort Ismail, Suzilah
collection PubMed
description Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3–4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cure.
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spelling pubmed-94183772022-09-08 The practicality of Malaysia dengue outbreak forecasting model as an early warning system Ismail, Suzilah Fildes, Robert Ahmad, Rohani Wan Mohamad Ali, Wan Najdah Omar, Topek Infect Dis Model Article Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3–4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cure. KeAi Publishing 2022-08-08 /pmc/articles/PMC9418377/ /pubmed/36091345 http://dx.doi.org/10.1016/j.idm.2022.07.008 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Ismail, Suzilah
Fildes, Robert
Ahmad, Rohani
Wan Mohamad Ali, Wan Najdah
Omar, Topek
The practicality of Malaysia dengue outbreak forecasting model as an early warning system
title The practicality of Malaysia dengue outbreak forecasting model as an early warning system
title_full The practicality of Malaysia dengue outbreak forecasting model as an early warning system
title_fullStr The practicality of Malaysia dengue outbreak forecasting model as an early warning system
title_full_unstemmed The practicality of Malaysia dengue outbreak forecasting model as an early warning system
title_short The practicality of Malaysia dengue outbreak forecasting model as an early warning system
title_sort practicality of malaysia dengue outbreak forecasting model as an early warning system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418377/
https://www.ncbi.nlm.nih.gov/pubmed/36091345
http://dx.doi.org/10.1016/j.idm.2022.07.008
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