Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables
Despite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This...
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042724001222 |
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| author | Xinyi Lu Su Yean Teh Chai Jian Tay Nur Faeza Abu Kassim Pei Shan Fam Edy Soewono |
| author_facet | Xinyi Lu Su Yean Teh Chai Jian Tay Nur Faeza Abu Kassim Pei Shan Fam Edy Soewono |
| author_sort | Xinyi Lu |
| collection | DOAJ |
| description | Despite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This study aims to develop a comprehensive approach for forecasting dengue cases in Selangor, Malaysia by incorporating climate variables. An ensemble of Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM), and Susceptible-Infected mosquito vectors, Susceptible-Infected-Recovered human hosts (SI-SIR) model were used to establish a relation between climate variables (temperature, humidity, precipitation) and mosquito biting rate. Dengue incidence subject to climate variability can then be projected by SI-SIR model using the forecasted mosquito biting rate. The proposed approach outperformed three alternative approaches and expanded the temporal horizon of dengue prediction for Selangor with the ability to forecast approximately 60 weeks ahead with a Mean Absolute Percentage Error (MAPE) of 13.97 for the chosen prediction window before the implementation of the Movement Control Order (MCO) in Malaysia. Extended validation across subsequent periods also indicates relatively satisfactory forecasting performance (with MAPE ranging from 13.12 to 17.09). This research contributed to the field by introducing a novel framework for the prediction of dengue cases over an extended temporal range. |
| format | Article |
| id | doaj-art-62c3a12892bd49b0807fbbd1bdcc7d79 |
| institution | OA Journals |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-62c3a12892bd49b0807fbbd1bdcc7d792025-08-20T01:57:08ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-03-0110124025610.1016/j.idm.2024.10.007Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variablesXinyi Lu0Su Yean Teh1Chai Jian Tay2Nur Faeza Abu Kassim3Pei Shan Fam4Edy Soewono5School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia; Corresponding author.Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300, Gambang, Pahang, MalaysiaSchool of Biological Sciences, Universiti Sains Malaysia, 11800, USM Pulau Pinang, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, MalaysiaCenter of Mathematical Modeling and Simulation, Institut Teknologi Bandung, Bandung, 40132, Indonesia; Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Sumatera, Lampung, 35365, IndonesiaDespite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This study aims to develop a comprehensive approach for forecasting dengue cases in Selangor, Malaysia by incorporating climate variables. An ensemble of Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM), and Susceptible-Infected mosquito vectors, Susceptible-Infected-Recovered human hosts (SI-SIR) model were used to establish a relation between climate variables (temperature, humidity, precipitation) and mosquito biting rate. Dengue incidence subject to climate variability can then be projected by SI-SIR model using the forecasted mosquito biting rate. The proposed approach outperformed three alternative approaches and expanded the temporal horizon of dengue prediction for Selangor with the ability to forecast approximately 60 weeks ahead with a Mean Absolute Percentage Error (MAPE) of 13.97 for the chosen prediction window before the implementation of the Movement Control Order (MCO) in Malaysia. Extended validation across subsequent periods also indicates relatively satisfactory forecasting performance (with MAPE ranging from 13.12 to 17.09). This research contributed to the field by introducing a novel framework for the prediction of dengue cases over an extended temporal range.http://www.sciencedirect.com/science/article/pii/S2468042724001222DengueClimateSIRRegressionDeep learning |
| spellingShingle | Xinyi Lu Su Yean Teh Chai Jian Tay Nur Faeza Abu Kassim Pei Shan Fam Edy Soewono Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables Infectious Disease Modelling Dengue Climate SIR Regression Deep learning |
| title | Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables |
| title_full | Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables |
| title_fullStr | Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables |
| title_full_unstemmed | Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables |
| title_short | Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables |
| title_sort | application of multiple linear regression model and long short term memory with compartmental model to forecast dengue cases in selangor malaysia based on climate variables |
| topic | Dengue Climate SIR Regression Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2468042724001222 |
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