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

• Published in: Infectious disease modelling Vol. 10; no. 1; pp. 240 - 256
• Main Authors: Lu, Xinyi, Teh, Su Yean, Tay, Chai Jian, Abu Kassim, Nur Faeza, Fam, Pei Shan, Soewono, Edy
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01.03.2025; KeAi Publishing; KeAi Communications Co., Ltd
• Subjects: Climate; Deep learning; Dengue; Regression; SIR; Deep learning; Regression; Climate; SIR; Dengue
• ISSN: 2468-0427; 2468-2152; 2468-0427
• DOI: 10.1016/j.idm.2024.10.007

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.