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.

Autor:	Lu X; School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia., Teh SY; School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia., Tay CJ; Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300, Gambang, Pahang, Malaysia., Abu Kassim NF; School of Biological Sciences, Universiti Sains Malaysia, 11800, USM Pulau Pinang, Malaysia., Fam PS; School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia., Soewono E; Center 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, Indonesia.
Jazyk:	angličtina
Zdroj:	Infectious Disease Modelling [Infect Dis Model] 2024 Oct 28; Vol. 10 (1), pp. 240-256. Date of Electronic Publication: 2024 Oct 28 (Print Publication: 2025).
DOI:	10.1016/j.idm.2024.10.007
Abstrakt:	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. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu