Parameter Estimation of a Climate-Based Dengue Mathematical Model in Bandung City Using the Particle Swarm Optimization Algorithm

Sindi Meli Nur Afni; Khusnul Novianingsih; Ririn Sispiyati

doi:10.37905/jjom.v8i2.39153

Parameter Estimation of a Climate-Based Dengue Mathematical Model in Bandung City Using the Particle Swarm Optimization Algorithm

Sindi Meli Nur Afni, Khusnul Novianingsih, Ririn Sispiyati

Abstract

Dengue Hemorrhagic Fever (DHF) is spread through the bite of the Aedes aegypti mosquito and is affected by the environment, especially temperature and rainfall. The purpose of this study is to create a SEIR (Susceptible–Exposed–Infected–Recovered) mathematical model taking into account the effects of climate factors and to estimate the parameters of this model using the Particle Swarm Optimization (PSO) algorithm. The analyzed data consisted of monthly reports of the number of dengue fever cases, temperature, and rainfall for the city of Bandung in 2022–2023 and were smoothed using a moving average. The parameter estimation process was performed by minimizing the Mean Absolute Percentage Error (MAPE), and the numerical simulation of the model was performed using the fourth-order Runge–Kutta method (RK4). The results of this study show that the model has two equilibrium points: a disease-free equilibrium point and an endemic equilibrium point, and the stability of the equilibrium points depends on the basic reproduction number R0. The best parameters obtained were β0 = 3.5553, β1 = 0.0021, β2 = 0.0001, σ = 7.5, µ = 0.0011, and γ = 3.6865, with an MAPE value of 0.1740 or 17.40%. These findings indicate that the model is able to represent the pattern of dengue fever spread with a low level of prediction error. Sensitivity analysis showed that the recovery rate parameter (γ) was the most responsive to changes in the model.

Keywords

Dengue Hemorrhagic Fever; SEIR Model; Parameter Estimation; Particle Swarm Optimization; MAPE

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DOI: https://doi.org/10.37905/jjom.v8i2.39153