TY  - EJOU
AU  - Mukhsar, 
AU  - Tenriawaru, Andi 
AU  - Wibawa, Gusti Ngurah Adhi 
AU  - Abapihi, Bahriddin 
AU  - Ahmad, Sitti Wirdhana 
AU  - Sudayasa, I Putu 

TI  - Bayesian Stochastic INLA Application to the SIR-SI Model for Investigating Dengue Transmission Dynamics
T2  - Intelligent Automation \& Soft Computing

PY  - 2025
VL  - 40
IS  - 1
SN  - 2326-005X

AB  - Despite extensive prevention efforts and research, dengue hemorrhagic fever (DHF) remains a major public health challenge, particularly in tropical regions, with significant social, economic, and health consequences. Statistical models are crucial in studying infectious DHF by providing a structured framework to analyze transmission dynamics between humans (hosts) and mosquitoes (vectors). Depending on the disease characteristics, different stochastic compartmental models can be employed. This research applies Bayesian Integrated Nested Laplace Approximation (INLA) to the SIR-SI model for DHF data. The method delivers accurate parameter estimates, improved computational efficiency, and effective integration with early warning systems. The model compared to existing work using Markov Chain Monte Carlo (MCMC) using monthly DHF data from 10 districts in Kendari-Indonesia from 2020–2023. While MCMC requires 10,000 iterations with an 80,000 burn-in, INLA achieves parameter convergence with just 10,000 iterations. The parameter estimation results show that INLA provides a better fit, with the lowest deviance = 105.23, compared to MCMC. Risk analysis using INLA highlights dengue case dynamics from January to May each year. Kadia and Wua-Wua districts consistently show high case numbers, emphasizing the need for targeted interventions in Kendari City. Early surveillance and control efforts are essential to curb mosquito breeding in these areas starting in January. In contrast, the Puuwatu, Kambu, and Kendari Barat districts are sporadic outbreaks, often linked to cases originating in Kadia and Wua-Wua districts.
KW  - Bayesian; DHF (Dengue Hemorrhagic Fever); dynamic risk; INLA; SIR-SI model

DO  - 10.32604/iasc.2025.058884