Mukhsar1,*, Andi Tenriawaru2, Gusti Ngurah Adhi Wibawa1, Bahriddin Abapihi1, Sitti Wirdhana Ahmad3, I Putu Sudayasa4
Intelligent Automation & Soft Computing, Vol.40, pp. 177-193, 2025, DOI:10.32604/iasc.2025.058884
- 24 February 2025
Abstract 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… More >
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