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.Statisti...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 usingMarkovChainMonteCarlo(MCMC)usingmonthlyDHF data from10 districts inKendari-Indonesia from2020–2023.WhileMCMC 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 toMCMC.Risk analysis using INLA highlights dengue case dynamics fromJanuary toMay 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 andWua-Wua districts.展开更多
基金support from the Kementerian Pendidikan,Kebudayaan,Riset,dan Teknologi of Indonesia through Regular Fundamental Grant No.049/E5/PG.02.00.PL/2024.
文摘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 usingMarkovChainMonteCarlo(MCMC)usingmonthlyDHF data from10 districts inKendari-Indonesia from2020–2023.WhileMCMC 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 toMCMC.Risk analysis using INLA highlights dengue case dynamics fromJanuary toMay 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 andWua-Wua districts.
