Space-time conditional autoregressive modeling to estimate neighborhood-level risks for dengue fever in Cali, Colombia

摘要: Vector-borne diseases (VBDs) affect more than 1 billion people a year worldwide, cause over million deaths, and cost hundreds of billions dollars in societal costs. Mosquitoes are the most common vectors, responsible for transmitting variety arboviruses. Dengue fever (DENF) has been nearly 400 infections annually. is primarily transmitted by female Aedes aegypti albopictus mosquitoes. Since both species peri-domestic container-breeding mosquitoes, dengue surveillance should begin at local level - where factors may increase risk transmission. endemic Colombia decades notably hyperendemic city Cali. For this study, we use weekly cases DENF Cali, from 2015-2016; develop space-time conditional autoregressive models to quantify how influenced socioeconomic, environmental, accessibility factors, lagged weather variables. Our identify high-risk neighborhoods throughout Statistical inference drawn under Bayesian paradigm using Markov Chain Monte Carlo techniques. The results provide detailed insight about spatial heterogeneity associated (such as weather, proximity habitats, socioeconomic classification) fine-level, informing public health officials motivate at-risk take an active role vector control, improving educational resources