Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with microclimates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks. Climate information was incorporated to model and forecast the dengue cases and relative risks using a Bayesian spatio-temporal model, from 2000 to 2021, in 32 Costa Rican municipalities. This approach is capable of analyzing the spatio-temporal behavior of dengue and also producing reliable predictions.

由于伊蚊在地理上的快速传播和登革热发病率的增加，登革热已成为全球热带和亚热带国家公共卫生当局日益关注的问题。气候变化、卫生系统负担以及杀虫剂耐药性上升等重大挑战突出表明需要引入新的、具有成本效益的工具来制定公共卫生干预措施。用于开发基于气候的预警系统的各种适合当地的统计方法已日益成为全世界关注和研究的领域。哥斯达黎加是一个小气候，自 1993 年以来登革热病毒 (DENV) 流行的国家，为开发预测模型提供了理想的条件，该模型有可能帮助指导公共卫生工作和干预措施，以控制和监测未来的登革热疫情。使用贝叶斯时空模型纳入气候信息，对 2000 年至 2021 年哥斯达黎加 32 个城市的登革热病例和相关风险进行建模和预测。这种方法能够分析登革热的时空行为并产生可靠的预测。