Medical statistics collected by WHO indicates that dengue fever is still ravaging developing regions with climates befitting mosquito breeding amidst moderate-to-weak health systems. This work initiates a study over 2009–2017 panel data of dengue incidences and meteorological factors in Jakarta, Indonesia to bear particular understanding. Using a panel random-effect model joined by the pooled estimator, we show positively significant relationships between the incidence level and meteorological factors. We ideate a clustering strategy to decompose the meteorological datasets into several more datasets such that more explanatory variables are present and the zero-inflated problem from the incidence data can be handled properly. The resulting new model gives good agreement with the incidence data accompanied by a high coefficient of determination and normal zero-mean error in the prediction window. A risk measure is characterized from a one-step vector autoregression model relying solely on the incidence data and a threshold incidence level separating the low-risk and high-risk regime. Its magnitude greater than unity and the weak stochastic convergence to the endemic equilibrium mark a persistent cyclicality of the disease in all the five districts in Jakarta. Moreover, all districts are shown to co-vary profoundly positively in terms of epidemics occurrence, both generally and timely. We also show that the peak of incidences propagates almost periodically every year on the districts with the most to the least recurrence: Central, South, West, East, and North Jakarta.

世卫组织收集的医学统计数据表明，登革热仍在中，弱卫生系统中肆虐，气候适宜于蚊子繁殖。这项工作启动了一项针对2009-2017年印尼雅加达登革热发病率和气象因素的面板数据的研究，以加深了解。使用由合并估计量组成的面板随机效应模型，我们显示了发生率和气象因素之间的正相关关系。我们提出了一种聚类策略，将气象数据集分解为更多的数据集，以便存在更多的解释变量，并且可以正确处理突发事件数据中的零膨胀问题。所得的新模型与入射数据具有良好的一致性，并伴随着较高的确定系数和预测窗口中的正常零均值误差。风险度量的特征在于，仅依靠发病数据的单步向量自回归模型和将低风险和高风险方案分开的阈值发病水平。雅加达的所有五个地区的疫情规模都大于统一，随机收敛到地方均衡的弱势，标志着该病的持续周期性。此外，在流行病的发生方面，所有地区都普遍和及时地表现出深刻的积极变化。我们还显示，发病率的高峰几乎每年每年在雅加达中部，南部，西部，东部和北部复发率最高的区域中周期性地传播。