Nuraini Nuning, Fauzi Ilham Saiful, Fakhruddin Muhammad, Sopaheluwakan Ardhasena, Soewono Edy
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia.
Center for Mathematical Modeling and Simulation, Institut Teknologi Bandung, Bandung, 40132, Indonesia.
Infect Dis Model. 2021 Mar 24;6:598-611. doi: 10.1016/j.idm.2021.03.005. eCollection 2021.
Dengue is one of the most rapidly spreading vector-borne diseases, which is considered to be a major health concern in tropical and sub-tropical countries. It is strongly believed that the spread and abundance of vectors are related to climate. Construction of climate-based mathematical model that integrates meteorological factors into disease infection model becomes compelling challenge since the climate is positively associated with both incidence and vector existence.
A host-vector model is constructed to simulate the dynamic of transmission. The infection rate parameter is replaced with the time-dependent coefficient obtained by optimization to approximate the daily dengue data. Further, the optimized infection rate is denoted as a function of climate variables using the Autoregressive Distributed Lag (ARDL) model.
The infection parameter can be extended when updated daily climates are known, and it can be useful to forecast dengue incidence. This approach provides proper prediction, even when tested in increasing or decreasing prediction windows. In addition, associations between climate and dengue are presented as a reversed slide-shaped curve for dengue-humidity and a reversed U-shaped curves for dengue-temperature and dengue-precipitation. The range of optimal temperature for infection is 24.3-30.5 °C. Humidity and precipitation are positively associated with dengue upper the threshold 70 at lag 38 days and below 50 mm at lag 50 days, respectively.
Identification of association between climate and dengue is potentially useful to counter the high risk of dengue and strengthen the public health system and reduce the increase of the dengue burden.
登革热是传播速度最快的媒介传播疾病之一，被认为是热带和亚热带国家主要的健康问题。人们坚信，媒介的传播和数量与气候有关。由于气候与发病率和媒介存在均呈正相关，构建将气象因素纳入疾病感染模型的基于气候的数学模型成为一项紧迫的挑战。
构建宿主-媒介模型以模拟传播动态。用通过优化获得的时间相关系数替代感染率参数，以近似每日登革热数据。此外，使用自回归分布滞后（ARDL）模型将优化后的感染率表示为气候变量的函数。
当已知每日更新的气候时，感染参数可以扩展，这对预测登革热发病率可能有用。即使在增加或减少的预测窗口中进行测试，这种方法也能提供适当的预测。此外，气候与登革热之间的关联表现为登革热-湿度呈倒滑形曲线，登革热-温度和登革热-降水呈倒U形曲线。感染的最佳温度范围是24.3-30.5°C。湿度和降水分别在滞后38天高于阈值70和滞后50天低于50毫米时与登革热呈正相关。
确定气候与登革热之间的关联可能有助于应对登革热的高风险，加强公共卫生系统并减轻登革热负担的增加。
