目的 登革热是一个全球性公共卫生问题,从地理学时空数据分析的视角,探究登革热的时空特质、构建登革热时空过程模型,是有效预防、控制登革热的新方法、研究新热点。方法 基于时空数据挖掘、时空过程建模,综合环境、气象、地理、人口4大因素,分析登革热的空间相关性及登革热病例的空间自相关,挖掘登革热影响因子;针对BP(back propagation)神经网络模型易陷入局部最优的缺陷,引入遗传算法(GA)改进BP神经网络模型,用于登革热时空模拟。结果 登革热的时空扩散与温度、湿度、居民地、交通、人口密度呈显著相关;登革热病例之间呈显著自相关;登革热发生、扩散与环境、气象、地理、人口中的多种因子存在非线性关系;利用改进的GA-BP神经网络模型模拟登革热时空扩散,均方根误差达到0.081。结论 登革热发生、扩散是由多种因素综合影响的结果;GA-BP神经网络模型能够有效模拟登革热时空过程;此模型同样适用于其他伊蚊类传染病的模拟。

Objective Dengue fever is a global health problem. Probing its spatio-temporal characteristics and building corresponding spatio-temporal models from geographical analyses may constitute a novel approach to its effective prevention and control. Method Spatio-temporal data mining and spatio-temporal process model-building were used to examine spatial relationships between dengue fever and environmental, climate, geographical, and population factors, as well as spatial autocorrelation of dengue fever cases, aiming to identify influencing factors of dengue fever. A Genetic Algorithm (GA) was introduced to overcome the weakness of Back Propagation (BP) neural network models, which is typically subject to local optima. The improved model was applied to spatio-temporal simulation of dengue fever. Result the spatio-temporal diffusion of dengue fever was significantly associated with temperature, humidity, residential areas, traffic, and population density. A remarkable spatial autocorrelation was found amongst the dengue fever cases. Nonlinear relationships were revealed between the occurrence and diffusion of dengue fever and environmental, climate, geographical, and population factors. The improved GA-BP neural network model may enhance the accuracy of simulation of spatio-temporal diffusion of dengue fever (RMSE=0.081). Conclusion The occurrence and diffusion of dengue fever were affected by multiple factors. The GA-BP neural network model can effectively simulate spatio-temporal processes of dengue fever. The improved model could likewise apply to simulations of other aedes-related diseases.