关键词: 登革热, 空间相关, 影响因素, BP神经网络模型

Abstract: Objective The prediction model of dengue fever based on back propagation (BP) neural network was constructed and verified, which provided a reference for the prevention and control of dengue. Methods Based on the temporal and spatial data of dengue fever epidemics and geographical environment, the spatio-temporal distribution characteristics of dengue fever and the spatial autocorrelation of dengue fever cases were analyzed. Pearson's method was used to analyze the correlation between dengue fever and various influencing factors in Guangzhou and Foshan areas. Then, Matlab 7.0 software was used to complete BP neural network prediction model construction, training and simulation. Results From August to October 2014, the highest incidence of dengue cases in Guangzhou and Foshan area was 90, 386, 456 cases/km², respectively, and the spatial distribution of the epidemics mainly concentrated in Guangzhou (P=0.001, Z=134.402 5). The global Moran's I index was 0.606 5. In the same month of dengue fever, the local epidemic situation of dengue in Guangzhou and Foshan district was significantly different. The outbreaks of the local cases were correlated to the epidemics of the previous month (July, August and September) (local cases and imported cases), meteorological (temperature, humidity and precipitation), and social (population density, urban and rural residential land, forest, farmland)factors. The correlation coefficient between the predicted value and the true value was 0.773 and the root mean square error was 7.522 0. Conclusion Dengue epidemics in Guangzhou and Foshan areas was not randomly distributed but obviously spatially clustered. The occurrence of dengue fever is influenced by many factors and the BP neural network model can effectively predict the temporal and spatial distribution of dengue fever in Guangzhou and Foshan areas.

Key words: Dengue fever, Spatial correlation, Influencing factors, Back propagation neural network model