Objective To establish BP artificial neural network predicting model regarding the daily cases of infectious diarrhea in Shanghai.Methods Data regarding both the incidence of infectious diarrhea from 2005 to 2008 in Shanghai and meteorological factors including temperature,relative humidity,rainfall,atmospheric pressure,duration of sunshine and wind speed within the same periods were collected and analyzed with the MatLab R2012b software.Meteorological factors that were correlated with infectious diarrhea were screened by Spearman correlation analysis.Principal component analysis (PCA) was used to remove the multi-colinearities between meteorological factors.Back-Propagation (BP) neural network was employed to establish related prediction models regarding the daily infectious diarrhea incidence,using artificial neural networks toolbox.The established models were evaluated through the fitting,predicting and forecasting processes.Results Data from Spearman correlation analysis indicated that the incidence of infectious diarrhea had a highly positive correlation with factors as daily maximum temperature,minimum temperature,average temperature,minimum relative humidity and average relative humidity in the previous two days (P＜0.01),and a relatively high negative correlation with the daily average air pressure in the previous two days (P＜0.01).Factors as mean absolute error,root mean square error,correlation coefficient (r),and the coefficient of determination (r2) of BP neural network model were established under the input of 4 meteorological principal components,extracted by PCA and used for training and prediction.Then appeared to be 4.7811,6.8921,0.7918,0.8418 and 5.8163,7.8062,0.7202,0.8180,respectively.The rate on mean error regarding the predictive value to actual incidence in 2008 was 5.30％ and the forecasting precision reached 95.63％.Conclusion Temperature and air pressure showed important impact on the incidence of infectious diarrhea.The BP neural network model had the advantages of low simulation forecasting errors and high forecasting hit rate that could ideally predict and forecast the effects on the incidence of infectious diarrhea.%目的 建立基于气象因素的上海市感染性腹泻逐日发病例数BP人工神经网络预测模型.方法 收集上海市2005-2008年感染性腹泻逐日发病例数与同期气象资料包括气温、相对湿度、降雨量、气压、日照时数、风速,通过Spearman相关分析选出与感染性腹泻相关的气象因素,用主成分分析(PCA)去除气象因素间的共线性影响.利用MatLab R2012b软件的神经网络工具箱建立感染性腹泻日发病例数的BP神经网络预测模型,并对拟合效果、外推预测能力和等级预报效果进行评价.结果 Spearman相关性分析显示,日感染性腹泻与前两天的日最高气温、最低气温、平均气温、最低相对湿度、平均相对湿度呈正相关(P＜0.01),与前两天的日平均气压呈负相关(P＜0.01).输入PCA提取的4个气象主成分构建BP神经网络预测模型,训练和预测样本平均绝对误差、均方根误差、相关系数、决定系数分别为4.7811、6.8921、0.7918、0.8418和5.8163、7.8062、0.7202、0.8180.模型预测值对2008年实际发病数的年平均误差率为5.30％,对感染性腹泻的等级预报正确率为95.63％.结论 温度和气压对感染性腹泻日发病例数影响较大.BP神经网络模型的拟合及预测误差较小,预报正确率较高,预报效果理想.
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