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 (r(2)) 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.