Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai

Infectious diarrhea is an important public health problem around the world. Meteorological factors have been strongly linked to the incidence of infectious diarrhea. Therefore, accurately forecast the number of infectious diarrhea under the effect of meteorological factors is critical to control efforts. In this paper, a three layered feed-forward back-propagation artificial neural network model (FFBPNN) are developed to predict the weekly number of infectious diarrhea by using meteorological factors as input variable. The meteorological factors were chosen based on the strongly relativity with infectious diarrhea. Also, as a comparison study, the multivariate linear regression (MLR) also was applied as prediction model using the same dataset. Further, since one of the drawbacks of FFBPNN model is the interpretation of the final model in terms of the importance of variables, a sensitivity analysis is performed to determine the parametric influence on the model outputs. The simulation results obtained from the neural network confirms the feasibility of this model in terms of applicability and shows better agreement with the actual data, compared to those from the regression models. The FFBPNN model, described in this paper, is an efficient quantitative tool to evaluate and predict the infectious diarrhea using meteorological factors. Keywords-artificial neural networks; forecasting model; infectious diarrhea; multivariate linear regression; sensitivity analysis; meteorological factors