title: [Early warning on measles through the neural networks].
abstract: To discuss the effects on early warning of measles, using the neural networks. Based on the available data through monthly and weekly reports on measles from January 1986 to August 2006 in Wuhan city. The modal was developed using the neural networks to predict and analyze the prevalence and incidence of measles. When the dynamic time series modal was established with back propagation (BP) networks consisting of two layers, if p was assigned as 9, the convergence speed was acceptable and the correlation coefficient was equal to 0.85. It was more acceptable for monthly forecasting the specific value, but better for weekly forecasting the classification under probabilistic neural networks (PNN). When data was big enough to serve the purpose, it seemed more feasible for early warning using the two-layer BP networks. However, when data was not enough, then PNN could be used for the purpose of prediction. This method seemed feasible to be used in the system for early warning.
authors.affiliation: Wuhan Center for Disease Control and Prevention, Wuhan 430015, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Yu
authors.forename: Chun
authors.initials: C
authors.lastname: Ding
authors.forename: Shan-bo
authors.initials: SB
authors.lastname: Wei
authors.forename: Bang-hua
authors.initials: BH
authors.lastname: Chen
authors.forename: Pu-lin
authors.initials: PL
authors.lastname: Liu
authors.forename: Tong-yong
authors.initials: TY
authors.lastname: Luo
authors.forename: Jia-gang
authors.initials: JG
authors.lastname: Wang
authors.forename: Zhi-wei
authors.initials: ZW
authors.lastname: Pan
authors.forename: Jun-an
authors.initials: JA
authors.lastname: Lu
mesh_terms: D005544:Forecasting; D006801:Humans; D008457:Measles / Q000517:prevention & control*; D016571:Neural Networks, Computer