title: [Study on meteorological factors-based neural network model of malaria].
abstract: OBJECTIVE
  In order to provide reliable data for strategies development on prevention, a meteorological factors-based predicating model for malaria forecast was studied.
  
  
  METHODS
  Data on malaria occurrence and climate changes from 1994 to 1999 in counties in Yunnan province was collected and analyzed with software packages of FoxPro 6.0 and Excel 5.0. The forecasting model for malaria occurrence was established, using the Neural Network Toolbox of Matlab 6.1 software package. In the studies of forecasting model, data of malaria and meteorological factors from 1994 to 1999 in Honghe state in Yunnan province was chosen. The meteorological factors included average monthly pressure, air temperature, relative humidity, monthly maximum air temperature, minimum air temperature, rainfall, rainday, evaporation and sunshine hours in the study. The established forecasting model was also tested and verified.
  
  
  RESULTS
  The BP network model was established according to data of diseases and meteorological factors from Honghe state in Yunnan province. After training the neural network for 100 times, the error of performance decreased from 3.23608 to 0.035862. Verified by fact data of malaria, the efficiency of malaria forecasting was 84.85%.
  
  
  CONCLUSIONS
  Neural network model was effective for forecasting malaria. It showed advantages as: strong ability for analysis, lower claim for data, convenient and easy to apply etc. Neural network model might be used as a new method for malaria forecasting.
authors.affiliation: Airforce Center for Diseases Control, Sanitation and Anti-epidemic Team, Logistics Department, Airforce, Beijing 100076, China.
authors.forename: Chun-yu
authors.initials: CY
authors.lastname: Gao
authors.forename: Hong-yan
authors.initials: HY
authors.lastname: Xiong
authors.forename: Dong
authors.initials: D
authors.lastname: Yi
authors.forename: Guang-jun
authors.initials: GJ
authors.lastname: Chai
authors.forename: Xiao-wei
authors.initials: XW
authors.lastname: Yang
authors.forename: Li
authors.initials: L
authors.lastname: Liu
mesh_terms: D006801:Humans; D008288:Malaria / Q000209:etiology*; D008685:Meteorological Concepts; D016571:Neural Networks, Computer