title: Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model.
abstract: BACKGROUND
  The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors.
  
  
  RESULTS
  We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected.
  
  
  CONCLUSIONS
  Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control.
authors.affiliation: National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, PR China.
authors.forename: Jun-Fang
authors.initials: JF
authors.lastname: Xu
authors.forename: Jing
authors.initials: J
authors.lastname: Xu
authors.forename: Shi-Zhu
authors.initials: SZ
authors.lastname: Li
authors.forename: Tia-Wu
authors.initials: TW
authors.lastname: Jia
authors.forename: Xi-Bao
authors.initials: XB
authors.lastname: Huang
authors.forename: Hua-Ming
authors.initials: HM
authors.lastname: Zhang
authors.forename: Mei
authors.initials: M
authors.lastname: Chen
authors.forename: Guo-Jing
authors.initials: GJ
authors.lastname: Yang
authors.forename: Shu-Jing
authors.initials: SJ
authors.lastname: Gao
authors.forename: Qing-Yun
authors.initials: QY
authors.lastname: Wang
authors.forename: Xiao-Nong
authors.initials: XN
authors.lastname: Zhou
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000818:Animals; D001519:Behavior; D002648:Child; D002681:China / Q000453:epidemiology; D004493:Education; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D018570:Risk Assessment; D012424:Rural Population; D012549:Schistosoma japonicum / Q000302:isolation & purification*; D012554:Schistosomiasis japonica / Q000453:epidemiology* / Q000635:transmission*; D012959:Socioeconomic Factors; D055815:Young Adult
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