Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

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. Methodology/Principal Findings: 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. Conclusion/Significance: 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. Author Summary: Schistosomiasis japonica is one of the serious infectious diseases causing problems in public health in People's Republic of China (P.R. China). The prevalence of schistosomiasis japonica is easily rebounding in the lake regions due to unique environmental settings. By considering the importance in assessing the risk factors for transmission of schistosomiasis, we conducted an epidemiologic investigation of schistosomiasis in Jiangling County, a lake region of P.R. China. Results showed that the top risk factors included integration of water contact history and infection history, infection times, main lifestyle of water contact, main recreation of water contact, etc., illustrated by both back-propagation artificial neural network and multivariable logistic regression approaches. It was found that the population at age 15 or younger having one or two times of infections was the high-risk group in the study settings. We concluded that socioeconomic factors are far more important than environmental factors in the transmission of schistosomiasis at local settings or in a small scale in Jiangling County, P.R. China.