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.

[1]  R. Peeling,et al.  Performance of a dipstick dye immunoassay for rapid screening of Schistosoma japonicum infection in areas of low endemicity , 2011, Parasites & Vectors.

[2]  Yuesheng Li,et al.  Familial aggregation of human infection with Schistosoma japonicum in the Poyang Lake region, China. , 2006, International journal for parasitology.

[3]  Wei Sun,et al.  A New Method to Calculate Weights of Attributes in Spectral Clustering Algorithms , 2011, 2011 International Conference of Information Technology, Computer Engineering and Management Sciences.

[4]  Alex Loukas,et al.  Current Status of Vaccines for Schistosomiasis , 2008, Clinical Microbiology Reviews.

[5]  Jürg Utzinger,et al.  The public health significance and control of schistosomiasis in China--then and now. , 2005, Acta tropica.

[6]  Rajarshi Guha,et al.  A Risk Factor Analysis of West Nile Virus: Extraction of Relationships from a Neural-Network Model , 2010, SBP.

[7]  F. M. Bock.  Considering human factors in the initial analysis and design of a medical computer system , 1982, Journal of medical systems.

[8]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[9]  P. Gong,et al.  A readapted Malone schistosome transmission index model. , 2009, Acta tropica.

[10]  Xiaonong Zhou,et al.  Prioritizing research for “One health - One world” , 2012, Infectious Diseases of Poverty.

[11]  Penelope Vounatsou,et al.  Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard , 2008, PLoS neglected tropical diseases.

[12]  I. Yu,et al.  [Study on the interaction under logistic regression modeling]. , 2008, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[13]  Stanley Lemeshow,et al.  Applied Logistic Regression, Second Edition , 1989 .

[14]  Hongbin Wang,et al.  One Prediction Model Based on BP Neural Network for Newcastle Disease , 2008, CCTA.

[15]  Peng Gong,et al.  Spatially explicit agent-based modelling for schistosomiasis transmission: human-environment interaction simulation and control strategy assessment. , 2010, Epidemics.

[16]  M. Tanner,et al.  Elimination of tropical disease through surveillance and response , 2013, Infectious Diseases of Poverty.

[17]  C. Collins,et al.  Schistosomiasis control and the health system in P.R. China , 2012, Infectious Diseases of Poverty.

[18]  R. Peeling,et al.  Evaluation of Immunoassays for the Diagnosis of Schistosoma japonicum Infection Using Archived Sera , 2011, PLoS neglected tropical diseases.

[19]  Qing-Wu Jiang,et al.  Surveillance for schistosomiasis japonica in China from 2000 to 2003. , 2005, Acta tropica.

[20]  A. Clements,et al.  Identifying high-risk areas of schistosomiasis and associated risk factors in the Poyang Lake region, China , 2010, Parasitology.

[21]  S. Liang,et al.  Toward Sustainable and Comprehensive Control of Schistosomiasis in China: Lessons from Sichuan , 2011, PLoS neglected tropical diseases.

[22]  Jacek M. Zurada,et al.  Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance , 2008, Neural Networks.

[23]  Xiaonong Zhou,et al.  Schistosomiasis in China: acute infections during 2005-2008. , 2009, Chinese medical journal.

[24]  H. Kloos,et al.  Water resources development and schistosomiasis ecology in the Awash Valley, Ethiopia. , 1985, Social science & medicine.

[25]  T A Hammad,et al.  Comparative evaluation of the use of artificial neural networks for modelling the epidemiology of schistosomiasis mansoni. , 1996, Transactions of the Royal Society of Tropical Medicine and Hygiene.

[26]  J. Remais.  Modelling Environmentally-Mediated Infectious Diseases of Humans: Transmission Dynamics of Schistosomiasis in China , 2010, Advances in experimental medicine and biology.

[27]  Steven Riley,et al.  Estimating the intensity of infection with Schistosoma japonicum in villagers of leyte, Philippines. Part I: a Bayesian cumulative logit model. The schistosomiasis transmission and ecology project (STEP). , 2005, The American journal of tropical medicine and hygiene.

[28]  C. Bruijning.  Epidemiology of schistosomiasis. , 1982, Acta Leidensia.

[29]  Xiaonong Zhou,et al.  [Schistosomiasis situation in People's Republic of China in 2011]. , 2012, Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control.

[30]  Jun-Hu Chen,et al.  Evaluation of Kato-Katz examination method in three areas with low-level endemicity of schistosomiasis japonica in China: A Bayesian modeling approach. , 2009, Acta tropica.

[31]  Zhu Rong,et al.  Schistosomiasis situation in People's Republic of China in 2009. , 2010 .

[32]  Xiao-Nong Zhou,et al.  Bayesian estimation of community prevalences of Schistosoma japonicum infection in China. , 2006, International journal for parasitology.

[33]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[34]  Bruijning Cf.  Epidemiology of schistosomiasis. , 1982 .

[35]  Gail M. Williams,et al.  Schistosomiasis in the People's Republic of China: the Era of the Three Gorges Dam , 2010, Clinical Microbiology Reviews.

[36]  Shahriar Gharibzadeh,et al.  A Novel Method for Diagnosing Cirrhosis in Patients with Chronic Hepatitis B: Artificial Neural Network Approach , 2011, Journal of Medical Systems.

[37]  Jing Xu,et al.  Epidemiology of Schistosomiasis in the People’s Republic of China, 2004 , 2007, Emerging infectious diseases.

[38]  Guo-Jing Yang,et al.  Schistosomiasis japonica control and research needs. , 2010, Advances in parasitology.

[39]  John D. Lamb,et al.  Novel Algorithms for Noisy Minimization Problems with Applications to Neural Networks Training , 2006 .

[40]  S. Liang,et al.  Individual and village‐level study of water contact patterns and Schistosoma japonicum infection in mountainous rural China , 2007, Tropical medicine & international health : TM & IH.