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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | http://europepmc.org/articles/PMC3605232?pdf=render |
| _version_ | 1818700676489805824 |
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| author | Jun-Fang Xu Jing Xu Shi-Zhu Li Tia-Wu Jia Xi-Bao Huang Hua-Ming Zhang Mei Chen Guo-Jing Yang Shu-Jing Gao Qing-Yun Wang Xiao-Nong Zhou |
| author_facet | Jun-Fang Xu Jing Xu Shi-Zhu Li Tia-Wu Jia Xi-Bao Huang Hua-Ming Zhang Mei Chen Guo-Jing Yang Shu-Jing Gao Qing-Yun Wang Xiao-Nong Zhou |
| author_sort | Jun-Fang Xu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T15:08:44Z |
| format | Article |
| id | doaj.art-d724ace89c8343129949c5e26a631a3a |
| institution | Directory Open Access Journal |
| issn | 1935-2727 1935-2735 |
| language | English |
| last_indexed | 2024-12-17T15:08:44Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj.art-d724ace89c8343129949c5e26a631a3a2022-12-21T21:43:44ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352013-01-0173e212310.1371/journal.pntd.0002123Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model.Jun-Fang XuJing XuShi-Zhu LiTia-Wu JiaXi-Bao HuangHua-Ming ZhangMei ChenGuo-Jing YangShu-Jing GaoQing-Yun WangXiao-Nong ZhouBACKGROUND: 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.http://europepmc.org/articles/PMC3605232?pdf=render |
| spellingShingle | Jun-Fang Xu Jing Xu Shi-Zhu Li Tia-Wu Jia Xi-Bao Huang Hua-Ming Zhang Mei Chen Guo-Jing Yang Shu-Jing Gao Qing-Yun Wang Xiao-Nong Zhou Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. PLoS Neglected Tropical Diseases |
| title | Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. |
| title_full | Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. |
| title_fullStr | Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. |
| title_full_unstemmed | Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. |
| title_short | Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. |
| title_sort | transmission risks of schistosomiasis japonica extraction from back propagation artificial neural network and logistic regression model |
| url | http://europepmc.org/articles/PMC3605232?pdf=render |
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