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Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China
BACKGROUND: The prevalence of schistosomiasis remains a key public health issue in China. Jiangling County in Hubei Province is a typical lake and marshland endemic area. The pattern analysis of schistosomiasis prevalence in Jiangling County is of significant importance for promoting schistosomiasis...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5406921/ https://www.ncbi.nlm.nih.gov/pubmed/28446227 http://dx.doi.org/10.1186/s40249-017-0303-5 |
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author | Xia, Shang Xue, Jing-Bo Zhang, Xia Hu, He-Hua Abe, Eniola Michael Rollinson, David Bergquist, Robert Zhou, Yibiao Li, Shi-Zhu Zhou, Xiao-Nong |
author_facet | Xia, Shang Xue, Jing-Bo Zhang, Xia Hu, He-Hua Abe, Eniola Michael Rollinson, David Bergquist, Robert Zhou, Yibiao Li, Shi-Zhu Zhou, Xiao-Nong |
author_sort | Xia, Shang |
collection | PubMed |
description | BACKGROUND: The prevalence of schistosomiasis remains a key public health issue in China. Jiangling County in Hubei Province is a typical lake and marshland endemic area. The pattern analysis of schistosomiasis prevalence in Jiangling County is of significant importance for promoting schistosomiasis surveillance and control in the similar endemic areas. METHODS: The dataset was constructed based on the annual schistosomiasis surveillance as well the socio-economic data in Jiangling County covering the years from 2009 to 2013. A village clustering method modified from the K-mean algorithm was used to identify different types of endemic villages. For these identified village clusters, a matrix-based predictive model was developed by means of exploring the one-step backward temporal correlation inference algorithm aiming to estimate the predicative correlations of schistosomiasis prevalence among different years. Field sampling of faeces from domestic animals, as an indicator of potential schistosomiasis prevalence, was carried out and the results were used to validate the results of proposed models and methods. RESULTS: The prevalence of schistosomiasis in Jiangling County declined year by year. The total of 198 endemic villages in Jiangling County can be divided into four clusters with reference to the 5 years’ occurrences of schistosomiasis in human, cattle and snail populations. For each identified village cluster, a predictive matrix was generated to characterize the relationships of schistosomiasis prevalence with the historic infection level as well as their associated impact factors. Furthermore, the results of sampling faeces from the front field agreed with the results of the identified clusters of endemic villages. CONCLUSION: The results of village clusters and the predictive matrix can be regard as the basis to conduct targeted measures for schistosomiasis surveillance and control. Furthermore, the proposed models and methods can be modified to investigate the schistosomiasis prevalence in other regions as well as be used for investigating other parasitic diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0303-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5406921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54069212017-04-27 Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China Xia, Shang Xue, Jing-Bo Zhang, Xia Hu, He-Hua Abe, Eniola Michael Rollinson, David Bergquist, Robert Zhou, Yibiao Li, Shi-Zhu Zhou, Xiao-Nong Infect Dis Poverty Research Article BACKGROUND: The prevalence of schistosomiasis remains a key public health issue in China. Jiangling County in Hubei Province is a typical lake and marshland endemic area. The pattern analysis of schistosomiasis prevalence in Jiangling County is of significant importance for promoting schistosomiasis surveillance and control in the similar endemic areas. METHODS: The dataset was constructed based on the annual schistosomiasis surveillance as well the socio-economic data in Jiangling County covering the years from 2009 to 2013. A village clustering method modified from the K-mean algorithm was used to identify different types of endemic villages. For these identified village clusters, a matrix-based predictive model was developed by means of exploring the one-step backward temporal correlation inference algorithm aiming to estimate the predicative correlations of schistosomiasis prevalence among different years. Field sampling of faeces from domestic animals, as an indicator of potential schistosomiasis prevalence, was carried out and the results were used to validate the results of proposed models and methods. RESULTS: The prevalence of schistosomiasis in Jiangling County declined year by year. The total of 198 endemic villages in Jiangling County can be divided into four clusters with reference to the 5 years’ occurrences of schistosomiasis in human, cattle and snail populations. For each identified village cluster, a predictive matrix was generated to characterize the relationships of schistosomiasis prevalence with the historic infection level as well as their associated impact factors. Furthermore, the results of sampling faeces from the front field agreed with the results of the identified clusters of endemic villages. CONCLUSION: The results of village clusters and the predictive matrix can be regard as the basis to conduct targeted measures for schistosomiasis surveillance and control. Furthermore, the proposed models and methods can be modified to investigate the schistosomiasis prevalence in other regions as well as be used for investigating other parasitic diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0303-5) contains supplementary material, which is available to authorized users. BioMed Central 2017-04-26 /pmc/articles/PMC5406921/ /pubmed/28446227 http://dx.doi.org/10.1186/s40249-017-0303-5 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Xia, Shang Xue, Jing-Bo Zhang, Xia Hu, He-Hua Abe, Eniola Michael Rollinson, David Bergquist, Robert Zhou, Yibiao Li, Shi-Zhu Zhou, Xiao-Nong Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China |
title | Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China |
title_full | Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China |
title_fullStr | Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China |
title_full_unstemmed | Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China |
title_short | Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China |
title_sort | pattern analysis of schistosomiasis prevalence by exploring predictive modeling in jiangling county, hubei province, p.r. china |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5406921/ https://www.ncbi.nlm.nih.gov/pubmed/28446227 http://dx.doi.org/10.1186/s40249-017-0303-5 |
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