Comparison of data-fitting models for schistosomiasis: a case study in Xingzi, China

  • Yi Hu Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China.
  • Cheng-Long Xiong Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
  • Zhi-Jie Zhang | epistat@gmail.com Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai; Biomedical Statistical Center, Fudan University, Shanghai, China.
  • Robert Bergquist Ingerod, Brastad, Sweden.
  • Zeng-Liang Wang Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China.
  • Jie Gao Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China.
  • Rui Li Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China.
  • Bo Tao Xingzi Station for Schitosomiasis Prevention and Control, Xingzi, China.
  • Qiu-Lin Jiang Xingzi Station for Schitosomiasis Prevention and Control, Xingzi, China.
  • Qingwu Jiang Department of Epidemiology, School of Public Health, Fudan University, Shanghai; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China.

Abstract

When modelling prevalence data, epidemiological studies usually employ either Gaussian, binomial or Poisson models. However, reasons are seldom given in the literature why the chosen model was felt to be the most appropriate. In this study, we compared all three models for fitting schistosomiasis risk in Xingzi county, Jiangxi province, People’s Republic of China. Parasitological data from conventional surveys were available for 36,208 individuals aged between 6 and 65 years from 42 sampled villages and used in combination with environmental data to map the spatial patterns of schistosomiasis risk. The results show that the Poisson model fitted the data best and this model identified the role of environmental risk factors in explaining the geographical variation of schistosomiasis risk. These factors were further used to develop a predictive map, which has important implications for the control and eventual elimination of schistosomiasis in the People’s Republic of China.

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Published
2013-11-01
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Original Articles
Keywords:
Schistosoma japonicum, environmental factors, geostatistics, spatial prediction, geographical information systems, People’s Republic of China.
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How to Cite
Hu, Y., Xiong, C.-L., Zhang, Z.-J., Bergquist, R., Wang, Z.-L., Gao, J., Li, R., Tao, B., Jiang, Q.-L., & Jiang, Q. (2013). Comparison of data-fitting models for schistosomiasis: a case study in Xingzi, China. Geospatial Health, 8(1), 125-132. https://doi.org/10.4081/gh.2013.60

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