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

Submitted: 11 December 2014
Accepted: 11 December 2014
Published: 1 November 2013
Abstract Views: 2337
PDF: 785
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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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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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