OBJECTIVE: To develop a spatio-temporal model of schistosomiasis japonica based on Bayesian model, and to analyze the spatio-temporal pattern of schistosomiasis, as well as to evaluate the impact of environment changes on schistosomiasis endemic. METHODS: Different Bayesian models were established by employing the data of the periodical surveillance on schistosomiasis during 1996-2005 period by taking into account of the uncertainty in sensitivity and specificity of diagnostic test, then the best fitness model was selected to analyze the spatio-temporal pattern of schistosomiasis and evaluate the impact of environment changes on schistosomiasis. RESULTS: The model with space-time interaction was a better fitting model. No significant temporal correlation was found in human infection rate of Schistosoma japonicum, and the difference of spatial structure between human infection rates of each year was significant. The prediction map of S. japonicum infection showed the changes of infection in the south areas of the Yuan River were not significant, while the prevalence increased significantly in the north areas of the river, which indicated that the impact of the implementation of project on partial abandon areas for water storing on prevalence of S. japonicum was stronger than that of the project on completed abandon areas for water storing. CONCLUSIONS: It is feasible to develop the spatio-temporal model of schistosomiasis japonica based on Bayesian model, and this inetegrated Bayesian model approach may become a powerful and statistically robust tool for estimating and evaluating the control strategy.
OBJECTIVE: To develop a spatio-temporal model of schistosomiasis japonica based on Bayesian model, and to analyze the spatio-temporal pattern of schistosomiasis, as well as to evaluate the impact of environment changes on schistosomiasis endemic. METHODS: Different Bayesian models were established by employing the data of the periodical surveillance on schistosomiasis during 1996-2005 period by taking into account of the uncertainty in sensitivity and specificity of diagnostic test, then the best fitness model was selected to analyze the spatio-temporal pattern of schistosomiasis and evaluate the impact of environment changes on schistosomiasis. RESULTS: The model with space-time interaction was a better fitting model. No significant temporal correlation was found in human infection rate of Schistosoma japonicum, and the difference of spatial structure between human infection rates of each year was significant. The prediction map of S. japonicum infection showed the changes of infection in the south areas of the Yuan River were not significant, while the prevalence increased significantly in the north areas of the river, which indicated that the impact of the implementation of project on partial abandon areas for water storing on prevalence of S. japonicum was stronger than that of the project on completed abandon areas for water storing. CONCLUSIONS: It is feasible to develop the spatio-temporal model of schistosomiasis japonica based on Bayesian model, and this inetegrated Bayesian model approach may become a powerful and statistically robust tool for estimating and evaluating the control strategy.