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《Journal of Geo-Information Science》 2017-08
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A Bayesian Network Method Considering Spatial Cluster to Evaluate Health Risk of Hand, Foot and Mouth Disease

QIU Wenyang;LI Lianfa;ZHANG Jiehao;WANG Jinfeng;State Key Laboratory of Resources and Environmental Information System;University of Chinese Academy of Sciences;  
Hand, foot and mouth disease(HFMD) is a common infectious disease. Previous studies showed that multiple factors, such as meteorological, geographical, environmental and socio-economic factors were associated with HFMD. The associations between these risk factors and disease are complex. HFMD incidences present strong spatial clustering and auto-correlation. It is difficult to capture such complex non-linear associations and spatial auto-correlation using ordinary linear regression. Based on the previous studies, we proposed a Bayesian network based integrated risk approach to explore the relationship between HFMD incidence risk and the influential factors, such as meteorological parameters, land-use pattern, socio-economic status and air pollution. HFMD is a typical disease of children in Shandong Province of China and it was taken as our study case. Our approach incorporated the output of spatial clusters obtained by scanning statistics to enhance spatial reasoning of the proposed Bayesian network. This could also reduce the bias and improved the performance of the prediction. The results showed that the integrated Bayesian network model proposed achieved higher accuracy than the other methods. Also, spatial hot spots incorporated well in our model. By interpreting the marginal probability of every influential factor in the model, we analyzed the effect of these risk factors, in particular meteorological parameters, socio-economic factors and air pollution on the HFMD incidence. Our spatial Bayesian network approach is useful and the results provided important information for early-warning, prevention and control of HFMD.
【Fund】: 国家自然科学基金项目(41471376、41171344);; 上海市大气颗粒物污染防治重点实验室开放课题资助
【CateGory Index】: R181.3;R725.1
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