地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (8): 1036-1048.doi: 10.3724/SP.J.1047.2017.01036

• 地理空间分析综合应用 • 上一篇    下一篇

利用空间聚集的贝叶斯网络评估手足口病发病风险

丘文洋(), 李连发*(), 张杰昊, 王劲峰   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101;2. 中国科学院大学,北京 100049
  • 收稿日期:2017-02-01 修回日期:2017-05-06 出版日期:2017-08-20 发布日期:2017-08-20
  • 通讯作者: 李连发 E-mail:qiuwy@lreis.ac.cn;lilf@lreis.ac.cn
  • 作者简介:

    作者简介:丘文洋(1991-),男,硕士,研究方向为空间分析与空间统计。E-mail: qiuwy@lreis.ac.cn

  • 基金资助:
    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

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   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-02-01 Revised:2017-05-06 Online:2017-08-20 Published:2017-08-20
  • Contact: LI Lianfa E-mail:qiuwy@lreis.ac.cn;lilf@lreis.ac.cn

摘要:

手足口病是一种常见的传染病,以往的研究表明该疾病与气象、环境和社会经济等因素相关联,其影响关系复杂,而疾病本身体现出较强的区域聚集性,采用普通的线性风险建模方法无法捕捉影响因素的复杂性及空间聚集性。因此,本文以山东省为例,在前人研究的基础上,提出了采用贝叶斯网络综合风险建模方法研究手足口病的发病风险与气象、土地利用、社会经济及空气污染等要素间的关系,并通过引入空间扫描统计聚集结果,将空间聚集引入到贝叶斯网络模型加强其空间推理功能,减少模型的偏差,提高评估的精度。结果表明,本文建立的手足口病空间贝叶斯网络风险模型具有较高的估计效果,引入的空间聚集性较好地融入到贝叶斯概率推理模型中,合理建立预测因子同手足口病发病风险之间的关系。通过对建模结果的解译,分析了手足口病的发病风险影响因素,特别是气候、社会经济及空气污染的影响。本文的空间贝叶斯建模方法及研究结果对手足口病暴发的防控预警具有重要的意义。

关键词: 手足口病, 贝叶斯网络, 空间流行病学, 空间聚集

Abstract:

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.

Key words: Hand-foot-mouth disease, Bayesian network, spatial epidemiology, spatial cluster