通过搜索引擎查询数据和气象因素结合监测手足口病。
Monitoring hand, foot and mouth disease by combining search engine query data and meteorological factors.
机构信息
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China; University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
出版信息
Sci Total Environ. 2018 Jan 15;612:1293-1299. doi: 10.1016/j.scitotenv.2017.09.017. Epub 2017 Sep 8.
Hand, foot and mouth disease (HFMD) has been recognized as a significant public health threat and poses a tremendous challenge to disease control departments. To date, the relationship between meteorological factors and HFMD has been documented, and public interest of disease has been proven to be trackable from the Internet. However, no study has explored the combination of these two factors in the monitoring of HFMD. Therefore, the main aim of this study was to develop an effective monitoring model of HFMD in Guangzhou, China by utilizing historical HFMD cases, Internet-based search engine query data and meteorological factors. To this end, a case study was conducted in Guangzhou, using a network-based generalized additive model (GAM) including all factors related to HFMD. Three other models were also constructed using some of the variables for comparison. The results suggested that the model showed the best estimating ability when considering all of the related factors.
手足口病（HFMD）已被认为是重大的公共卫生威胁，给疾病控制部门带来了巨大挑战。迄今为止，气象因素与 HFMD 的关系已经得到证实，并且已经证明可以从互联网上追踪到公众对疾病的关注。然而，尚无研究探讨这两个因素在 HFMD 监测中的结合。因此，本研究的主要目的是通过利用历史 HFMD 病例、基于互联网的搜索引擎查询数据和气象因素，在中国广州开发一种有效的 HFMD 监测模型。为此，在广州进行了一项病例研究，使用了包含所有与 HFMD 相关因素的基于网络的广义加性模型（GAM）。还构建了另外三个模型，使用了一些变量进行比较。结果表明，当考虑所有相关因素时，该模型显示出最佳的估计能力。
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