Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models
文献类型：期刊论文
| 作者 | Song, Chao1,2; He, Yaqian3; Bo, Yanchen1; Wang, Jinfeng4; Ren, Zhoupeng4; Yang, Huibin1 |
| 刊名 | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
 |
| 出版日期 | 2018-07-01 |
| 卷号 | 15期号:7页码:16 |
| 关键词 | HFMD spatiotemporal zero-inflated modeling climate and socioeconomic factors spatiotemporal mapping Bayesian Hierarchical method |
| ISSN号 | 1660-4601 |
| DOI | 10.3390/ijerph15071476 |
| 通讯作者 | Bo, Yanchen(boyc@bnu.edu.cn) |
| 英文摘要 | Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China's HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD's spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health. |
| WOS关键词 | METEOROLOGICAL FACTORS ; AMBIENT-TEMPERATURE ; POISSON REGRESSION ; CROSS-VALIDATION ; RELATIVE RISK ; EPIDEMIOLOGY ; GUANGDONG ; GUANGZHOU ; INFERENCE ; MORTALITY |
| 资助项目 | National Natural Science Foundation of China[41701448] ; Southwest Petroleum University[201699010064] ; State Key Lab of Remote Sensing Science |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| 语种 | 英语 |
| WOS记录号 | WOS:000445543500183 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Southwest Petroleum University ; State Key Lab of Remote Sensing Science |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/52857]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Bo, Yanchen |
| 作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China 2.Southwest Petr Univ, Sch Geosci & Technol, Chengdu 610500, Sichuan, Peoples R China 3.West Virginia Univ, Dept Geol & Geog, Morgantown, WV 26505 USA 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Chao,He, Yaqian,Bo, Yanchen,et al. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models[J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,2018,15(7):16. |
| APA | Song, Chao,He, Yaqian,Bo, Yanchen,Wang, Jinfeng,Ren, Zhoupeng,&Yang, Huibin.(2018).Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,15(7),16. |
| MLA | Song, Chao,et al."Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 15.7(2018):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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