Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach
文献类型：期刊论文
| 作者 | Hu, Bisong1,2; Ning, Pan1; Li, Yi3; Xu, Chengdong2; Christakos, George4; Wang, Jinfeng2 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2020-07-23 |
| 页码 | 24 |
| 关键词 | Bayesian maximum entropy Kalman filter geostatistics space-time analysis hand foot and mouth disease |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2020.1795177 |
| 通讯作者 | Wang, Jinfeng(wangjf@lreis.ac.cn) |
| 英文摘要 | In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1(st), 2008 to March 19(th), 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application. |
| WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; LAND-USE REGRESSION ; SPATIOTEMPORAL ANALYSIS ; PM2.5 CONCENTRATIONS ; MODEL ; CHINA ; FRAMEWORK ; EPIDEMIC ; RISK ; LAI |
| 资助项目 | National Natural Science Foundation of China[41531179] ; National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41961055] ; National Natural Science Foundation of China[41671399] ; National Key R&D Program of China[2016YFC1302504] ; Innovation Project of LREIS[O88RA200YA] ; Key Laboratory of Poyang Lake Wetland and Watershed Research (Jiangxi Normal University), Ministry of Education[PK2019001] |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000550952300001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China ; National Key R&D Program of China ; Innovation Project of LREIS ; Key Laboratory of Poyang Lake Wetland and Watershed Research (Jiangxi Normal University), Ministry of Education |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/158312]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Jinfeng |
| 作者单位 | 1.Jiangxi Normal Univ, Sch Geog & Environm, Nanchang, Jiangxi, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 3.Chinese Acad Sci, Natl Engn Res Ctr Geoinformat, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China 4.San Diego State Univ, Geog Dept, San Diego, CA 92182 USA |
| 推荐引用方式 GB/T 7714 | Hu, Bisong,Ning, Pan,Li, Yi,et al. Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2020:24. |
| APA | Hu, Bisong,Ning, Pan,Li, Yi,Xu, Chengdong,Christakos, George,&Wang, Jinfeng.(2020).Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,24. |
| MLA | Hu, Bisong,et al."Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020):24. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。