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Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China
Song, Chao1,2,3; Shi, Xun2; Bo, Yanchen4; Wang, Jinfeng3,5; Wang, Yong3; Huang, Dacang3
2019-01-15
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume648Pages:550-560
Corresponding AuthorShi, Xun(xun.shi@dartmouth.edu)
AbstractBackground: Pediatric hand, foot, and mouth disease (HFMD) has generally been found to be associated with climate. However, knowledge about how this association varies spatiotemporally is very limited, especially when considering the influence of local socioeconomic conditions. This study aims to identify multi-sourced HFMD environmental factors and further quantify the spatiotemporal nonstationary effects of various climate factors on HFMD occurrence. Methods: We propose an innovative method, named spatiotemporally varying coefficients (STVC) model, under the Bayesian hierarchical modeling framework, for exploring both spatial and temporal nonstationary effects in climate covariates, after controlling for socioeconomic effects. We use data of monthly county-level HFMD occurrence and data of related climate and socioeconomic variables in Sichuan. China from 2009 to 2011 for our experiments. Results: Cross-validation experiments showed that the STVC model achieved the best average prediction accuracy (81.98%), compared with ordinary (6827%), temporal (72.34%), spatial (75.99%) and spatiotemporal (77.60%) ecological models. The STVC model also outperformed these models in the Bayesian model evaluation. In this study, the STVC model was able to spatialize the risk indicator odds ratio (OR) into local ORs to represent spatial and temporal varying disease-climate relationships. We detected local temporal nonlinear seasonal trends and spatial hot spots for both disease occurrence and disease-climate associations over 36 months in Sichuan. China. Among the six representative climate variables, temperature (OR = 259), relative humidity (OR = 1.35), and wind speed (OR = 0.65) were not only overall related to the increase of HFMD occurrence, but also demonstrated spatiotemporal variations in their local associations with HFMD. Conclusion: Our findings show that county-level HFMD interventions may need to consider varying local-scale spatial and temporal disease-climate relationships. Our proposed Bayesian STVC model can capture spatiotemporal nonstationary exposure-response relationships for detailed exposure assessments and advanced risk mapping, and offers new insights to broader environmental science and spatial statistics. (C) 2018 Elsevier B.V. All rights reserved.
KeywordHFMD epidemics Disease-climate associations Bayesian STVC model Spatiotemporal nonstationarity OR spatiatization and mapping Local regression
DOI10.1016/j.scitotenv.2018.08.114
WOS KeywordMETEOROLOGICAL FACTORS ; AMBIENT-TEMPERATURE ; MAINLAND CHINA ; PROVINCE ; REGRESSION ; MORTALITY ; HFMD ; EPIDEMIOLOGY ; GUANGDONG ; INFERENCE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41701448] ; Southwest Petroleum University[201699010064] ; State Key Laboratory of Resources and Environmental Information System ; State Key Laboratory of Remote Sensing Science
Funding OrganizationNational Natural Science Foundation of China ; Southwest Petroleum University ; State Key Laboratory of Resources and Environmental Information System ; State Key Laboratory of Remote Sensing Science
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000447805500052
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52682
Collection中国科学院地理科学与资源研究所
Corresponding AuthorShi, Xun
Affiliation1.Southwest Petr Univ, Sch Geosci & Technol, Chengdu 610500, Sichuan, Peoples R China
2.Dartmouth Coll, Dept Geog, Hanover, NH 03755 USA
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Song, Chao,Shi, Xun,Bo, Yanchen,et al. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2019,648:550-560.
APA Song, Chao,Shi, Xun,Bo, Yanchen,Wang, Jinfeng,Wang, Yong,&Huang, Dacang.(2019).Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China.SCIENCE OF THE TOTAL ENVIRONMENT,648,550-560.
MLA Song, Chao,et al."Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China".SCIENCE OF THE TOTAL ENVIRONMENT 648(2019):550-560.
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