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Int. J. Environ. Res. Public Health 2018, 15(7), 1476; https://doi.org/10.3390/ijerph15071476

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
School of Geoscience and Technology, Southwest Petroleum University, Sichuan 610500, China
3
Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA
4
State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Received: 16 June 2018 / Revised: 7 July 2018 / Accepted: 10 July 2018 / Published: 12 July 2018
(This article belongs to the Special Issue Spatio-Temporal Analysis of Infectious Diseases)
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Abstract

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. View Full-Text
Keywords: HFMD; spatiotemporal zero-inflated modeling; climate and socioeconomic factors; spatiotemporal mapping; Bayesian Hierarchical method HFMD; spatiotemporal zero-inflated modeling; climate and socioeconomic factors; spatiotemporal mapping; Bayesian Hierarchical method
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Song, C.; He, Y.; Bo, Y.; Wang, J.; Ren, Z.; Yang, H. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. Int. J. Environ. Res. Public Health 2018, 15, 1476.

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