Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China

  • Chao Song
  • Yaqian He
  • Yanchen BoEmail author
  • Jinfeng Wang
  • Zhoupeng Ren
  • Jiangang Guo
  • Huibin Yang
Original Paper


Given the limitations of current approaches for disease relative risk mapping, it is necessary to develop a comprehensive mapping method not only to simultaneously downscale various epidemiologic indicators, but also to be suitable for different disease outcomes. We proposed a three-step progressive statistical method, named disease relative risk downscaling (DRRD) model, to localize different spatial epidemiologic relative risk indicators for disease mapping, and applied it to the real world hand, foot, and mouth disease (HFMD) occurrence data over Mainland China. First, to generate a spatially complete crude risk map for disease binary variable, we employed ordinary and spatial logistic regression models under Bayesian hierarchical modeling framework to estimate county-level HFMD occurrence probabilities. Cross-validation showed that spatial logistic regression (average prediction accuracy: 80.68%) outperformed ordinary logistic regression (69.75%), indicating the effectiveness of incorporating spatial autocorrelation effect in modeling. Second, for the sake of designing a suitable spatial case–control study, we took spatial stratified heterogeneity impact expressed as Chinese seven geographical divisions into consideration. Third, for generating different types of disease relative risk maps, we proposed local-scale formulas for calculating three spatial epidemiologic indicators, i.e., spatial odds ratio, spatial risk ratio, and spatial attributable risk. The immediate achievement of this study is constructing a series of national disease relative risk maps for China’s county-level HFMD interventions. The new DRRD model provides a more convenient and easily extended way for assessing local-scale relative risks in spatial and environmental epidemiology, as well as broader risk assessment sciences.


Disease mapping modeling Local relative risk assessment Spatial odds ratio Spatial risk ratio Spatial attributable risk Hand, foot, and mouth disease 



Attributable risk


Bayesian hierarchical modeling


Conditional autoregressive


Confidence interval


System for Disease Control and Prevention


China Meteorological Data Service Center


Deviance information criterion


Disease relative risk downscaling


Expectation maximum


Gross domestic product


Geographic information science


Hand, foot, and mouth disease


Integrated nested Laplace approximation


k-nearest neighbors


Logarithmic score


Odds ratio


Prediction accuracy


Progressive spatiotemporal


Random forest


Risk ratio or relative risk


Spatial attributable risk


Standard deviation


Standardized mortality ratio


Spatial odds ratio


Spatial risk ratio


Spatial stratified heterogeneity


Spatiotemporally varying coefficients


Singular value decomposition


Watanabe Akaike information criterion



The authors appreciate A-Xing Zhu (University of Wisconsin–Madison, US) and Xun Shi (Dartmouth College, US) for improving the quality of our article, Henry Chung (Michigan State University, US) for proofreading our paper carefully, María Dolores Ugarte (Public University of Navarre, Spain) for her help with coding. We would like to thank the editor and anonymous reviewers for their constructive comments and valuable suggestions on improving this manuscript. The work was jointly supported by the National Natural Science Foundation of China (No. 41701448), a grant from State Key Laboratory of Resources and Environmental Information System (No. 201811), the State Key Laboratory of Remote Sensing Science, the Young Scholars Development Fund of Southwest Petroleum University (No. 201699010064), the Technology Project of the Sichuan Bureau of Surveying, Mapping and Geoinformation (No. J2017ZC05), and the Science and Technology Strategy School Cooperation Projects of the Nanchong City Science and Technology Bureau (No. NC17SY4016, 18SXHZ0025).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and TechnologySouthwest Petroleum UniversityChengduChina
  4. 4.Department of GeographyDartmouth CollegeHanoverUSA
  5. 5.University of Chinese Academy of SciencesBeijingChina

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