Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression

  • Zhi-Min Hong School of Sciences, Inner Mongolia University of Technology, Hohhot; Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Inner Mongolia, Hohhot, China. https://orcid.org/0000-0001-8189-527X
  • Hu-Hu Wang | zhiminhong@163.com School of Sciences, Inner Mongolia University of Technology, Hohhot; Institute for infectious disease and endemic disease control, Inner Mongolia Autonomous Region Center for Disease Control and Prevention, Hohhot, China. https://orcid.org/0000-0002-1716-674X
  • Yan-Juan Wang School of Sciences, Inner Mongolia University of Technology, Hohhot, China.
  • Wen-Rui Wang Institute for infectious disease and endemic disease control, Inner Mongolia Autonomous Region Center for Disease Control and Prevention, Hohhot, China.

Abstract

Hand, Foot, and Mouth Disease (HFMD) is a common and widespread infectious disease. Previous studies have presented evidence that climate factors, including the monthly averages of temperature, relative humidity, air pressure, wind speed and Cumulative Risk (CR) all have a strong influence on the transmission of HFMD. In this paper, the monthly time-lag geographically- weighted regression model was constructed to investigate the spatiotemporal variations of effect of climate factors on HFMD occurrence in Inner Mongolia Autonomous Region, China. From the spatial and temporal perspectives, the spatial and temporal variations of effect of climate factors on HFMD incidence are described respectively. The results indicate that the effect of climate factors on HFMD incidence shows very different spatial patterns and time trends. The findings may provide not only an indepth understanding of spatiotemporal variation patterns of the effect of climate factors on HFMD occurrence, but also provide helpful evidence for making measures of HFMD prevention and control and implementing appropriate public health interventions at the county level in different seasons.

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Published
2020-12-29
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Original Articles
Keywords:
Hand, foot, and mouth disease, geographically weighted regression, time lag geographically weighted regression, spatiotemporal non-stationarity, China.
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How to Cite
Hong, Z.-M., Wang, H.-H., Wang, Y.-J., & Wang, W.-R. (2020). Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.849