Spatiotemporal effects of climate factors on childhood hand, foot, and mouth disease: a case study using mixed geographically and temporally weighted regression models

ABSTRACT

Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children’s health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons.

KEYWORDS:

• Hand
• foot
• mouth disease
• geographically and temporally weighted regression
• bootstrap test
• spatiotemporal heterogeneity
• climate factor

Acknowledgments

The authors thank the anonymous reviewers for their valuable comments and suggestions, which led to significant improvement on the manuscript. Thanks for data support from the Inner Mongolia Autonomous Region Center for Disease Control and Prevention, China.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data set and the R codes for the analyses are available at https://doi.org/10.6084/m9.figshare.13634945.v1.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [No. 81860605, No. 11871056, No. 11861049 and No. 41461102] and the Natural Science Foundation of Inner Mongolia [No. 2020MS01005]

Notes on contributors

Zhimin Hong

Zhimin Hong is a professor at School of Science, Inner Mongolia University of Technology, Hohhot, PR China. She is also a key member of Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Inner Mongolia, Hohhot, PR China. Her main research interests include spatiotemporal data analysis and regression analysis.

Changlin Mei

ChangLin Mei is a professor at School of Science, Xi’an Polytechnic University, Xi’an, PR China. His main research interests include spatial data analysis and non-parametric regression.

Huhu Wang

Huhu Wang is a Master degree candidate at School of Sciences, Inner Mongolia University of Technology, Hohhot, PR China. His major is Statistics and Health Statistics.

Wala Du

Wala Du is a senior engineer at Ecological and Agricultural Meteorology Center of Inner Mongolia Autonomous Region, Hohhot, PR China. She is also a graduate supervisor at College of Geographical Science, Inner Mongolia Normal University, Hohhot, PR China. Her main research interest is spatiotemporal analysis of agricultural and grassland disasters.