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. 2018 Dec 7;8(1):17707.
doi: 10.1038/s41598-018-35721-9.

Exploration of potential risks of Hand, Foot, and Mouth Disease in Inner Mongolia Autonomous Region, China Using Geographically Weighted Regression Model

Affiliations

Exploration of potential risks of Hand, Foot, and Mouth Disease in Inner Mongolia Autonomous Region, China Using Geographically Weighted Regression Model

Zhimin Hong et al. Sci Rep. .

Abstract

To quantify the associations between the spatial characteristics of hand, foot, and mouth disease (HFMD) epidemic and meteorological factors (average temperature (AT), relative humidity (RH), average pressure (AP), average wind speed (AW) and average rainfall (AR)), child population density (CPD) and Per capita GDP (GDP) in Inner Mongolia Autonomous Region, China, and to detect the variation of influence in different seasons and counties, geographically weighted regression (GWR) model was constructed. The monthly cumulative incidence (CI) of HFMD was worked out for children ≤9 years from June to December, 2016. The results revealed that GWR model had a far superior goodness-of-fit for describing the relationship between the risk factors and HFMD incidence. Meteorological factors had different significance in their effect on HFMD incidence depending on the season. AT and AR had the greatest impact on HFMD in summer. The influence of RH on HFMD was significant in early autumn. AW was negatively correlated with HFMD in summer and positively correlated in autumn and winter. The effects of AW and AP on the incidence of HFMD were statistically significant in winter. GDP and CPD were not significantly related to HFMD occurrence for most time periods.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The monthly distribution of HFMD cases in Inner Mongolia, China, 2016.
Figure 2
Figure 2
The distribution of the logarithm of adjusted CI of HFMD (a) and the Local Moran’s I of the cases of HFMD (b) (from June to December, 2016).
Figure 3
Figure 3
Monthly distribution of HFMD cases and weather factors.
Figure 4
Figure 4
Average coefficients of risk factors.
Figure 5
Figure 5
Map of the local R square values obtained from the GWR model.
Figure 6
Figure 6
Spatially varying surface of estimated parameters for the GWR model in June, 2016.
Figure 7
Figure 7
Spatially varying surface of estimated parameters for the GWR model in September, 2016.
Figure 8
Figure 8
Spatially varying surface of estimated parameters for the GWR model in December, 2016.

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