Why the study?
HFMD incidence is affected by natural environmental and socioeconomic factors with strong seasonal and spatial heterogeneity, requiring quantification of spatial relationships across seasons and provinces.
A geographically weighted regression model provides improved estimation of the seasonal and spatial effects of environmental and socioeconomic factors on HFMD incidence in China compared to ordinary linear regression.
GWR modeling captures spatially varying HFMD risks in China; leaves open whether identified factors should guide interventions without validation.
Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, and per capita GDP) on the Chinese mainland, we established a geographically weighted regression (GWR) model to analyze their impacts in different seasons and provinces. The GWR model successfully describes the spatial changes of the influence of potential risks, and shows greatly improved estimation performance compared with the ordinary linear regression (OLR) method. Our findings help to understand the seasonally and spatially relevant effects of natural environmental and socioeconomic factors on the I-HFMD, and can provide information to be used to develop effective prevention strategies against HFMD at different locations and in different seasons.
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Sun et al. (2021) studied this question.
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