Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland

Jingtao Sun,Yadong Li,Renyi Liu,Cheng Yan,Feng Zhang,Zhenhong Du,Zhen Yan,Sensen Wu

doi:10.3390/ijgi10070448

Abstract

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.

Highlights

Hand, foot, and mouth disease (HFMD) is a common infectious disease that is usually found in children under 5 years old
All potential risk factors were significantly correlated with the incidence of HFMD (I-HFMD) in each season
child population density (CHD) had the strongest correlation with the I-HFMD in spring (0.421), summer (0.420), and autumn (0.416), whereas mGDP had the strongest correlation with the I-HFMD in winter

Summary

Introduction

Foot, and mouth disease (HFMD) is a common infectious disease that is usually found in children under 5 years old This disease is caused by viruses, such as Human enterovirus 71 (EV71) and Coxsackie virus A16 strain (CoxA16), and can result in symptoms in the hand, mouth, or foot, including fever, blisters, and ulcers [1]. Most studies of HFMD have used exploratory data analysis methods to analyze the spatial distribution of HFMD in the form of graphs and tables, or classical data models to predict HFMD and estimate its potential risk factors, for which the model types mainly include dynamic models [27,28,29], linear regression models [5,14], seasonal moving average models [24,30], and Bayes networks [24]

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Conclusion