Analyzing the spatiotemporal effects of meteorological factors on hand, foot and mouth disease using mixed geographically and temporally weighted autoregressive models
摘要
Spatial autocorrelation is an important epidemiological feature of hand, foot, and mouth disease (HFMD). However, few studies have included this feature in the regression relationship between HFMD incidence and driving factors to explore its impact on incidence. In this paper, we propose a mixed geographically and temporally weighted autoregressive (MGTWAR) model to explore the impact of spatial autocorrelation and meteorological factors on the incidence of HFMD among children in Inner Mongolia, China, in 2016. In addition, we proposed a residual-based bootstrap test to identify the spatial autocorrelation in the incidence of HFMD and the spatiotemporal heterogeneity in regression relationships. The analysis results indicate that simultaneously modeling the spatiotemporal heterogeneity and spatial dependence of the incidence of HFMD can effectively improve the fitting effect of the model in terms of