Analyzing the spatiotemporal effects of meteorological factors on hand, foot and mouth disease using mixed geographically and temporally weighted autoregressive models

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. The bootstrap test method was introduced to identify the spatial autocorrelation of HFMD and the meteorological factors that have a global impact on the incidence of HFMD. The results of the analysis show that the spatial autocorrelation of HFMD in children in Inner Mongolia has significant statistical significance, and it has a significant promoting effect on the increase of incidence of HFMD. The impacts of air temperature (AT), air pressure (AP), and average wind speed (AW) on the incidence of HFMD have significant spatiotemporal heterogeneity, and relative humidity (RH) has a global positive influence on HFMD incidence. Overall, the degree of influence of meteorological factors on the incidence of HFMD is in the order of AT > AP > RH > AW. The results show that the influence of spatial autocorrelation on HFMD can not be ignored when exploring the driving factors of HFMD incidence and formulating preventive measures.