COVID-19 spread prediction using socio-demographic and mobility-related data

Abstract

Studying the impacts of factors that may vary spatially and temporally as infectious disease progresses is critical for the prediction and intervention of COVID-19. This study aimed to quantitatively assess the spatiotemporal impacts of socio-demographic and mobility-related factors to predict the spread of COVID-19. We designed two different schemes that enhanced temporal and spatial features respectively, and both with the geographically and temporally weighted regression (GTWR) model adopted to consider the heterogeneity and non-stationarity problems, to reveal the spatiotemporal associations between the factors and the spread of COVID-19 pandemic. Results indicate that our two schemes are effective in facilitating the accuracy of predicting the spread of COVID-19. In particular, the temporally enhanced scheme quantifies the impacts of the factors on the temporal spreading trend of the epidemic at the city level. Simultaneously, the spatially enhanced scheme figures out how the spatial variances of the factors determine the spatial distribution of the COVID-19 cases among districts, particularly between the urban area and the surrounding suburbs. Findings provide potential policy implications in terms of dynamic and adaptive anti-epidemic.