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

Mengling Qiao; Bo Huang

doi:10.1016/j.cities.2023.104360

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

Cities. 2023 Jul:138:104360. doi: 10.1016/j.cities.2023.104360. Epub 2023 May 4.

Authors

Mengling Qiao¹, Bo Huang^{1

2

3}

Affiliations

¹ Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
² Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
³ Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.

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.

Keywords: COVID-19; GTWR; Human mobility; Social vulnerability; Spatiotemporal impacts.