Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data

Journal article

The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.

Authors: Hu, N; Zhang, Z; Duffield, N; Li, X; Dadashova, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature Singapore
• Journal: Computational Urban Science
• Volume: 4
• Issue: 1
• Article number: 6
• Publication date: 2024-03-01
• Acceptance date: 2024-01-22
• DOI: 10.1007/s43762-024-00117-1
• EISSN: 2730-6852
• Language: English
• Keywords: COVID-19; Geographical and temporal weighted regression; Mobility; Multi-scale geographically weighted regression