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Abstract

This study explores the dynamic relationship between COVID-19 transmission and transportation mobility, with an emphasis on understanding the time-varying bidirectional interplay across the different phases of the pandemic. To gain insight into this relationship, we analyzed county-level data on transmission and mobility patterns from the United States over a 74-week period using a comprehensive list of factors including: temporal factors, socio-demographics, health indicators, health care infrastructure attributes, and spatial factors. For our analysis, we proposed a simultaneous econometric model system that explicitly accounts for the bidirectional relationship between COVID-19 transmission and mobility patterns while also accounting for the influence of common unobserved factors on the two variables. The model results strongly support our hypothesis that COVID-19 transmission and mobility patterns are interconnected. Further, our findings show distinct phases of the bidirectional relationship influenced by behavior changes, vaccine availability, and the emergence of new variants. Additionally, we conducted a validation exercise on a hold-out sample to assess the robustness of our model. The results confirm the superiority of the simultaneous model system with enhanced interpretability and prediction capability. By analyzing data from several weeks for the COVID-19 pandemic, our study provides valuable insights into the evolving dynamics and potential strategies for future pandemics.

Keywords

data and data science statistical methods analysis modeling

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Exploring the Relationship Between COVID-19 Transmission and Population Mobility over Time

Abstract

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