UC Berkeley

This paper develops models to quantify the dynamics of the impact of air travel on the spread of the COVID-19 pandemic, using a wide range of datasets covering the period from March to December 2020. With the help of flight operation data, we first develop a novel approach to estimate the county-level daily air passenger traffic, which combines passenger load factor estimates and information about the air traffic distribution. Cross-sectional models using aggregated county-level variables are estimated. While this study focuses on air travel variables, we also control for potential spatial autocorrelation and other relevant covariates, including vehicle miles traveled (VMT), road network connectivity, demographic characteristics, and climate. The model results indicate that air travel has a strong and positive impact on the initial pandemic growth rate for both case-based and fatality-based aggregate models.