Abstract
The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.
Highlights
MethodsTo quantify daily human mobility from collected Twitter data, we propose two different types of distance, respectively referred to as single-day distance (Dsd) and cross-day distance (Dcd)
The outbreak of Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus is a public health emergency that raises concerns worldwide, leading to serious health, economic, and social challenges
To answer the above questions, we focus on Twitter, a popular social media platform, and analyze over 580 million tweets from all over the world to see how the worldwide collaborative efforts in reducing mobility are reflected from this user-generated information in three different scales: global scale, country scale, and Conterminous U.S (CONUS) state scale
Summary
To quantify daily human mobility from collected Twitter data, we propose two different types of distance, respectively referred to as single-day distance (Dsd) and cross-day distance (Dcd). To reduce the computational complexity, the calculation of Dsd is adopted and modified from Warren and Skillman [41]. Dsd represents the users’ daily maximum travel distance of all locations relative to the initial location. Its calculation is confined within a single day so that users’ daily travel patterns can be revealed. Different from Dsd, Dcd measures the mean center shift between two consecutive days
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