Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures

Abstract

Non-Pharmaceutical Interventions (NPIs), aimed at reducing the diffusion of the COVID-19 pandemic, have dramatically influenced our everyday behaviour. In this work, we study how individuals adapted their daily movements and person-to-person contact patterns over time in response to the NPIs. We leverage longitudinal GPS mobility data of hundreds of thousands of anonymous individuals to empirically show and quantify the dramatic disruption in people’s mobility habits and social behaviour. We find that local interventions did not just impact the number of visits to different venues but also how people experience them. Individuals spend less time in venues, preferring simpler and more predictable routines, also reducing person-to-person contacts. Moreover, we find that the individual patterns of visits are influenced by the strength of the NPIs policies, the local severity of the pandemic and a risk adaptation factor, which increases the people’s mobility regardless of the stringency of interventions. Finally, despite the gradual recovery in visit patterns, we find that individuals continue to keep person-to-person contacts low. This apparent conflict hints that the evolution of policy adherence should be carefully addressed by policymakers, epidemiologists and mobility experts.

The impact of control and mitigation strategies during the second wave of coronavirus infections in Spain and Italy

“…This result provides supporting evidence to the effectiveness of these measures in limiting the number of close contacts among people and their potential in mitigating the spread of SARS-CoV-2. Coherent results were found in previous single-country 6 or multi-countries 5 , 7 , 48 data-driven studies as well as in previous modelling studies using fine-grained mobility data 49 , 50 . We also observed that a more limited impact of the mitigation interventions was found in Spain than in Italy, possibly due to the de-centralised approach to implementing these containment strategies in each of the 17 Autonomous Regions.…”

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

“…Along with the home and work locations, the radius of gyration and the entropy are commonly used [18,19,23,25,27,53,58,59] indicators of human mobility, and were also determined for every subscriber.…”

“…Bushman et al [10], Gao et al [21], Hu et al [26] and Tokey [51] also analyzed effects of the stay-at-home distancing on the COVID-19 increase rate, in the US. Lucchini et al studied the mobility changes during the pandemic, in four US states [32].…”

“…Khataee et al compared the effect of the social distancing in several countries, using mobility data from Apple iPhones [13]. Bushman et al [14], Gao et al [15], Hu et al [16] and Tokey [17] also analyzed effects of the stay-at-home distancing on the COVID-19 increase rate in the U.S. Lucchini et al studied the mobility changes during the pandemic in four U.S. states [18].…”

Generating Synthetic Mobility Networks with Generative Adversarial Networks

“…The increasing complexity of urban environments [1,2] and the crucial role played by human displacements in the diffusion of epidemics, not least the COVID-19 pandemic [3,4,5,6,7,8], have created a great deal of interest around the study of individual and collective human mobility [9,10,11]. The prevention of detrimental collective phenomena such as traffic congestion, air pollution, segregation, and epidemics spread, which is crucial to make our cities inclusive, safe, resilient, and sustainable [12,13,14], depends on how accurately we can predict and simulate people's movements within an urban environment.…”