Human mobility trends during the early stage of the COVID-19 pandemic in the United States

Abstract

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

“…In this study, we report and analyze various lockdown-induced changes in mobility in Germany during the initial phase of the COVID-19 pandemic. We found a considerable reduction of mobility during the pandemic, similar to what was previously reported for other countries that passed and implemented comparable policies ( 8 – 11 ). The reduction in mobility can be divided into a swift decrease, early in the lockdown phase, followed by a slow recovery.…”

“…Based on various digital data sources such as mobile phone data or social media data, several studies show that mobility significantly changed during lockdowns ( 7 ). Most studies focused on general mobility trends and confirmed an overall reduction in mobility in various countries ( 8 – 12 ). Other research focused on the relation between mobility and disease transmission: For instance, it has been argued that mobility reduction is likely instrumental in reducing the effective reproduction number in many countries ( 13 – 17 ), in agreement with theoretical models and simulations, which have shown that containment can effectively slow down disease transmission ( 18 – 20 ).…”

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…The third, the country of study and the references. Category Proxy data type Country and reference Adoption of NPI Mobile phones and/or vehicles through location intelligence and measurement platforms USA [367] , [368] , [369] , [370] , [371] , [372] Twitter Twitter population [373] , [374] Wikipedia and Reddit Canada, Italy, USA, UK [375] Google USA [376] ; 130 countries [377] TV USA [378] Mobility changes Mobile phones and/or vehicles through location intelligence and measurement platforms USA [37] , [38] , [379] , [380] Facebook Italy [39] Citymapper app 22 countries [381] …”

The importance of non-pharmaceutical interventions during the COVID-19 vaccine rollout

“…The importance of socio-demographic and socio-economic characteristics in the adoption of NPIs has been also reported in other studies conducted by monitoring variations to aggregate mobility patterns before and during NPIs. Unanimously they indicate that disadvantaged groups, though less mobile before the pandemic, were not able to reduce their mobility (i.e., stay home) during the implementation of strict NPIs [30][31][32][33][34][35][36][37]. The literature aimed at estimating the epidemiological and societal impact of COVID-19 vaccines has been focused mainly on two very important points.…”