Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States

Ever since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including the United States. It is one of the major community mitigation strategies, also known as non-pharmaceutical interventions. However, our understanding is remaining limited in how people practice social distancing. In this study, we construct a Social Distancing Index (SDI) to evaluate people's mobility pattern changes along with the spread of COVID-19. We utilize an integrated dataset of mobile device location data for the contiguous United States plus Alaska and Hawaii over a 100-day period from January 1, 2020 to April 9, 2020. The major findings are: 1) the declaration of the national emergency concerning the COVID-19 outbreak greatly encouraged social distancing and the mandatory stay-at-home orders in most states further strengthened the practice; 2) the states with more confirmed cases have taken more active and timely responses in practicing social distancing; 3) people in the states with fewer confirmed cases did not pay much attention to maintaining social distancing and some states, e.g., Wyoming, North Dakota, and Montana, already began to practice less social distancing despite the high increasing speed of confirmed cases; 4) some counties with the highest infection rates are not performing much social distancing, e.g., Randolph County and Dougherty County in Georgia, and some counties began to practice less social distancing right after the increasing speed of confirmed cases went down, e.g., in Blaine County, Idaho, which may be dangerous as well.

[1]  Mark Dredze,et al.  The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets , 2020, ArXiv.

[2]  Michael S. Warren,et al.  Mobility Changes in Response to COVID-19 , 2020, ArXiv.

[3]  Haoyang Sun,et al.  Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study , 2020, The Lancet Infectious Diseases.

[4]  Carl A. B. Pearson,et al.  The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study , 2020, The Lancet Public Health.

[5]  D. Bryant,et al.  Environmental indicators : a systematic approach to measuring and reporting on environmental policy performance in the context of sustainable development , 1995 .

[6]  Peng Wu,et al.  Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study , 2020, The Lancet Public Health.

[7]  Brent Skorup,et al.  Aggregated Smartphone Location Data to Assist in Response to Pandemic , 2020 .

[8]  Mark Jit,et al.  Projecting social contact matrices in 152 countries using contact surveys and demographic data , 2017, PLoS Comput. Biol..

[9]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[10]  James Sears,et al.  Are We #Stayinghome to Flatten the Curve? , 2020, American Journal of Health Economics.

[11]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[12]  Marcello Ienca,et al.  On the responsible use of digital data to tackle the COVID-19 pandemic , 2020, Nature Medicine.

[13]  H. O. Wood,et al.  Modified Mercalli intensity scale of 1931 , 1931 .

[14]  Filippo Privitera,et al.  Mobility Patterns and Income Distribution in Times of Crisis: U.S. Urban Centers During the COVID-19 Pandemic , 2020 .

[15]  Yuhao Kang,et al.  Mapping county-level mobility pattern changes in the United States in response to COVID-19 , 2020, ArXiv.

[16]  Albert-László Barabási,et al.  Understanding the Spreading Patterns of Mobile Phone Viruses , 2009, Science.

[17]  M. Biggerstaff,et al.  Community Mitigation Guidelines to Prevent Pandemic Influenza — United States, 2017 , 2017, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[18]  D. Small,et al.  Protocol for an Observational Study on the Effects of Social Distancing on Influenza-Like Illness and COVID-19 , 2020, 2004.02944.

[19]  Renato Casagrandi,et al.  Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis , 2017, Scientific Reports.

[20]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[21]  A. Kabiri,et al.  Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform , 2020, medRxiv.

[22]  S I Hay,et al.  Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings , 2019, Scientific Reports.

[23]  An Pan,et al.  Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China , 2020, medRxiv.