scholarly journals Quantifying behavior change during the first year of the COVID-19 pandemic in the United States

Author(s):  
Dennis L Chao ◽  
Victor Cho ◽  
Amanda S Izzo ◽  
Joshua L Proctor ◽  
Marita Zimmermann

Background: During the first year of the COVID-19 pandemic, the most effective way to reduce transmission and to protect oneself was to reduce contact with others. However, it is unclear how behavior changed, despite numerous surveys about peoples' attitudes and actions during the pandemic and public health efforts to influence behavior. Methods: We used two sources of data to quantify changes in behavior at the county level during the first year of the pandemic in the United States: aggregated mobile device (smartphone) location data to approximate the fraction of people staying at home each day and digital invitation data to capture the number and size of social gatherings. Results: Between mid-March to early April 2020, the number of events fell and the fraction of devices staying at home peaked, independently of when states issued emergency orders or stay-at-home recommendations. Activity began to recover in May or June, with later rebounds in counties that suffered an early spring wave of reported COVID-19 cases. Counties with high incidence in the summer had more events, higher mobility, and less stringent state-level COVID-related restrictions the month before than counties with low incidence. Counties with high incidence in early fall stayed at home less and had less stringent state-level COVID-related restrictions in October, when cases began to rise in some parts of the US. During the early months of the pandemic, the number of events was inversely correlated with the fraction of devices staying at home, but after the fall of 2020 mobility appeared to stay constant as the number of events fell. Greater changes in behavior were observed in counties where a larger fraction voted for Biden in the 2020 US Presidential election. The number of people invited per event dropped gradually throughout the first year of the pandemic. Conclusions: The mobility and events datasets uncovered different kinds of behavioral responses to the pandemic. Our results indicate that people did in fact change their behavior in ways that likely reduced COVID exposure and transmission, though the degree of change appeared to be affected by political views. Though the mobility data captured the initial massive behavior changes in the first months of the pandemic, the digital invitation data, presented here for the first time, continued to show large changes in behavior later in the first year of the pandemic.

2020 ◽  
Author(s):  
Paiheng Xu ◽  
Mark Dredze ◽  
David A Broniatowski

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.


Author(s):  
Hoang Pham

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.


2021 ◽  
Author(s):  
Brennan Klein ◽  
C. Brandon Ogbunugafor ◽  
Benjamin J. Schafer ◽  
Zarana Bhadricha ◽  
Preeti Kori ◽  
...  

During the first year of the COVID-19 pandemic, the number of incarcerated people in the United States decreased by at least 16%---the largest, fastest reduction in prison population in American history. Using an original dataset curated from public sources on prison demographics across all 50 states and the District of Columbia, we show that incarcerated white people benefited disproportionately from this decrease in the U.S. prison population, and the fraction of incarcerated Black and Latino people sharply increased. This pattern deviates from a decade-long trend before 2020 and the onset of COVID-19, during which the proportion of incarcerated Black people was declining. Using case studies of select states, we explore and quantify mechanisms that could explain these disparities: temporary court closures that led to fewer prison admissions, changes in the frequency of police interactions, and state-level prison release policies that sought to de-densify congregate settings. These findings illuminate how systemic racism pervades juridical and penal institutions and is the engine of mass incarceration in America.


2011 ◽  
Vol 113 (5) ◽  
pp. 1031-1066
Author(s):  
Dongbin Kim ◽  
John L. Rury

Background/Context American higher education witnessed rapid expansion between 1960 and 1980, as colleges and universities welcomed millions of new students. The proportion of 19- and 20-year-old students living in dormitories, rooming houses, or other group quarters fell from more than 40% to slightly less than a third. At the same time, the proportion of students in this age group living at home with one or two parents increased from about 35% to nearly 47%, becoming the largest segment of the entering collegiate population in terms of residential alternatives. While growing numbers of high school graduates each fall headed off to campus dormitories, even more enrolled in commuter institutions close to home, gaining their initial collegiate experience in circumstances that may not have differed very much from what they had experienced in secondary school. The increased numbers of commuter students, whether they attended two-year or four-year institutions, however, have received little attention from historians and other social scientists. Purpose/Objective/Research Question/Focus of Study This study focuses on students aged 19 and 20 who lived with parents and commuted from home during the years from 1960 to 1980, when commuters became the largest category of beginning college students. It also addresses the question of how this large-scale change affected the social and economic profile of commuter students in the United States. In this regard, this study can be considered an evaluation of policy decisions intended to widen access to postsecondary institutions. Did the growing number of students living at home represent a democratic impulse in higher education, a widening of access to include groups of students who had previously been excluded from college? The study approaches this question by examining changes in the characteristics and behavior of commuter students across the country. Recognizing the variation in enrollment rates and other educational indices by state or region, this study also focuses on how the individual behavior at the point of college entry is affected by these and other characteristics of the larger social setting, particularly from a historical perspective. Research Design To grasp the larger picture of historical trends in college enrollment during the period of study, particularly in the growth of commuter students, the first part of the study utilizes state-level data and identifies changes in the number of entering college students who were commuters. In the process, descriptive statistics and ordinary least squares regression are used to identify factors associated with the proportion of college students living with their parents across states. In the second stage of analysis, hierarchical generalized linear modeling, utilizing both state- and individual-level data, is used to consider different layers of contextual effects on individual decisions to enroll in college. Data Collection and Analysis At the individual level, the principal sources of information are from 1% Integrated Public Use Microdata Samples (IPUMS) for 1960 and 1980. These are individual-level census data that permit consideration of a wide range of variables, including college enrollment. State-level variables are drawn from the published decennial census volumes, from National Center for Education Statistics reports on the number of higher education institutions, and from aggregated IPUMS data. Conclusions/Recommendations This study finds that commuter students in the United States appear to have benefited from greater institutional availability, the decline of manufacturing, continued urbanization, and a general expansion of the middle class that occurred across the period in question. It was a time of growth for this sector of the collegiate population. Despite rhetoric about wider access to postsecondary education during the period, however, the nation's colleges appear to have continued to serve a relatively affluent population, even in commuter institutions. Although making postsecondary institutions accessible to commuter students may have improved access in some circumstances, for most American youth, going to college appears to have remained a solidly middle- and upper-class phenomenon.


10.2196/21499 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e21499 ◽  
Author(s):  
Paiheng Xu ◽  
Mark Dredze ◽  
David A Broniatowski

Background Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. Objective The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. Methods We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. Results We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. Conclusions We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gregory A. Wellenius ◽  
Swapnil Vispute ◽  
Valeria Espinosa ◽  
Alex Fabrikant ◽  
Thomas C. Tsai ◽  
...  

AbstractSocial distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.


2020 ◽  
Author(s):  
Kyle J. Bourassa

Objective: Social distancing has been one of the primary interventions used to slow the spread of COVID-19. State-wide stay-at-home orders received a large degree of attention as a public health intervention to increase social distancing, but relatively little peer-reviewed research has examined the extent to which stay-at-home orders affected people’s behavior. Method: This study used GPS-derived movement from 2,858 counties in the United States from March 1 to May 7, 2020 to test the degree to which changes in state-level stay-at-home orders were associated with movement outside the home. Results: From the first week of March to the first week of April, people in counties within states that enacted stay-at-home orders decreased their movement significantly more than people in counties within states that did not enact state-level stay-at-home orders. From the first week of April to the first week of May, people in counties within states that ended their stay-at-home orders increased their movement significantly more than people in counties within states whose stay-at-home orders remained in place. The magnitude of change in movement associated with state-level stay-at-home orders was many times smaller than the total change in movement across all counties over the same periods of time in both cases. Conclusions: Stay-at-home orders are likely insufficient to reduce people’s movement outside the home without additional public health actions. Existing research on behavior change would be useful to determine what additional interventions could support social distancing behaviors during the COVID-19 pandemic if becomes necessary to reduce movement in the future.


Author(s):  
Hoang Pham

AbstractCOVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University.With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way?In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study.The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020.The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020.The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.


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