Early Evidence on Social Distancing in Response to COVID-19 in the United States

Since COVID-19 transmission accelerated in the United States in March 2020, guidelines have recommended that individuals wear masks and limit close contact by remaining at least six feet away from others, even while outdoors. Such behavior is important to help slow the spread of the global pandemic; however, it may require pedestrians to make critical decisions about entering a roadway in order to avoid others, potentially creating hazardous situations for both themselves and for drivers. In this survey study, we found that while overall patterns of self-reported pedestrian activity remained largely consistent over time, participants indicated increased willingness to enter active roadways when encountering unmasked pedestrians since the COVID-19 pandemic began. Participants also rated the risks of encountering unmasked pedestrians as greater than those associated with entering a street, though the perceived risk of passing an unmasked pedestrian on the sidewalk decreased over time.

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Correcting misperceptions of exponential coronavirus growth increases support for social distancing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2006048117 ◽

2020 ◽

Vol 117 (28) ◽

pp. 16264-16266 ◽

Cited By ~ 12

Author(s):

Joris Lammers ◽

Jan Crusius ◽

Anne Gast

Keyword(s):

United States ◽

Exponential Growth ◽

The United States ◽

Statistical Literacy ◽

Social Distancing

The most effective way to stem the spread of a pandemic such as coronavirus disease 2019 (COVID-19) is social distancing, but the introduction of such measures is hampered by the fact that a sizeable part of the population fails to see their need. Three studies conducted during the mass spreading of the virus in the United States toward the end of March 2020 show that this results partially from people’s misperception of the virus’s exponential growth in linear terms and that overcoming this bias increases support for social distancing. Study 1 shows that American participants mistakenly perceive the virus’s exponential growth in linear terms (conservatives more so than liberals). Studies 2 and 3 show that instructing people to avoid the exponential growth bias significantly increases perceptions of the virus’s growth and thereby increases support for social distancing. Together, these results show the importance of statistical literacy to recruit support for fighting pandemics such as the coronavirus.

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Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States

10.1101/2020.06.30.20143636 ◽

2020 ◽

Cited By ~ 1

Author(s):

Niayesh Afshordi ◽

Benjamin Holder ◽

Mohammad Bahrami ◽

Daniel Lichtblau

Keyword(s):

United States ◽

Population Density ◽

The United States ◽

Effective Response ◽

Susceptible Population ◽

Local Climate ◽

Climate Conditions ◽

Mortality And Morbidity ◽

Social Distancing ◽

The Impact

The SARS-CoV-2 pandemic has caused significant mortality and morbidity worldwide, sparing almost no community. As the disease will likely remain a threat for years to come, an understanding of the precise influences of human demographics and settlement, as well as the dynamic factors of climate, susceptible depletion, and intervention, on the spread of localized epidemics will be vital for mounting an effective response. We consider the entire set of local epidemics in the United States; a broad selection of demographic, population density, and climate factors; and local mobility data, tracking social distancing interventions, to determine the key factors driving the spread and containment of the virus. Assuming first a linear model for the rate of exponential growth (or decay) in cases/mortality, we find that population-weighted density, humidity, and median age dominate the dynamics of growth and decline, once interventions are accounted for. A focus on distinct metropolitan areas suggests that some locales benefited from the timing of a nearly simultaneous nationwide shutdown, and/or the regional climate conditions in mid-March; while others suffered significant outbreaks prior to intervention. Using a first-principles model of the infection spread, we then develop predictions for the impact of the relaxation of social distancing and local climate conditions. A few regions, where a significant fraction of the population was infected, show evidence that the epidemic has partially resolved via depletion of the susceptible population (i.e., “herd immunity”), while most regions in the United States remain overwhelmingly susceptible. These results will be important for optimal management of intervention strategies, which can be facilitated using our online dashboard.

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Obligation or Desire: Variation in Motivation for Compliance With COVID-19 Public Health Guidance

Frontiers in Psychology ◽

10.3389/fpsyg.2021.647830 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ting Ai ◽

Glenn Adams ◽

Xian Zhao

Keyword(s):

Public Health ◽

United States ◽

The United States ◽

Psychological Experience ◽

Social Distancing ◽

Prosocial Motivation ◽

Self Study ◽

Practical Implications ◽

Health Guidance

Why do people comply with coronavirus disease 2019 (COVID-19) public health guidance? This study considers cultural-psychological foundations of variation in beliefs about motivations for such compliance. Specifically, we focused on beliefs about two sources of prosocial motivation: desire to protect others and obligation to society. Across two studies, we observed that the relative emphasis on the desire to protect others (vs. the obligation to the community) as an explanation for compliance was greater in the United States settings associated with cultural ecologies of abstracted independence than in Chinese settings associated with cultural ecologies of embedded interdependence. We observed these patterns for explanations of psychological experience of both others (Study 1) and self (Study 2), and for compliance with mandates for both social distancing and face masks (Study 2). Discussion of results considers both practical implications for motivating compliance with public health guidance and theoretical implications for denaturalizing prevailing accounts of prosocial motivation.

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Introductory Models of COVID-19 in the United States

The Biophysicist ◽

10.35459/tbp.2021.000200 ◽

2021 ◽

Vol 2 (3) ◽

pp. 74-98

Author(s):

Peter Hugo Nelson

Keyword(s):

United States ◽

The United States ◽

Transition Function ◽

Model Parameters ◽

Microsoft Excel ◽

New Model ◽

Social Distancing ◽

Unlimited Growth ◽

Least Squares Techniques

ABSTRACT Students develop and test simple kinetic models of the spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Microsoft Excel is used as the modeling platform because it is nonthreatening to students and it is widely available. Students develop finite difference models and implement them in the cells of preformatted spreadsheets following a guided inquiry pedagogy that introduces new model parameters in a scaffolded step-by-step manner. That approach allows students to investigate the implications of new model parameters in a systematic way. Students fit the resulting models to reported cases per day data for the United States using least squares techniques with Excel's Solver. Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the susceptible-infected-recovered (SIR) model. They also discover that the effects of social distancing can be modeled using a Gaussian transition function for the infection rate coefficient and that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing. Students then model the effect of vaccinations and validate the resulting susceptible-infected-recovered-vaccinated (SIRV) model by showing that it successfully predicts the reported cases per day data from Thanksgiving through the holiday period up to 14 February 2021. The same SIRV model is then extended and successfully fits the fourth peak up to 1 June 2021, caused by further relaxation of social distancing measures. Finally, students extend the model up to the present day (27 August 2021) and successfully account for the appearance of the delta variant of the SARS-CoV-2 virus. The fitted model also predicts that the delta variant peak will be comparatively short, and the cases per day data should begin to fall off in early September 2021, counter to current expectations. This case study makes an excellent capstone experience for students interested in scientific modeling.

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Information Technology and Administrative Reform

E-Government Research ◽

10.4018/978-1-59904-913-7.ch001 ◽

2008 ◽

pp. 1-20 ◽

Cited By ~ 4

Author(s):

Kenneth Kraemer ◽

John Leslie King

Keyword(s):

United States ◽

Information Technology ◽

Service Delivery ◽

Information Technologies ◽

The United States ◽

Administrative Reform ◽

The Internet ◽

Early Evidence ◽

Mainframe Computer

This article examines the theoretical ideal of information technology as an instrument of administrative reform and examines the extent to which that ideal has been achieved in the United States. It takes a look at the findings from research about the use and impacts of information technology from the time of the mainframe computer through the PC revolution to the current era of the Internet and e-government. It then concludes that information technology has never been an instrument of administrative reform; rather, it has been used to reinforce existing administrative and political arrangements. It assesses why this is the case and draws conclusions about what should be expected with future applications of information technologies—in the time after e-government. It concludes with a discussion of the early evidence about newer applications for automated service delivery, 24/7 e-government, and e-democracy.

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Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States

Health & Place ◽

10.1016/j.healthplace.2020.102378 ◽

2020 ◽

Vol 64 ◽

pp. 102378 ◽

Cited By ~ 5

Author(s):

Shima Hamidi ◽

Reid Ewing ◽

Sadegh Sabouri

Keyword(s):

United States ◽

The United States ◽

Mortality Rates ◽

Morbidity And Mortality ◽

Longitudinal Analyses ◽

Early Evidence ◽

The Relationship ◽

Metropolitan Counties

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Using implementation science to mitigate worsening health inequities in the United States during the COVID-19 pandemic

International Journal for Equity in Health ◽

10.1186/s12939-020-01293-2 ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Tyler A. Jacobson ◽

Lauren E. Smith ◽

Lisa R. Hirschhorn ◽

Mark D. Huffman

Keyword(s):

United States ◽

Community Engagement ◽

Implementation Research ◽

Policy Design ◽

Contextual Factors ◽

Health Inequities ◽

The United States ◽

Audit And Feedback ◽

Evidence Based ◽

Social Distancing

Abstract With the threat of coronavirus disease 2019 (Covid-19) enduring in the United States, effectively and equitably implementing testing, tracing, and self-isolation as key prevention and detection strategies remain critical to safely re-opening communities. As testing and tracing capacities increase, frameworks are needed to inform design and delivery to ensure their effective implementation and equitable distribution, and to strengthen community engagement in slowing and eventually stopping Covid-19 transmission. In this commentary, we highlight opportunities for integrating implementation research into planned and employed strategies in the United States to accelerate reach and effectiveness of interventions to more safely relax social distancing policies and open economies, schools, and other institutions. Implementation strategies, such as adapting evidence-based interventions based on contextual factors, promoting community engagement, and providing data audit and feedback on implementation outcomes, can support the translation of policies on testing, tracing, social distancing, and public mask use into reality. These data can demonstrate how interventions are put into practice and where adaptation in policy or practice is needed to respond to the needs of specific communities and socially vulnerable populations. Incorporating implementation research into Covid-19 policy design and translation into practice is urgently needed to mitigate the worsening health inequities in the pandemic toll and response. Applying rigorous implementation research frameworks and evaluation systems to the implementation of evidence-based interventions which are adapted to contextual factors can promote effective and equitable pandemic response and accelerate learning both among local stakeholders as well as between states to further inform their varied experiences and responses to the pandemic.

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