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This paper presents a review of social distancing measures deployed by transit agencies in the United States and Canada during the COVID-19 pandemic and discusses how specific operators across the two countries have implemented changes. Challenges and impacts on their operations are also provided. Social distancing is one of the community mitigation measures traditionally implemented during influenza pandemics and the novel coronavirus pandemic. Research has shown that social distancing is effective in containing the spread of disease. This is applicable to the current situation with the novel coronavirus, given the lack of effective vaccines and treatments in the United States and Canada in the first eight months of the pandemic. Moreover, social distancing is particularly useful in settings where community transmission is substantial. Directives for social distancing were issued in several states and public transit operators were charged with how to provide for physical distance of six feet between passengers on their property including physical infrastructure such as station buildings and rolling infrastructure (rolling stock) including trains, subway cars and buses. Operational changes were also required due to physical distancing, e.g. adding train cars to provide for opportunities to physically distance on the train. Examples of some measures discussed in this research includes taping off every other seat on buses, increasing the total length of trains by adding cars, separating bus drivers from passengers with plastic sheeting, rear door boarding, etc. This research also analyzes long-term impacts for transit operators and challenges to encourage passengers to return to public transit after lockdown requirements ordered by government officials are lifted. A section on the policies that are being explored by government to continue to sustain public transportation is also included. • We review the interventions deployed by public transit operators to comply with social distancing measures. • We explore operational changes recommended to facilitate physical distancing. • We analyze the impacts for transit operators and challenges for passengers to return to public transit post pandemic. • We discuss the challenges for transit to return to pre-pandemic conditions and some solutions explored. [ABSTRACT FROM AUTHOR]

• The paper predicts the COVID-19 epidemic in the US with different vaccine effectiveness and coverage. • The required vaccine effectiveness and coverage to suppress the epidemic are calculated. • The degree to relax social distancing and face mask use depends on the vaccine effectiveness and coverage. Multiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. We developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. Without a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8–4 million infections and 15,000–240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55–94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32–57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48–78% or a strong vaccine (100% effective) with coverage of 33–58% would be required to suppress the epidemic. Delaying vaccination rollout for 1–2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained. The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic. [ABSTRACT FROM AUTHOR]

Understanding the effects of COVID-19 mitigation for persons in group living environments is of critical importance to limiting the spread of the virus. In the U.S., residential recovery homes for persons with alcohol and drug disorders are good examples of high-risk environments where virus mitigation procedures are essential. The National Alliance for Recovery Residences (NARR) has taken recommendations developed by the Center for Disease Control (CDC) and applied them to recovery home settings. This paper describes how COVID-19 mitigation efforts in recovery homes may be influenced by two factors. First, while some houses are licensed by states with rigorous health and safety standards, others are not licensed and are subject to less oversight. These homes may be more inconsistent in adhering to mitigation standards. Second, to varying degrees, recovery homes use a social model approach to recovery that contrasts with mitigation procedures such as social distancing and stay-at-home orders. This paper provides examples of ways recovery homes have been forced to adjust to the competing demands of mitigation efforts and social model recovery. The paper also identifies multiple questions that could be addressed by provider-researcher coalitions to inform how social model recovery can navigate forward during the era of COVID-19. As we move forward during the era of COVID-19, providers are encouraged to remember that recovery homes have a history of resilience facing adversity and in fact have their origins in grassroots responses to the challenges of their times. [ABSTRACT FROM AUTHOR]

• A dynamic model structure was designed to delineate spatiotemporal evolution of health disparities. • The paper analyzed racial disparities in human mobility and COVID-19 health outcomes jointly. • Confounding effects from built environment, socioeconomics, demographics, and partisanship on racial disparities have been quantified. • The study found a persistent and disproportionate burden of COVID-19 suffered by racial minorities. Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19. [ABSTRACT FROM AUTHOR]

Much attention on the spread and impact of the ongoing pandemic has focused on institutional factors such as government capacity along with population-level characteristics such as race, income, and age. This paper draws on a growing body of evidence that bonding, bridging, and linking social capital - the horizontal and vertical ties that bind societies together - impact public health to explain why some U.S. counties have seen higher (or lower) excess deaths during the COVID19 pandemic than others. Drawing on county-level reports from the Centers for Disease Control and Prevention (CDC) since February 2020, we calculated the number of excess deaths per county compared to 2018. Starting with a panel dataset of county observations over time, we used coarsened exact matching to create smaller but more similar sets of communities that differ primarily in social capital. Controlling for several factors, including politics and governance, health care quality, and demographic characteristics, we find that bonding and linking social capital reduce the toll of COVID-19 on communities. Public health officials and community organizations should prioritize building and maintaining strong social ties and trust in government to help combat the pandemic. • This study examines excess death rates during the COVID19 pandemic among US counties. • We use a panel dataset of counties over 6 time-steps from February to September 2020. • We identified more comparable samples using coarsened exact matching, different only by social capital. • Bonding and linking social capital were associated with fewer excess deaths in US counties. • Bonding social capital was effective in spring, and linking social capital in summer. [ABSTRACT FROM AUTHOR]

• A big-data-driven analytical framework assessing human mobility during COVID-19. • Location-based evidence from large-scale and privacy-protected samples in the US. • County-level human mobility metrics are made publicly available via an open platform. • Comprehensive assessment of human mobility using generalized additive mixed models. • The policy effect of "Stay-at-home" order on restricting travel is found limited. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus. [ABSTRACT FROM AUTHOR]

2020 in the United States was marked by two converging crises—the COVID-19 pandemic and the large-scale uprisings in support of Black lives. These crises were met with both a counterproductive and inadequate response from the federal government. We examine these converging crises at the individual, social, and political scales. The biological realities of COVID-19 impact different populations in widely varied ways—the poor, the elderly, Black, Indigenous, and people of color, and those living with comorbidities get sick and die at the highest rates. Social distancing guidelines shifted millions of people to work-from-home and millions more lost their jobs, even as care laborers, preponderantly women, Black, Indigenous, and people of color, were asked to put their and their loved ones' lives on the line for the continuation of all of our lives. These biological, social, and economic crises have been punctuated by civil unrest, as millions took to the streets for racial justice, noting the unequal impacts of the pandemic. These converging crises have laid bare decades of neoliberal and neoconservative policies and ideologies, undergirded as they have been by racial capitalism, for their fundamental uncaringness. In this paper, we argue that this pandemic not only made a wider population more acutely aware of the necessity and importance of the need to care and for caring labors, but also that we stand at the precipice of potentiality--of producing a more caring society. To frame our argument, we draw on Nancy Scheper-Hughes and Margaret Lock's (1987) framework of three bodies—individual, social, and political—to unpack the multi-scalar entanglements in the differential impacts of COVID-19, questions of care, and their articulation in the current political-economic context. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, a growing number of states, counties and cities in the United States issued mandatory stay-at-home orders as part of their efforts to slow down the spread of the virus. We argue that the consequences of this one-size-fits-all order will be differentially distributed among economic groups. In this paper, we examine social distance behavior changes for lower income populations. We conduct a comparative analysis of responses between lower-income and upper-income groups and assess their relative exposure to COVID-19 risks. Using a difference-in-difference-in-differences analysis of 3140 counties, we find social distance policy effect on the lower-income group is smaller than that of the upper-income group, by as much as 46% to 54%. Our explorations of the mechanisms behind the disparate effects suggest that for the work-related trips the stay-at-home orders do not significantly reduce low income work trips and this result is statistically significant. That is, the share of essential business defined by stay-at-home orders is significantly negatively correlated with income at county level. In the non-work-related trips, we find that both the lower-income and upper-income groups reduced visits to retail, recreation, grocery, and pharmacy visits after the stay-at-home order, with the upper-income group reducing trips more compared to lower-income group. [ABSTRACT FROM AUTHOR]