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The Omicron wave is the largest wave of COVID‐19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, a large‐scale agent‐based model of policy interventions that could have been implemented to mitigate the Omicron wave is presented. The model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. The results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action. [ABSTRACT FROM AUTHOR]

Purpose: The aims of this paper are to explore the rise of cyberhate on the Zoom video conferencing platform at the outset of the coronavirus disease 2019 (COVID-19) pandemic and to examine victimized cases of Zoombombing where it was used as a cyberhate tool. The COVID-19 pandemic has not only impacted our lives and modes of working and studying, but also created new environments for cybercriminals to engage in cybercrime, cyberhate and hacking by exploiting tools such as Zoom. This new phenomenon called "Zoombombing" was first reported in mid-March 2020, when the social distancing and stay-at-home policies in the United States were nationally introduced. Design/methodology/approach: This research conducted a news media content analysis on cases of Zoombombing. To conduct this analysis empirically, a dataset with all of the reported Zoombombing cases from March to April 2020 was created. Google Trends, news media and tweets were used to analyze Zoombombing as a form of cyberhate, particularly digital racism. Findings: The results reveal prevalent Zoom-mediated racism toward Asian Americans, African Americans and Jewish Americans. This study understands Zoombombing from a sociopolitical/cultural perspective through news reporting of victimized cases and explores various ways that Zoombombing shapes, mediates, transforms and escalates racism. Originality/value: This study is one of the very first studies to analyze Zoombombing in a way that builds upon an emerging body of literature on cyberhate. This paper considers Zoom as a space where curious young people, cybercriminals, extremists and hackers impose their ideologies and beliefs upon newly established online learning and working environments and engage in a struggle for identity recognition in the midst of increasingly accessible vulnerable software and cyberspace. [ABSTRACT FROM AUTHOR]

To date, 110,000+ people in the United States have died from the COVID-19 pandemic. In this paper, the authors will discuss COVID-19 relative to Black people and their overrepresentation among those who are infected and died from the disease. Their dying, death, and grief experiences are explored through a cultural and spiritual lens. The physical distancing, social isolation, misinformation, and restrictive burials and cremations now elicited by this unprecedented pandemic have had diminished familial, cultural, emotional, and economic impacts on the Black community. Implications for public health and Black peoples' involvement in the political process are also addressed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in profound changes in mobility patterns and altered travel behaviors locally and globally. As a result, movement metrics have widely been used by researchers and policy makers as indicators to study, model, and mitigate the impacts of the COVID-19 pandemic. However, the veracity and variability of these mobility metrics have not been studied. This paper provides a systematic review of mobility and social distancing metrics available to researchers during the pandemic in 2020 in the United States. Twenty-six indices across nine different sources are analyzed and assessed with respect to their spatial and temporal coverage as well as sample representativeness at the county-level. Finally global and local indicators of spatial association are computed to explore spatial and temporal heterogeneity in mobility patterns. The structure of underlying changes in mobility and social distancing is examined in different US counties and across different data sets. We argue that a single measure might not describe all aspects of mobility perfectly. [ABSTRACT FROM AUTHOR]

A crucial question in the governance of infectious disease outbreaks is how to ensure that people continue to adhere to mitigation measures for the longer duration. The present paper examines this question by means of a set of cross-sectional studies conducted in the United States during the COVID-19 pandemic, in May, June, and July of 2020. Using stratified samples that mimic the demographic characteristics of the U.S. population, it seeks to understand to what extent Americans continued to adhere to social distancing measures in the period after the first lockdown ended. Moreover, it seeks to uncover which variables sustained (or undermined) adherence across this period. For this purpose, we examined a broad range of factors, relating to people's (1) knowledge and understanding of the mitigation measures, (2) perceptions of their costs and benefits, (3) perceptions of legitimacy and procedural justice, (4) personal factors, (5) social environment, and (6) practical circumstances. Our findings reveal that adherence was chiefly shaped by three major factors: respondents adhered more when they (a) had greater practical capacity to adhere, (b) morally agreed more with the measures, and (c) perceived the virus as a more severe health threat. Adherence was shaped to a lesser extent by impulsivity, knowledge of social distancing measures, opportunities for violating, personal costs, and descriptive social norms. The results also reveal, however, that adherence declined across this period, which was partly explained by changes in people's moral alignment, threat perceptions, knowledge, and perceived social norms. These findings show that adherence originates from a broad range of factors that develop dynamically across time. Practically these insights help to improve pandemic governance, as well as contributing theoretically to the study of compliance and the way that rules come to shape behavior. [ABSTRACT FROM AUTHOR]

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]

As cities and states implement social distancing guidelines to mitigate the effects of COVID-19, one concern is that the social construction of race, and the privileges inherent to those constructions, influence how and when to enforce social distancing. In this theoretical paper, we discuss why Black people may be at a greater risk for police intervention when not abiding by public health guidelines. We also describe the importance of considering how Whiteness, in addition to anti-Blackness, may influence how and when public health guidelines are enforced. Finally, we consider how disparate public health policing related to COVID-19 is situated in a broader historical and global context. [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]

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States. [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]

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame. [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]