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Little is known about how portrayals of American unity (i.e. "we're all in this together") have been received by essential workers on the front lines of the COVID risk divide, and how the pandemic may have contributed to perceptions of class inequality among lower-income workers. In this paper, we draw upon 192 in-depth interviews with precarious and gig-based workers in New York City. We find that during the height of the first wave of the pandemic, precarious workers often expressed frustration over class-based inequalities and antagonism toward elites. Many respondents expressed significant skepticism toward messages that the pandemic has brought Americans together. Instead, workers identified two distinct, class-based realities in New York: elite Americans are able to socially-distance in "mansions," or on "yachts," while precarious workers struggled to weather the storm in "dinghies." Likewise, workers felt "abandoned" by wealthy owners and managers, who fled the city to socially-distanced homes in beach communities and surrounding suburbs. Our findings suggest that low-wage, high-risk workers articulate complex conceptualizations of inequality and convey grievances toward elites during the pandemic. This study contributes to broader literature on perceived inequality, the rise of noxious work, and the social consequences of COVID-19. [ABSTRACT FROM AUTHOR]

This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID‐19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in‐sample and out‐of‐sample. [ABSTRACT FROM AUTHOR]

On March 22, 2020, the State of New York issued a "stay-at-home" policy, wherein all non-essential businesses were on pause until June 8, 2020. The bike-sharing system (BSS) and yellow taxi system (YTS) in Manhattan were substantially affected. This sudden drop in demand can impact not only short and long-term mobility but also the sustainability of transport network. Given that few empirical studies are focusing on the impacts of the "stay-at-home" policy on the BSS and YTS, this further substantiates the importance of analyzing how the policy affects the overall transportation system in New York City (NYC). This paper aims to fill this gap by quantifying the impacts of the "stay-at-home" policy on the two aforementioned transportation systems. Specifically, the following three research gaps are summarized in this study: I) The hidden biases in current "stay-at-home" policy estimation methods were not properly addressed; II) The policy impacts on BSS and YTS during different periods of the effective day were unclear; III) The sensitivity of uncontrolled confounders in long-term policy impact estimations was poorly discussed. We addressed these important research gaps by introducing robust statistical approaches like regression discontinuity design (RDD) and propensity score matching (PSM) methods, which can overcome methodological challenges such as counterfactual restoration, spatiotemporal heterogeneities, and unmeasured confounders. The BSS and YTS were studied at the aggregated neighborhood levels. Results demonstrate that the impacts on BSS have higher variations than YTS usage. The monthly average treatment effects on the treated (ATT) for BSS ranged from −72% to −28% respectively in March and June, while YTS ranged from −96% to −94%. Evidence suggests that demand for BSS surged on weekends in May and June. Understanding the impact of this short-term yet significant policy change on travel behavior will help optimize supply and demand management strategies, thereby improving the long-term sustainability should similar situations arise in the future. • Quantifying the short-term and long-term impacts of the "stay-at-home" policy. • Data-driven approaches using regression discontinuity design and propensity score matching method. • The bike-sharing system and yellow taxi system have different sensitivities to the policy. • Policy implications are derived from the robust analyses. [ABSTRACT FROM AUTHOR]

COVID‐19 has extraordinarily impacted every facet of the health care facilities' operations. Various strategies and policies were implemented promptly to preserve resources, not only to provide medical care to the expected massive numbers of COVID‐19 patients, but also to mitigate the contagion spread at the workplace to ensure safety of healthcare workers. All routine, non‐essential medical services and procedures were ramped down and workers deemed non‐essential were directed to work remotely from home to reduce the number of people at hospital premises and preserve much needed personal protective equipment that were in short supply at the outset of the pandemic. The laboratories did not remain unscathed and were under immense pressure to maintain workplace safety while being operational and provide best patient care with limited resources. In this paper, we share our experience and challenges that we faced in a cytopathology laboratory at a major academic centre in New York, USA during the peak of infection. This study reviews the impact of COVID‐19 on cytopathology specimen numbers during the peak of pandemic in New York City. Most specimens decreased in number and proportion except for effusion cytology which almost doubled. The rate of malignant and indeterminate diagnostic categories significantly increased while the benign category decreased, and the non‐diagnostic category remained the same. Adaptations to staffing and clinical operations to provide continuous patient care and trainee education while maintaining workplace safety are described. [ABSTRACT FROM AUTHOR]

• COVID-19 travel-restriction policies can affect road traffic accident patterns. • NYC data shows a decrease in number of accidents but an increase in severity. • There has been a sharp increase in severe accidents involving cyclists. • Bike sharing trips can help predict safety risks for cyclists across space and time. • Hot zones for severe cyclist accidents are spatially and temporally concentrated. Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists' safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists' severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered. [ABSTRACT FROM AUTHOR]

• COVID-19 travel-restriction policies can affect road traffic accident patterns. • NYC data shows a decrease in number of accidents but an increase in severity. • There has been a sharp increase in severe accidents involving cyclists. • Bike sharing trips can help predict safety risks for cyclists across space and time. • Hot zones for severe cyclist accidents are spatially and temporally concentrated. Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists' safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists' severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a strain of coronavirus that causes coronavirus disease 2019 (Covid-19) and has been declared a global pandemic by the World Health Organization. Total cases of SARS-CoV-2 worldwide exceed 10.2 million, with over 503,000 deaths recorded. Little is known about the body's immune response to SARS-CoV-2 infection. In this paper, we describe SARS-CoV-2 IgG antibody responses in 28,523 patients from the New York City metropolitan area and report a SARS-CoV-2 IgG positivity rate of 44%, indicating the widespread nature of the pandemic in the city and state of New York. Additionally, for a subset of patients, we report on the correlation between SARS-CoV-2 patient symptom severity and level of SARS-CoV-2 IgG antibody found in the patient sample. [ABSTRACT FROM AUTHOR]