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The global COVID-19 pandemic had an unprecedented impact on transportation worldwide. Significant decreases in transportation across all modes were evident and sustained as governments worldwide implemented various countrywide closures and quarantine restrictions to slow the spread of the virus. This paper quantifies and assesses daily vehicle counts by Federal Highway Administration (FHWA) vehicle classifications during the COVID-19 pandemic in New York and Florida throughout 2020. The study found that duringMarch and April of 2020, traffic among all investigated FHWA categories was significantly reduced in both Florida and New York. However, commodity carriers in both states were able to recover faster and remained more consistent than passenger vehicles. This pattern was also observed in both urban and rural communities in Florida. The findings of this work demonstrate how commodity carrier movements, assessed through FHWA vehicle category counts, were less impacted by the governmental restrictions during the pandemic than passenger transportation. While overall traffic volume dropped by more than half in most places at the height of the pandemic, larger commodity-carrying vehicles remained nearly unchanged from the prior year by June of 2020. This was likely because of the critical need to maintain trucking movements to sustain populations. Understanding how truck traffic and freight movements more broadly were impacted during the COVID-19 pandemic is critical in preserving the continuity of service and preventing supply shortages in the event of future outbreaks. [ABSTRACT FROM AUTHOR]

Worldwide, the recent SARS-CoV-2 virus disease outbreak has infected more than 691,000,000 people and killed more than 6,900,000. Surprisingly, Sub-Saharan Africa has suffered the least from the SARS-CoV-2 pandemic. Factors that are inherent to developing countries and that contrast with their counterparts in developed countries have been associated with these disease burden differences. In this paper, we developed data-driven COVID-19 mathematical models of two 'extreme': Cameroon, a developing country, and New York State (NYS) located in a developed country. We then identified critical parameters that could be used to explain the lower-than-expected COVID-19 disease burden in Cameroon versus NYS and to help mitigate future major disease outbreaks. Through the introduction of a 'disease burden' function, we found that COVID-19 could have been much more severe in Cameroon than in NYS if the vaccination rate had remained very low in Cameroon and the pandemic had not ended. [ABSTRACT FROM AUTHOR]

In Spring 2020, the New York Army and Air National Guard (NYNG) rapidly deployed to New York City (NYC) to assist in the recovery, processing, and transport of COVID-19 decedents. This study reports on a survey conducted by NYNG service members three to six months post-mission (n = 177). Data showed that there was a dose–response relationship between mission stress exposure and decremented mental health, but certain activities were associated with better mental health outcomes. The paper also reviews resources provided by behavioral health personnel to support service members during the mission and lessons learned to inform future decedent recovery missions. [ABSTRACT FROM AUTHOR]

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 presents an in-person and digital ethnography of people in New York State who use drugs and seek treatment for opioid use disorder (OUD) using phone or video connection to receive healthcare (telecare) including interviews prior to and during the COVID-19 pandemic. This article leverages a Feminist and Science and Technology Studies (STS) approach to elucidate how the framing of the opioid crisis shapes the interconnections that are discernable, providing a heuristic to understand the increased rates of deaths due to drug overdose during the pandemic. The narratives of people seeking treatment are analyzed through the theoretical lenses of Nelly Oudshoorn's concept of the technogeography of care, Nancy Campbell's concept of technologies of suspicion, and Nancy Fraser's analysis of the US juridical-administrative-therapeutic in/justice system. This paper traces and problematizes how telecare contributes to redefining the experience of familiar places, such as home, into spaces of both care and surveillance, and how the technology of telecare presents both affordances and foreclosures to accessing care as people struggle to conform with its requirements in order to receive care. Key findings are, (1) the significance of hugs and tactile connection that is sorely missed by people using telecare for group therapy, (2) the critical importance of proximity to in-person services even while using telecare, (3) the resistance strategies of telecare users to surveillance mechanisms, and (4) the continued stigmatization of drug use and treatment acts as a key barrier to people who are striving to produce the identity of a patient who is clinically stable for take-home medication. [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]

COVID‐19 has led to procedural changes in vascular access services to protect healthcare workers and patients from further spread of the virus. Operational changes made by the vascular access service at a healthcare system in New York City during the first wave of the COVID‐19 (SARS‐CoV‐2) pandemic included a team‐based approach as well as consideration for types of lines placed to address the increase in patient volume while providing safety to healthcare workers and conserving personal protective equipment. The study consists of two samples of adult inpatients admitted to Mount Sinai Hospital in New York City in need of vascular access. Chi‐square tests of independence were used to analyze trends in data. By the fourth wave, usage of shorter lifespan ultrasound‐guided peripheral intravenous lines increased significantly and the use of longer lasting intravenous catheters decreased significantly between the first and fourth waves of COVID‐19. This paper aims to show that with greater knowledge about proper personal protective equipment and mindful resource use, hospitals are able to become more comfortable and efficient while providing increasingly frequent vascular access services in the current and future pandemics. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic highlighted the deep-rooted links between poor housing and poor health, particularly for working class communities of colour, for whom the right to the city is systemically undermined by unaffordable, precarious and sub-standard housing. This paper examines these issues against the background of a grassroots movement against evictions in New York during 2021. It describes and discusses the organisation, tactics and strategies of that movement as it attempted to challenge the dominance and norms of the real estate industry. It suggests that the pandemic engendered a radical shift in the demands of tenant and housing justice organisations, which led to New York being virtually eviction free for 22 months. While this success partly arises from the particular circumstances of New York, it is argued that there are lessons to be learned for housing campaigns elsewhere. However, questions are raised about the extent to which 'professionalised activism' represents a sustainable model and the capacity for local mobiisations to influence national political forces at a time of unprecedented volatility in an age of crisis capitalism. [ABSTRACT FROM AUTHOR]

The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID‐19 signs in CT scans included ground‐glass opacity (GGO), followed by crazy‐paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has exposed the difficulty of the US public health system to respond effectively to vulnerable subpopulations, causing disproportionate rates of morbidity and mortality. New York Haredi-Orthodox Jewish communities represent a group that have been heavily impacted by Covid-19. Little research has examined their experience or perceptions toward Covid-19 and vaccines. We conducted a cross-sectional, observational study to explore the experience of Covid-19 among Haredim. Paper surveys were self-administered between December 2020 and January 2021 in Haredi neighborhood pediatricians' offices in Brooklyn, New York. Of 102 respondents, 43% reported either a positive SARS-CoV-2 viral or antibody test. Participants trusted their physicians, Orthodox medical organizations, and rabbinic leaders for medical information. Knowledge of Covid-19 transmission and risk was good (69% answered ≥ 4/6 questions correctly). Only 12% of respondents would accept a Covid-19 vaccine, 41% were undecided and 47% were strongly hesitant. Independent predictors of strong vaccine hesitancy included believing natural infection to be better than vaccination for developing immunity (adjusted odds ratio [aOR] 4.28; 95% confidence interval [CI] 1.23–14.86), agreement that prior infection provides a path toward resuming communal life (aOR 4.10; 95% CI 1.22–13.77), and pandemic-related loss of trust in physicians (aOR 5.01; 95% CI 1.05–23.96). The primary disseminators of health information for self-protective religious communities should be stakeholders who understand these groups' unique health needs. In communities with significant Covid-19 experience, vaccination messaging may need to be tailored toward protecting infection-naïve individuals and boosting natural immunity against emerging variants. [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]

This paper presents a qualitative content analysis of survey data collected from behavioral health care providers from throughout New York regarding the challenges faced as a result of COVID-19. Survey responses from 295 agency and program administrators and staff, representing 238 organizations, were analyzed. Ten themes were identified: business operations, service provision, telehealth, safety, client concerns, staff concerns, supplies, technology, illness/grief/loss, and communication. These themes represent concerns that arose from the rapid transition to widespread use of telehealth, limited technology accessibility for both staff and clients, reduced revenue and billing changes, impact of COVID-19 infection itself and subsequent deaths of clients and staff, and necessary modifications for organizational communication both internally and externally. The implications of these challenges and the need for further research to identify how to best address them are discussed. [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]

The COVID-19 pandemic has rapidly changed our lives. While the global impacts of the pandemic are shocking, the implications for energy burdens, climate policy, and energy efficiency are salient. This study examines income differences in the acceptance of and willingness to pay for home energy management systems during the COVID-19 pandemic among 632 residents in New York. Additionally, this study examines energy profiles, energy burdens, climate change issues, risk perceptions, and social-psychological factors. Compared with low-income households, our findings suggest that high-income households use more energy, have higher utility bills during quarantine mandates, perceive a higher risk of COVID-19 infection, and perceive climate change issues to be better than before. Low-income households, however, experience the highest energy burdens. Regarding HEMS acceptance, high-income households are more willing to adopt energy and well-being-promoting features of HEMS and more willing to pay a higher monthly fee for all the features than other income groups. Overall, participants were more willing to pay a higher price for the energy features than the well-being-promoting features. Low-income households indicate lower social norms, personal norms, and perceived behavioral control over adopting HEMS; they also perceive HEMS to be more difficult to use and less useful. Higher-income households express a higher trust in utilities than low-income households. Surprisingly, cost concerns, technology anxiety, and cybersecurity concerns relating to HEMS do not differ across income groups. This paper addresses the interactions among technology attributes and social-psychological and demographic factors, and provides policy implications and insights for future research. • Adoption of home energy manage systems (HEMS) is different across income-levels. • Low-income households report lower social norms, personal norms, and perceived behavioral control over adopting HEMS. • Social-psychological and economic factors influence HEMS adoption and willingness to pay. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM 10 , PM 2.5, and NO 2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM 2.5 and NO 2 are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM 2.5 to Deaths, NO 2 to Deaths, and economic growth to both PM 2.5 and NO 2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM 2.5 to Deaths, NO 2 to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM 2.5 and NO 2) in New York state. [Display omitted] • We assess the effect of economic growth, PM 2.5 and NO 2 variations on COVID-19 deaths. • We employ two experimental approaches based on Deep Machine Learning. • PM 2.5 and NO 2 are the most significant air pollutant responsible for COVID-19 deaths. • Increasing levels of economic growth spur air pollution in New York state. [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]