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Global efforts are focused on discussing effective measures for minimizing the impact of COVID-19 on global community. It is clear that the ongoing pandemic of this virus caused an immense threat to public health and economic development. Mathematical models with real data simulations are powerful tools that can identify key factors of pandemic and improve control or mitigation strategies. Compared with integer-order and left-hand side fractional models, two-side fractional models can better capture the state of pandemic spreading. In this paper, two-side fractional models are first proposed to qualitative and quantitative analysis of the COVID-19 pandemic. A basic framework are given for the prediction and analysis of infectious diseases by these types of models. By means of asymptotic stability analysis of disease-free and endemic equilibrium points, basic reproduction number R 0 can be obtained, which is helpful for estimating the severity of an outbreak qualitatively. Sensitivity analysis of R 0 is performed to identify and rank key epidemiological parameters. Based on the real data of the United States, numerical tests reveal that the model with both left-hand side fractional derivative and right-hand side fractional integral terms has a better forecast ability for the epidemic trend in the next ten days. Our extensive computational results also quantitatively reveal that non-pharmaceutical interventions, such as isolation, stay at home, strict control of social distancing, and rapid testing can play an important role in preventing the pandemic of the disease. Thus, the two-side fractional models are proposed in this paper can successfully capture the change rule of COVID-19, which provide a strong tool for understanding and analyzing the trend of the outbreak. [ABSTRACT FROM AUTHOR]

Since the COVID-19 outbreak, consumer behavior has been affected by the perceived threat of the pandemic and economic uncertainty. This paper aims to explore the dynamic effects of COVID-19, consumer sentiment, economic policy uncertainty, and fuel prices on travel behavior in the United States. Using updated daily trip data, the results show that consumer sentiment has a positive long-run impact on travel demand for air and auto, suggesting that a positive change in consumer sentiment can boost demand for these modes of transportation in the long term. Additionally, consumer sentiment has a favorable effect (1.34) on demand for long-distance trips, but it has a negative impact (−0.42) on the number of people staying at home. Economic and political shocks have a detrimental impact on demand for air and auto travel, suggesting that consumers reduce the frequency and cost of these transport services if they have pessimistic expectations about the future state of the economy and policy. However, in the short term, US travelers appear to be insensitive to shocks in consumer sentiment and economic policy uncertainty. Regarding the perceived threat of the pandemic, the results indicate that rising COVID-19 cases have a negative long-term effect on demand for air travel (−0.09) and public transit (−0.19), while they are positively associated with demand for auto travel (0.06). Similarly, the increasing number of deaths due to COVID-19 has led to a shift from shared-use mass transportation (air travel and public transit) to private autos and non-motorized travel, such as walking in the short term. • We examine changes in travel behavior and mode preferences during COVID-19. • Positive sentiment shocks boost travel demand for air and auto in the long run. • Economic and political shocks have an adverse effect on demand for air and auto. • COVID-19 cases are the main long-term determinant of demand for air and transit. • COVID-19 threat results in a modal shift from shared-use transport to personal transport. [ABSTRACT FROM AUTHOR]

• Vaccine-derived herd immunity against COVID-19 can be achieved if at least 60% of the population is fully-vaccinated. • COVID-19 elimination is feasible by June 2022 if the current baseline full vaccination coverage is increased by about 20%. • Elimination prospects are improved significantly if vaccination is combined with the use of moderate to high-quality masks. • Having more people wear surgical masks is more beneficial than having fewer people wear the highly-effective N95 masks. • Treatment of symptomatic individuals has a significant impact on reducing daily hospitalizations. The effectiveness of control interventions against COVID-19 is threatened by the emergence of SARS-CoV-2 variants of concern. We present a mathematical model for studying the transmission dynamics of two of these variants (Delta and Omicron) in the United States, in the presence of vaccination, treatment of individuals with clinical symptoms of the disease and the use of face masks. The model is parameterized and cross-validated using observed daily case data for COVID-19 in the United States for the period from November 2021 (when Omicron first emerged) to March 2022. Rigorous qualitative analysis of the model shows that the disease-free equilibrium of the model is locally-asymptotically stable when the control reproduction number of the model (denoted by R c) is less than one. This equilibrium is shown to be globally-asymptotically stable for a special case of the model, where disease-induced mortality is negligible and both vaccine-derived immunity in fully-vaccinated individuals and natural immunity do not wane, when the associated reproduction number is less than one. The epidemiological implication of the latter result is that the combined vaccination-boosting strategy can lead to the elimination of the pandemic if its implementation can bring (and maintain) the associated reproduction number to a value less than one. An analytical expression for the vaccine-derived herd immunity threshold is derived. Using this expression, together with the baseline values of the parameters of the parameterized model, we showed that the vaccine-derived herd immunity can be achieved in the United States (so that the pandemic will be eliminated) if at least 68 % of the population is fully-vaccinated with two of the three vaccines approved for use in the United States (Pfizer or Moderna vaccine). Furthermore, this study showed (as of the time of writing in March 2022) that the control reproduction number of the Omicron variant was approximately 3.5 times that of the Delta variant (the reproduction of the latter is computed to be ≈ 0.2782), indicating that Delta had practically died out and that Omicron has competitively-excluded Delta (to become the predominant variant in the United States). Based on our analysis and parameterization at the time of writing of this paper (March 2022), our study suggests that SARS-CoV-2 elimination is feasible by June 2022 if the current baseline level of the coverage of fully-vaccinated individuals is increased by about 20 %. The prospect of pandemic elimination is significantly improved if vaccination is combined with a face mask strategy that prioritizes moderately effective and high-quality masks. Having a high percentage of the populace wearing the moderately-effective surgical mask is more beneficial to the community than having low percentage of the populace wearing the highly-effective N95 masks. We showed that waning natural and vaccine-derived immunity (if considered individually) offer marginal impact on disease burden, except for the case when they wane at a much faster rate (e.g., within three months), in comparison to the baseline (estimated to be within 9 months to a year). Treatment of symptomatic individuals has marginal effect in reducing daily cases of SARS-CoV-2, in comparison to the baseline, but it has significant impact in reducing daily hospitalizations. Furthermore, while treatment significantly reduces daily hospitalizations (and, consequently, deaths), the prospects of COVID-19 elimination in the United States are significantly enhanced if investments in control resources are focused on mask usage and vaccination rather than on treatment. [ABSTRACT FROM AUTHOR]

With the introduction of COVID-19 vaccines, many colleges and universities decided to mandate vaccination for all students and employees. The objective of this paper is to empirically investigate the effect of the mandate policy on Fall 2021 COVID-19 cases in institutions of higher education. We construct a unique dataset of a sample of 94 colleges and universities in the east and southeast regions of the United States, 41 of which required vaccination prior to Fall 2021. A difference-in-differences analysis is conducted, considering vaccine requirement as a policy implemented only in a sub-group of these institutions. We control for several factors, including state-level case per capita and student population. Our analysis shows that mandatory vaccination substantially decreased cases in institutions of higher education by 1,473 cases per 100,000 student population (95 CI: 132, 2813). The results suggest that a COVID-19 vaccine requirement is an effective policy in decreasing cases in such institutions, leading to a safer educational experience. [ABSTRACT FROM AUTHOR]

English-language reporting on the continuing difficulties in accessing Evusheld reflects the marginalization of immunocompromised people in discussions about the public policy response to Covid-19. Moreover, the lack of available data on global Evusheld access, particularly in low-income countries, has emerged as a key form of nonknowledge that must be redressed within public health research. Through examining how knowledge about domestic and global barriers to Evusheld access circulates, and does not circulate, within The New York Times , this paper identifies a case study of the social production of ignorance related to a key issue in the Covid-19 pandemic. Drawing on science and technology studies, the history of science and media studies, I situate these trends in the context of longer explanatory histories of nonknowledge. First, through a critical discourse analysis of the New York Times' reporting on Evusheld access in the U.S., I trace the individualizing framework evident in many articles to longstanding trends in reporting on health and illness, and to the structural marginalization of immunocompromised people in U.S. Secondly, I argue that the near-total absence of reporting on Evusheld access in low-income countries is consistent with the longstanding structural neglect of health crises in the global south. • New York Times coverage of preventative treatment Evusheld rarely discusses access barriers. • Access barriers discussed are largely logistical rather than structural. • The perspectives of immunocompromised people are rarely included in Times articles on Evusheld. • Failure to include voices from impacted communities contributes to agnogenesis. • Centering the perspectives of constituencies most impacted by a health crisis will advance health equity. [ABSTRACT FROM AUTHOR]

Life expectancy in the United States is decreasing. Health disparities are widening. Growing evidence for and integration of social and structural determinants into theory and practice has not yet improved outcomes. The COVID-19 pandemic reinforced the fact. In this paper, we argue that the biomedical model and its underlying scientific paradigm of causal determinism, which currently dominate population health, cannot meet population health needs. While criticism of the biomedical model is not new, this paper advances the field by going beyond criticism to recognize the need for a paradigm shift. In the first half of the paper, we present a critical analysis of the biomedical model and the paradigm of causal determinism. In the second half, we outline the agentic paradigm and present a structural model of health based on generalizable, group-level processes. We use the experience of the COVID-19 pandemic to illustrate the practical applications of our model. It will be important for future work to investigate the empirical and pragmatic applications of our structural model of population health. • Population health must move beyond the biomedical model. • This requires going beyond the model to critique the paradigm of causal determinism. • The agentic paradigm is an alternative to causal determinism. • The structural model is an alternative to the biomedical model. • The structural model highlights structural resilience and congruence in the population. [ABSTRACT FROM AUTHOR]

Extreme heat events are the deadliest weather-related event in the United States. Cities throughout the United States have worked to develop heat adaptation strategies to limit the impact of extreme heat on vulnerable populations. However, the COVID-19 pandemic presented unprecedented challenges to local governments. This paper provides a preliminary review of strategies and interventions used to manage compound COVID-19-extreme heat events in the 25 most populous cities of the United States. Heat adaptation strategies employed prior to the COVID-19 pandemic were not adequate to meet during the co-occurring compound hazard of COVID-19-EHE. Long-term climate-adaptation strategies will require leveraging physical, financial, and community resources across multiple city departments to meet the needs of compound hazards, such as COVID-19 and extreme heat. • Cities faced compound crises of COVID-19 and extreme heat in 2020. • 2020 heat response plans analyzed for 25 largest US cities. • Public health and financial limitations significantly reduced cooling access. • Record breaking temperatures left many vulnerable to heat illness and death. • Adaptation and mitigation strategies are needed to manage compound heat events. [ABSTRACT FROM AUTHOR]

• Close oversight by a Community Coordinator, relationships built with community partners, and the use of telehealth were creative solutions used by the Infant Toddler Court Teams during COVID-19 to support timely access to services. • This study builds on a companion paper that examined receipt and access to child health services at multiple Infant-Toddler Court Teams prior to and during the first year of the pandemic and details the challenges faced and the solutions identified by ITCT partners to support access to child services in the context of the COVID-19 pandemic within the US. • Understanding the challenges and solutions faced by the Infant-Toddler Court Team Community Coordinators during COVID-19 will be important for future pandemics or crises that impact care for child welfare involved children. This qualitative study examines Infant-Toddler Court Teams' (ITCT) responses to the impact of the COVID-19 pandemic on the accessibility and timeliness of services for children involved with an Infant Toddler Court Team (ITCT) within the United States. ITCTs utilize collaborative practice to improve, align, and integrate systems and build community capacity to improve outcomes for very young children and their families. This study reports findings from 350 transcripts of community partner interviews and an analysis of written case notes from ITCT Community Coordinators related to more than 700 instances of child service needs. We describe challenges faced and solutions and innovations developed by ITCTs to support and maintain child health services during the first year of the pandemic, as well as findings related to challenges associated with other types of services that reduced accessibility and timeliness. Understanding the challenges experienced and solutions developed can provide the child welfare field with insight and guidance on how to approach disruptions in care in the future and the persistent lack of service providers for court involved families. [ABSTRACT FROM AUTHOR]

• Integrated primary care can improve evidence-based treatment access during COVID-19. • Low intensity, high yield resources can be quickly and easily disseminated. • Cognitive behavioral therapy can be adapted and employed as a higher level of care. The coronavirus disease 2019 (COVID-19) pandemic has consistently been described as an "unprecedented" global health crisis. While the focus has been primarily on the medical and economic impact of the pandemic, psychological sequelae are anticipated. Primary care is the main point of access for mental health care in the United States, making it the ideal locale to provide psychological services for a larger proportion of the population than traditional mental health care settings. The aim of this paper is to describe how our multi-state, multi-site integrated primary care program adapted and applied cognitive behavioral therapy in the context of COVID-19. Access to mental health care was disrupted despite burgeoning mental health concerns, necessitating novel approaches to providing care. A stepped-care approach was implemented within our primary care practice, which consisted of a combination of low-intensity, high-yield stress management and resiliency building resources and cognitive behavioral therapy that were delivered flexibly based on patient preference, technological capabilities, state ordinances, insurance coverage, and institutional policies. The lessons learned from this experience can inform other integrated primary care clinics in responding to the current and future pandemics. [ABSTRACT FROM AUTHOR]

This paper investigates the historical behavior of initial unemployment claims (ICSA) in the United States (U.S.) during all the recession periods and epidemic diseases such as Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and COVID-19 since 1967 by applying statistical methods based on long range dependence and fractional differentiation. Using unit root tests (ADF, PP and KPSS) we discover that the original time series is stationary I(0) and the subsamples are non-stationary I(1). Finally, to analyze the original time series as well as the several periods corresponding to the recessions that occurred in U.S. and the three epidemic diseases, we use AIC and BIC criterion to fit the best ARFIMA model. We conclude that the results display long memory with a degree of integration strictly below 1 (d ​< ​1) for the COVID-19 episode and for the rest of the subsamples, except for the original time series and the 2nd subsample. Thus we can conclude that the impacts will be transient and with long lasting effects of shocks and expecting to disappear on their own in long term. Finally, we use a methodology proposed by Bai and Perron to estimate structural breaks not being necessary to know the time of the breaks in advance. The results are similar to those obtained previously. [ABSTRACT FROM AUTHOR]

COVID-19 has upended travel across the world, disrupting commute patterns, mode choices, and public transit systems. In the United States, changes to transit service and reductions in passenger volume due to COVID-19 are lasting longer than originally anticipated. In this paper we examine the impacts of the COVID-19 pandemic on individual travel behavior across the United States. We analyze mobility data from Janurary to December 2020 from a sample drawn from a nationwide smartphone-based panel curated by a private firm, Embee Mobile. We combine this with a survey that we administered to that sample in August 2020. Our analysis provides insight into travel patterns and the immediate impacts of the COVID-19 pandemic on transit riders. We investigate three questions. First, how do transit riders differ socio-demographically from non-riders? Second, how has the travel behavior of transit riders changed due to the pandemic in comparison to non-riders, controlling for other factors? And third, how has this travel behavior varied across different types of transit riders? The travel patterns of transit riders were more significantly disrupted by the pandemic than the travel of non-riders, as measured by the average weekly number of trips and distance traveled before and after the onset of the pandemic. This was calculated using GPS traces from panel member smartphones. Our survey of the panel revealed that of transit riders, 75% reported taking transit less since the pandemic, likely due to a combination of being affected by transit service changes, concerns about infection risk on transit, and trip reductions due to shelter-in-place rules. Less than 10 percent of transit riders in our sample reported that they were comfortable using transit despite COVID-19 infection risk, and were not affected by transit service reductions. Transit riders were also more likely to have changed their travel behavior in other ways, including reporting an increase in walking. However, lower-income transit riders were different from higher-income riders in that they had a significantly smaller reduction in the number of trips and distance traveled, suggesting that these lower-income households had less discretion over the amount of travel they carried out during the pandemic. These results have significant implications for understanding the way welfare has been affected for transportation-disadvantaged populations during the course of the pandemic, and insight into the recovery of U.S. transit systems. The evidence from this unique dataset helps us understand the future effects of the pandemic on transit riders in the United States, either in further recovery from the pandemic with the anticipated effects of mass vaccination, or in response to additional waves of COVID-19 and other pandemics. • The COVID-19 pandemic significantly disrupted travel for public transit users. • Transit riders reduced their travel more than non-riders. • Among transit riders, those of lower income reduced their travel less than others. • The majority of transit riders were hesitant to use transit due to infection risk. • Less crowding, and enforcing mask use, could increase willingness to use transit. [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]

Investment regulations implemented by the US government as part of the response to COVID-19 have implications for capital flows. The objective of this paper is to analyse how these regulations affect FDI flows, the potential long-term consequences for the United States and developing economies. We use secondary data to chart trends and simple descriptive and analytic framework to synthesise arguments and perspectives. The effects of COVID-19 related restrictions on FDI in the U.S. run through the invocation of the Defense Production Act. Our analysis indicates that these restrictions are highly concentrated on investments that fall under the regulations of Committee on Foreign Investment in the United States, which has increasingly become active in restricting investments flows. While this bodes ill for FDI inflows into the U.S and global development prospects, it is also an opportunity for developing countries to reform and refocus on FDI flows from emerging economies. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 in 2020 has a severe impact on all countries. This paper first studies the impact of the epidemic on the macro-economy in China, which is one of the countries with early epidemic situation, in the rapid development stage of the epidemic situation, and the specific performance in macro-economy aspects. On this basis, study the development trend and macroeconomic performance of the United States as one of the most serious countries, and the relationship between the epidemic development and macro-economy in the United States. As for the differences in epidemic control measures between China and the US, China's indicators are significant in terms of sentiment factors, but the impact of COVID-19 on the sentiment factors in the US is different. The Chinese government has not adopted unlimited easing and maintained consistent policies. At present, the economic impact of COVID-19 on the US is far greater than that of China. [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]

The COVID-19 pandemic has disproportionately impacted ethnic minorities in the global north, evidenced by higher rates of transmission, morbidity, and mortality relative to population sizes. Orthodox Jewish neighbourhoods in London had extremely high SARS-CoV-2 seroprevalence rates, reflecting patterns in Israel and the US. The aim of this paper is to examine how responsibilities over health protection are conveyed, and to what extent responsibility is sought by, and shared between, state services, and 'community' stakeholders or representative groups, and families in public health emergencies. The study investigates how public health and statutory services stakeholders, Orthodox Jewish communal custodians and households sought to enact health protection in London during the first year of the pandemic (March 2020–March 2021). Twenty-eight semi-structured interviews were conducted across these cohorts. Findings demonstrate that institutional relations – both their formation and at times fragmentation – were directly shaped by issues surrounding COVID-19 control measures. Exchanges around protective interventions (whether control measures, contact tracing technologies, or vaccines) reveal diverse and diverging attributions of responsibility and authority. The paper develops a framework of public health relations to understand negotiations between statutory services and minority groups over responsiveness and accountability in health protection. Disaggregating public health relations can help social scientists to critique who and what characterises institutional relationships with minority groups, and what ideas of responsibility and responsiveness are projected by differently-positioned stakeholders in health protection. • COVID-19 produced diverse responses and relations around health protection in London. •Pandemic preparedness can be enhanced by mapping possible partners in localities. •Establishing public health partnerships requires adequate resources. •Public health services cannot defer responsibility entirely to local partners. •Households are key constituents of public health relations. [ABSTRACT FROM AUTHOR]

In a very brief period, the COVID-19 pandemic has swept across the planet leaving governments, societies, and healthcare systems unprepared and under-resourced. New York City now represents the global viral epicenter with roughly one-third of all mortalities in the United States. To date, our hospital has treated thousands of COVID-19 positive patients and sits at the forefront of the United States response to this pandemic. The goal of this paper is to share the lessons learned by our spine division during a crisis when hospital resources and personnel are stretched thin. Such experiences include management of elective and emergent cases, outpatient clinics, physician redeployment, and general health and wellness. As peak infections spread across the United States, we hope this article will serve as a resource for other spine departments on how to manage patient care and healthcare worker deployment during the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

COVID-19 is the most devastating pandemic that affected humanity and the world economy. This paper aimed to study the time-varying connectedness between the COVID-19 vaccination, infection rate (INFR), and the case fatality ratio (CFR) in the United States and the stock market returns. We used COVID-19 daily confirmed number of infections, deaths, and vaccinations and the daily US stock market index return. A wavelet coherence approach was used to assess the co-movement of the US stock market with the COVID-19 vaccination, INFR, and the CFR. The COVID-19 vaccination, INFR, and CFR have a positive and significant influence on S&P 500 returns at the majority of business cycle frequencies with an in-phase relation. The wavelet coherence analysis uncovers strong and significant connectedness between COVID-19 vaccination rate and S&P 500 return. From an economic perspective, the US government should continue its intervention with their vaccination strategy, as it is beneficial for fighting the pandemic. This may lead to the recovery of the stock market as well as to the whole economy. [ABSTRACT FROM AUTHOR]

The Delta-Omicron wave of the COVID-19 pandemic (Wave 4) in the United States occurred in Fall of 2021 through Spring of 2022. Although vaccinations were widely available, this was the deadliest period to date in the U.S., and the toll was especially high in rural areas, exacerbating an existing rural mortality penalty. This paper uses county-level multilevel regression models and publicly available data for 47 U.S. states and the District of Columbia. We describe differences in COVID-19 case and mortality rates across the rural-urban continuum during Wave 4 of the COVID-19 pandemic. Using a progressive modeling approach, we evaluate the relative contribution of a range of explanatory factors for the rural disadvantage we observe, including: pre-pandemic population health composition, vaccination rates, political partisanship, socioeconomic composition, access to broadband internet rate, and primary care physicians per capita. Results show that rural counties had higher observed burdens of cases and deaths in Wave 4 compared to more urban counties. The most remote rural counties had Wave 4 COVID-19 mortality rates 52% higher than the most urban counties. Older age composition, worse pre-pandemic population health, lower vaccination rates, higher share of votes cast for Donald Trump in the 2020 Presidential election, and lower socioeconomic composition completely explained the rural disadvantage in reported COVID-19 case rates in Wave 4, and accounting for these factors reversed the observed rural disadvantage in COVID-19 mortality. In models of mortality rate, Trump vote share had the largest effect size, followed by the percentage of the population age 50 or older, the poverty rate, the pre-pandemic mortality rate, the share of residents with a 4-year college degree, and the vaccination rate. These findings add to a growing literature describing the disproportionate toll of the COVID-19 pandemic on rural America, highlighting the combined effect of multiple sources of rural disadvantage. • Rural counties had higher case and death rates in the Delta-Omicron wave. • Rural counties had up to 51% more deaths per capita than the most urban counties. • Age, health, and SES composition partially explained the rural disadvantage. • Vaccination and Trump vote share were separate factors in the rural disadvantage. • Multiple sources of disadvantage combined in rural areas to produce poor outcomes. [ABSTRACT FROM AUTHOR]

• The proliferation of novel viruses has put traditional health systems under immense pressure and posed several serious issues. Henceforth, early detection, identification, rapid testing, and advanced surveillance systems are required to address public health emergencies. • The widespread of COVID-19 and other infectious diseases have significantly emphasized the importance of Information and Communication Technology (ICT) for timely detection, prevention, and control of viral infections. • This paper assesses the international research status of this field in terms of citation structure, prolific journals, co-occurrence network, intellectual landscape, and country contributions. • This article explores influential articles, active research topics, resarch hotspots, and emerging ICT trends in the scientific literature of infectious diseases. The prevalence of severe infectious diseases has become a major global health concern. Currently, the COVID-19 outbreak has spread across the world and has created an unprecedented humanitarian crisis. The proliferation of novel viruses has put traditional health systems under immense pressure and posed several serious issues. Henceforth, early detection, identification, rapid testing, and advanced surveillance systems are required to address public health emergencies. However, Information and Communication Technology (ICT) tackles several issues raised by this pandemic and significantly improves the quality of services in the health care sector. This paper presents an ICT-assisted scientometric analysis of infectious diseases, namely, airborne, food & waterborne, fomite-borne, sexually transmitted illnesses, and vector-borne illnesses. It assesses the international research status of this field in terms of citation structure, prolific journals, and country contributions. It has used the CiteSpace tool to address the visualization needs and in-depth insights of scientific literature to pinpoint core hotspots, research frontiers, emerging research areas, and ICT trends. The research finding reveals that mobile apps, telemedicine, and artificial intelligence technologies have greater scope to reduce the threats of infectious diseases. COVID-19, influenza, HIV, and malaria viruses have been identified as research hotspots whereas COVID-19, contact tracing applications, security and privacy concerns about users' data are the recent challenges in this field that need to address. The United States has produced higher research output in all domains of infectious diseases. Furthermore, it explores the co-occurrence network analysis and intellectual landscape of each domain of infectious diseases. It provides potential research directions and insightful clues to researchers and the academic fraternity for further research. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction. • This paper aims at predicting domestic and international air travel. • Predictors include WEI, daily trips, COVID-19 variables, and travel restrictions. • Methodology: Neural network and Monte Carlo simulations. • Air travel is more sensitive to changes in WEI than COVID-19 variables. • It will take several years for air travel to return to normal. [ABSTRACT FROM AUTHOR]

In many parts of the world nursing home residents have experienced a disproportionate risk of exposure to COVID-19 and have died at much higher rates than other groups. There is a critical need to identify the factors driving COVID-19 risk in nursing homes to better understand and address the conditions contributing to their vulnerability during public health crises. This study investigates the characteristics associated with COVID-19 cases and deaths among residents in U.S. nursing homes from 2020 to 2021, with a focus on geospatial and racial inequalities. Using data from the Centers for Medicare and Medicaid Services and LTCFocus, this paper uses zero-inflated negative binomial regression models, Kruskal-Wallis tests, and Local Moran's I to generate statistical and geospatial results. Our analysis reveals that majority Hispanic facilities have alarmingly high COVID-19 cases and deaths, suggesting that these facilities have the greatest need for policy improvements in staffing and financing to reduce racial inequalities in nursing home care. At the same time we also detect COVID-19 hot spots in rural areas with predominately White residents, indicating a need to rethink public messaging strategies in these areas. The top states with COVID-19 hot spots are Kentucky, Pennsylvania, Illinois, and Oklahoma. This research provides new insights into the socio-spatial contexts and inequities that contribute to the vulnerability of nursing home residents during a pandemic. • Geographic and racial inequalities are important indicators of covid-19 risk. • Covid-19 cases & deaths are high among facilities with majority Hispanic residents. • Facilities with majority Hispanic residents have high covid-19 vaccination rates. • Most covid-19 hot spots of cases & deaths are in predominantly White, rural areas. • The top states with covid-19 hot spots of cases & deaths are KY, PA, IL, and OK. [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]

While the overall impact of the COVID-19 pandemic on U.S. population health has been devastating, it has not affected everyone equally. The risks of hospitalization and death from the disease are relatively low for the population as a whole, but much higher for specific subpopulations defined by age, health status, and race or ethnicity. The extent to which Americans perceive these disparities is an open question, with potentially important political implications. Recognition of unequal impacts may prime concerns about justice and fairness, making Americans more concerned and willing to support government intervention. On the other hand, belief that the pandemic primarily threatens "other people" or out-groups may reduce, rather than increase, a person's concern. Partisanship and media consumption habits are also likely to play a role in these perceptions, as they do in most issues related to COVID-19. In this paper, we use original survey data from the Cooperative Election Study (N = 1000) to explore Americans' perceptions of which groups are most harmed by the pandemic, the demographic and political determinants of these perceptions, and the relationship of these perceptions with their opinions about COVID-related mitigation policy. We find that, on average, people perceived accurately that certain groups (e.g., Black Americans, older people) were more affected, but these group perceptions varied by demographic and political characteristics of respondents. We find, in contrast with recent experimental evidence, that the perception that populations of color were harmed was associated with more support for pandemic mitigation strategies. More research should investigate the relationships among pandemic politics and the racial dynamics of the target populations most affected. • In fall 2020, survey participants recognized groups harmed by COVID based on age. • They had lower recognition of racial groups harmed, lowest for Native Americans. • Perceptions of groups harmed varies by information exposure and politics. • Perceiving group disparities was associated with more support for pandemic policy. • The racial dynamics surrounding the politics of COVID-19 requires more attention. [ABSTRACT FROM AUTHOR]

• Mothers have experienced a significant amount of stress during the COVID-19 pandemic stay-at-home orders in the US, which can have major implications for children's health and development; attention should be paid to developmental timing and unfolding of developmental trajectories following this pandemic. • Mothers have used a variety of strategies to cope; as more parents navigate social disruptions, they may benefit from additional resources and supports, such as innovations in childcare modalities, paid leave policies, and strengthening whole family processes using adaptive coping mechanisms. • Mothers with low income and/or children with special needs may experience more vulnerability to the sequalae of the pandemic; it is important to provide financial security, mental health, and educational and social support to relieve stress during the pandemic. This paper presents the results of a qualitative study of mothers' lived experiences during the COVID-19 lockdown in the United States. An analysis of open-ended interviews with 44 mothers who had children ages zero-to-five identified two main themes: (1) increased stress among mothers; and (2) resilience through the use of coping mechanisms. The findings indicate that the COVID-19 pandemic has led to higher stress among mothers due to issues of work-family life balance, family and children's needs, decision-making about getting sick, concerns for children's development, and lack of clarity from government officials. Mothers described using a variety of problem-focused and emotion-focused methods to cope with this stress. The lived experiences of mothers during the pandemic highlights the need for innovations in childcare modalities, paid leave policies to relieve stress, and strengthening whole family processes and resilience through the use of coping mechanisms. [ABSTRACT FROM AUTHOR]

A major challenge in managing disasters during a pandemic is assessing the inequalities in society and protecting vulnerable people. The objective of this paper is to geographically understand the discrimination and inequality against minorities by COVID-19. This study designed a locational discrimination index (LDI) to measure COVID-19 discrimination against minorities at county-level in the US. LDI is the difference between the death proportion of a minority and the proportion of a minority's population. If LDI >0 is significant, COVID-19 discrimination is identified against a minority in a county. I further developed a locational minority inequality index (LMII), and LMII of a minority is directly quantified by comparing its LDI with the LDI of the majority population (i.e., the White population in the US). If LMII>0 is significant, a significant health inequality is confirmed against a minority in a county. In the US, I found 157 counties with significant discrimination against Black people, and 103 counties with significant inequality against Black people; 58 counties with significant discrimination against the American Indian population, but 38 counties with significant inequality against the American Indian population; 17 counties with significant discrimination against Native Hawaiians, but only 8 counties with significant inequality; for Hispanic people, 47 counties had significant discrimination, and 64 counties had significant inequality; for Asians, 7 counties had significant discrimination, but 28 had significant inequality. LDI, LMII, and the thematic mapping provide novel insight into COVID-19 discrimination and inequalities. To the best of our knowledge, this is the first time anyone has quantitatively and statistically defined and mapped COVID-19 discrimination and inequality against minorities at a county-level across the US. Based on this, governments and communities could make efficient decisions and take effective action to addressthe significant discrimination and inequality against Black, American Indian, Native Hawaiian, Hispanic, and Asian people, which can be applied to other pandemics or public health disasters in the USA or other countries. • The significant discrimination and inequality of COVID-19 are tested and mapped. • Locational discrimination index to quantify minority's vulnerability is developed. • Locational minority inequality index is developed to further compare inequality in vulnerability. • COVID-19 caused significant spatial inequality between minorities and the white population in the USA. [ABSTRACT FROM AUTHOR]

• COVID-19-caused resilience performance of metro rails was evaluated quantitatively. • Key characteristics of responses to the disturbance were modeled with multivariate multiple regression. • The applicability and rationality of developed models were validated with U.S. metro data. The metro rail system has proven to be the most efficient high-capacity carriers. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions become a widely adopted strategy to limit physical movements and interactions. For situational awareness and decision support, data-driven analytics about serviceability are invaluable to metro agencies and decision-makers of cities. This paper presents a data-driven analytical framework that quantitatively evaluates COVID-19-caused resilience performance of metro rails. Several characteristics (e.g., vulnerability, robustness, rapidity, and degree to return) of the metro system's responses to the disturbance were identified and modeled with multivariate multiple regression. The applicability and rationality of the resilience evaluation model were validated by the metro transit data of the United States. The preliminary results disclosed that metro rail transit encountered more vulnerability (90.6%) in passenger trips than motorbus and light rail (around 70%). A set of statistical models were employed to disentangle the effect of socio-demographic variables and COVID-19-related factors on the metro transit. The disclosed emerging knowledge of resilience provides an in-depth understanding of mobility trends for the public and time-sensitive decision support for the policy effects, to further improve the service and management of the metro system under the spread of the epidemic. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19, and its current resurgence in the United States has resulted in a shortage of isolation rooms within many U.S. hospitals admitting COVID-19-positive cases. As a result, hospital systems, especially those at an epicenter of this outbreak, have initiated task forces to identify and implement various approaches to increase their isolation capacities. This paper describes an innovative temporary anteroom in addition to a portable air purifier unit to turn a general patient room into an isolation space. Using an aerosolization system with a surrogate oil-based substance, we evaluated the effectiveness of the temporary plastic anteroom and the portable air purifier unit. Moreover, the optimal location of the portable unit, as well as the effect of negative pressurization and door opening on the containment of surrogate aerosols were assessed. Results suggested that the temporary anteroom alone could prevent the migration of nearly 98% of the surrogate aerosols into the adjacent corridor. Also, it was shown that the best location of a single portable air purifier unit is inside the isolation room and near the patient's bed. The outcome of this paper can be widely used by hospital facilities managers when attempting to retrofit a general patient room into an airborne infection isolation room. • Temporary anteroom and portable HEPA filter were used to make an expedient isolation room. • The anteroom alone prevented nearly 98% of aerosols from migrating to adjacent corridor. • The HEPA filter unit was most effective when placed inside the isolation space next to patient bed. • The combination of anteroom, HEPA filtration, and negative pressure is an effective isolation strategy. [ABSTRACT FROM AUTHOR]

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission. • The determinants of the spread of COVID-19 vary in different epidemic waves. • People in poverty were vulnerable to the spread of COVID-19 during all three waves. • Socio-economic factors were related with the virus transmission in the early waves. • Density and service levels of daily facilities showed effect in subsequent waves. • Policy implications for planning and structuring anti-virus places are suggested. [ABSTRACT FROM AUTHOR]

Background and Aims: About 18% of college students have disabilities. Social capital, resources we can tap from relationships, may be particularly valuable for students with disabilities. Yet, disabilities often limit the individual's ability to develop or use social capital. We studied how college students with developmental disabilities understand, develop, and use social capital.Methods and Procedures: We conducted in-depth semi-structured Zoom interviews with 10 women with developmental disabilities enrolled at a public university in the southeastern United States early in 2021. We examined the qualitative data with thematic analysis.Outcomes and Results: Participants averaged age 20; 70% reported attention deficit disorder or attention deficit hyperactivity disorder; 90% reported multiple diagnoses. Most participants described COVID-19 pandemic-related isolation and stress, which magnified both the need for relationships and awareness of that need, prompting participants to become proactive in forming and maintaining relationships despite anxiety about them. Themes were: foundational relationships, reciprocity, expanding horizons, a need for new relationships, focus on the future and relationship barriers.Conclusions and Implications: Results highlight the importance of social relationships and the resources they provide to students with disabilities, particularly in stressful times. Colleges can help students by connecting them with others and providing strategies for building and maintaining social capital.What This Paper Adds: College students with developmental disabilities often face challenges developing and maintaining social capital, resources derived from relationships with other people. These resources are key to success in school and after graduation, as students continue into adulthood. We studied how students with developmental disabilities build social capital. The students described their relationships with others and the types of support they contributed to and received from those relationships. We also extended previous research by examining pandemic-related effects, interviewing participants nearly one year into the COVID-19 pandemic. We provide recommendations for further research and ways colleges and universities can encourage social capital development among all students. [ABSTRACT FROM AUTHOR]

Research in several countries shows higher Covid-19 vaccination willingness and uptake among physicians than nurses. Our paper aims to characterize and explain this difference. In early 2021, we surveyed 1047 U.S. primary care professionals who served adolescents, ages 11–17. The national sample included physicians (71%) as well as nurses and advanced practice providers. The survey assessed the three domains of the Increasing Vaccination Model: thinking and feeling , social processes , and direct behavior change. Covid-19 vaccine uptake was higher among physicians than among nurses and advanced practice providers (91% vs. 76%, p <.05). Overall, in the thinking and feeling domain, higher confidence in Covid-19 vaccination, higher perceived susceptibility to the disease, and stronger anticipated regret were associated with higher vaccine uptake (all p <.05). In the social processes domain, perceiving more positive social norms for Covid-19 vaccination, receiving recommendations to get the vaccine, and wanting to help others were associated with higher vaccine uptake (all p <.05). In the direct behavior change domain, receiving an invitation to get the vaccine and better access to vaccination were associated with higher uptake (both p <.05). Of these variables, most of the thinking and feeling and social processes variables mediated the association of training with vaccine uptake. Physicians had higher Covid-19 vaccine uptake than nurses and advanced practice providers, corresponding with their more supportive vaccine beliefs and social experiences. Efforts to reach the remaining unvaccinated cohort can build on these findings. • Covid-19 vaccine uptake among advanced practice providers and nurses was lower than in physicians. • Vaccine confidence and social norms explained the difference in Covid-19 vaccine uptake. • Recommendations from employers and providers suppressed some differences in vaccination. • Behavioral nudges – invitation and incentives – were associated with more vaccine uptake. • Offers directions and contexts for future vaccination interventions and programs in the U.S. [ABSTRACT FROM AUTHOR]

This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths. • We compare and contrast the natural history of COVID-19 in the US, India and Brazil. • Case, death and mortality rate trajectories are analysed on a state-by-state basis. • We quantify heterogeneity and identify anomalous states therein within each country. • Wave behaviours are explored and clustered across the three countries states. • New mathematical methods quantify the lag in the progression from cases and deaths. [ABSTRACT FROM AUTHOR]

In this paper we analyze the average hourly temporal dynamics of human mobility in the United States from 2019 to 2020. We discuss how large decreases in human mobility nonuniformly effect the daily temporal dynamics of aggregate human behavior. The data used are weekly activity patterns for POIs from 2019 to 2020 in the United States, provided by SafeGraph and made openly available to academic and research institutions. We use clustering methods to create metrics describing how human activity changes throughout the day/week at the county and national levels. In response to significant mobility reductions starting March 2020, daily temporal patterns of human activity changed nonuniformly. Morning activity started later, and evening activity started earlier in 2020 compared to 2019, and temporal behavioral patterns on weekdays began to look more similar to weekends. The changes in daily temporal behavior persisted throughout the year even as total mobility levels recovered. The results provide insights on the changes in human behavior in response covid-19 policies and illustrate influences on social systems, health, and transportation networks. [ABSTRACT FROM AUTHOR]

This paper presents the first longitudinal study of sex disparities in COVID-19 cases and mortalities across U.S. states, derived from the unique 13-month dataset of the U.S. Gender/Sex COVID-19 Data Tracker. To analyze sex disparities, weekly case and mortality rates by sex and mortality rate ratios were computed for each U.S. state, and a multilevel crossed-effects conditional logistic binomial regression model was fitted to estimate the variation of the sex disparity in mortality over time and across states. Results demonstrate considerable variation in the sex disparity in COVID-19 cases and mortalities over time and between states. These data suggest that the sex disparity, when present, is modest, and likely varies in relation to context-sensitive variables, which may include health behaviors, preexisting health status, occupation, race/ethnicity, and other markers of social experience. • First report of findings from unique longitudinal dataset of sex-disaggregated COVID-19 data in U.S. states. • Sex disparities in COVID-19 are highly heterogeneous in magnitude and direction and vary across states and over time. • Gender-related and other social and contextual factors likely shape COVID-19 sex disparities. • Single-factor approaches are ill-positioned to explain patterns in sex disparities in COVID-19 outcomes. [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]

• COVID-19 modified when, where, and how we use electricity. • The impact of COVID-19 varied across spatiotemporal scales. • There are multiple offsetting effects that can mask the impact of COVID-19 on electricity demand. Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) has been shown to have more severe health outcomes in older people specifically in relation to mortality and disability. Vaccination seems to be efficacious and safe for preventing the negative consequences of COVID-19, but vaccine hesitancy seems to be high in older adults. We therefore aimed to investigate the prevalence of unwillingness and the uncertainty to vaccinate against COVID-19 in older people and the factors that can be associated with the unwillingness to vaccinate. For this work, we searched several databases until 18th June 2021 for studies reporting the prevalence of unwillingness and the uncertainty to vaccinate against COVID-19 in people aged >60 years. A meta-analysis of the prevalence, with the correspondent 95% confidence intervals (CIs), was proposed. Factors that can be associated with the unwillingness to vaccinate against COVID-19 were explored through multivariable analyses and reported as odds ratios (ORs). Among 662 papers initially screened, we included 15 studies for a total of 9753 older adults. The prevalence of unwillingness to vaccinate against COVID-19 in older people was 27.03% (95%CI: 15.10–38.95%), whilst the correspondent figure of uncertainty was 19.33% (95%CI: 12.28–26.39). The risk of being unvaccinated was significantly higher in Hispanics (OR=1.197; 95%CI: 1.010–1.418) and in case of low education (OR=1.678; 95%CI: 1.170–2.408) and low income (OR=1.287; 95%CI: 1.127–1.469). In conclusion, the hesitancy for COVID-19 vaccination is a relevant problem in older people, particularly in those with a low income, a low level of education, and in Hispanics living in the United States. • COVID-19 is considered a geriatric syndrome people. • vaccine hesitancy in older people can affect vaccine uptake. • The prevalence of unwillingness and uncertainty seems to be higher in older adults compared to younger persons. • The risk of being unvaccinated was significantly higher in Hispanics and in case of low education and low income. [ABSTRACT FROM AUTHOR]

COVID-19 fear restricts human freedom and decreases human flourishing. This paper examines the relationship between COVID-19 fear and human flourishing. Additionally, it tests gender's moderating effect in this relationship. Although studies show the relationship between COVID-19 fear and human flourishing, this study is a first attempt to determine gender's moderating role. Data were obtained from 326 hotel staff in the United States and analyzed using structural equation modeling and hierarchical regression. This study found that COVID-19 negatively affected human flourishing and gender moderated this relationship. The current study will contribute to the literature by clarifying that women's flourishing is more negatively affected than men due to COVID-19. This finding further indicates how to reduce women's COVID-19 fears from both practical and legal standpoints. [ABSTRACT FROM AUTHOR]

This paper explores the role of social capital in mitigating the mental health harms of social/mobility restrictions instigated in the wake of the COVID-19 pandemic. We test whether: (a) social capital continued to predict lower mental distress during the pandemic; and (b) whether social capital buffered (moderated) the harm of social/mobility restrictions on psychological distress. In addition, we test the level at which social capital mitigation effects operated, i.e., at the individual- and/or contextual-level. To do so, we apply multilevel models to three waves of the COVID-19 Household Impact Survey consisting of probability samples of U.S. adults (with the average interview completion rate of 93%). In a novel approach, we explore two modes of capturing contextual social capital: aggregated individual-level survey responses and independently measured social capital indices (SCIs). Findings show that at the individual level social capital was associated with lower psychological distress. It also buffered the harm of restrictions: increasing restrictions had a weaker effect on distress among individuals interacting with neighbors more frequently. Importantly, mitigating processes of contextual social capital appeared conditional on how it was measured. Using aggregated survey responses, contextual social capital had no direct effect on distress but exerted an additional buffering role: individuals in counties with higher average neighbor-interaction experienced a weaker impact of restrictions. Using the independent SCI measures, we found county social capital reduced distress. However, its negative effect on distress becomes increasingly weaker the more restrictions an individual reported: where individuals reported lower restrictions, higher county SCI reduced distress; however, where individuals reported higher restrictions, higher county SCI had no effect on distress. More restrictive environments thus cut individuals off from the benefits of higher county social capital as measured using the SCI. • Individual social capital (SC) is associated with lower psychological distress. • Individual SC buffers the negative effect of mobility restrictions on distress. • People with higher ecological SC experienced a weaker effect of mobility restrictions. • Those living in higher SC counties reported less distress but only with fewer restrictions. [ABSTRACT FROM AUTHOR]

• Proposing an interpretable utility-based framework for dynamic activity pattern estimation. • Mapping from individual trip chains to aggregated activity location types. • Estimating traveler behavior with the support of multiple data sources. • Integrating machine learning tools, nested logit model and transportation domain knowledge. • Testing the proposed framework using 2017 national household survey and Google mobility dataset. During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases. [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]

In 2020, most countries around the world have observed varying degrees of public lockdown measures to mitigate the transmission of SARS-CoV-2. As an unintended consequence of reduced transportation and industrial activities, air quality has dramatically improved in many major cities around the world. In this paper, we analyze the environmental impact of the lockdown measures on P M 2.5 concentration levels in 48 core-based statistical areas (CBSA) of the United States, during the pre and post-lockdown period of January to June 2020. We model the effect of lockdown on the P M 2.5 concentration in different CBSAs while adjusting for various meteorological factors like temperature, wind-speed, precipitation and snow. Linear mixed effects models and functional regression methods with random intercepts are employed to capture the heterogeneity of the effect across different regions. Our analysis shows there is a statistically significant reduction in levels of P M 2.5 across most of the regions during the lock-down period, although interestingly, this effect is not uniform across all the CBSAs under consideration. • We examine the impact of the COVID-19 lockdown measures on the PM2.5 concentration level in USA. • We also investigate if there is any spatio-temporal heterogeneity in the effect of lockdown on air quality. • We use Linear mixed effects models and functional regression methods as primary tools of our analysis. • We adjust for local meteorological variables to remove their confounding effect on PM2.5 concentration. [ABSTRACT FROM AUTHOR]

• Bitcoin has appeared as safe haven asset and a winner amid uncertainties in the US. • Political and economic uncertainties can lead the bitcoin prices in the US. • Wavelet-based quantile-on-quantile approach helps to validate safe haven hypothesis. • Causality structure between uncertainties and bitcoin prices varies in cycles. • Investors in the US may cautiously use bitcoin as a hedge against uncertainties. Bitcoin is considered to be an exclusive marvel of the Fourth Industrial Revolution, and is one of the most sophisticated technological and financial products. It has long been a pivot point of attention for investors who are in pursuit of a safe haven asset. In this paper, we use the wavelet-based quantile-on-quantile method, and the quantile-based Granger causality method, in order to investigate the notion of Bitcoin in actually being a safe-haven asset, amid political and economic uncertainty in the US for the period between 2010:M06, and 2020:M10. Using the Partisan Conflict Index (PCI), and the Economic Policy Uncertainty Index (EPU) as proxies of uncertainties, we find that although Bitcoin effectively appears to be a safe haven asset when uncertainties are on the rise, however, this relationship tends to change during the short- to long-run. In this regard, our sample provides us with a unique opportunity to evaluate the safe haven hypothesis for Bitcoin, amid a time span with three Presidential elections in the US, and recently, an ongoing COVID-19 outbreak, which has been declared as a global pandemic. We have also supplemented our analysis with the bootstrap rolling window causality method, as a measure of robustness. In light of the recent COVID-19 pandemic, and the dynamic economic situation, our work provides valuable knowledge for investors, who wish to construct investment portfolios based on Bitcoin, and also provide insights for regulators about how to regulate the cryptocurrency speculation in an effective manner. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in immediate and wide impacts on human and agricultural systems. While some of the positive and negative impacts of COVID-19 on the environment and economies are emerging, there is not a comprehensive understanding of the potential impacts of COVID-19 on the most vulnerable farmers. The purpose of this study is to evaluate the immediate impacts of COVID-19 on agricultural and food systems in the United States. Our aim is to quantify the impacts on labor productivity in crops and livestock production considering the heterogenous vulnerability of different farmworkers. We are interested in measuring the production that is not realized due to COVID-19. In this paper, we introduce IMLAP, Immediate impact Model of Local Agricultural Production. This model is an economic framework considering short-term agricultural production responses to economic, environmental, and policy changes. We investigate the potential impacts of COVID-19 on the farmers in the U.S. for each county with a special focus on female, Hispanic, black and African American, and small-scale producers. Considering the impacts of COVID-19 on labor, the findings of this study suggest a decline in agricultural output in all the U.S. counties ranging from 1.18% to 7.14% of total production. Our simulation results show that counties with a higher number of small-scale farms, non-white farmers, and female-operated farms are the most vulnerable to COVID-19. Also, we argue that the stimulus policies and support packages must target these communities of producers to ensure that their livelihood is protected. The findings suggest that productivity growth (technological improvements) and international trade can eliminate the negative impacts of pandemics. The proposed quantitative framework of this study is a simple yet novel model that empowers diverse research communities to provide a quick analysis of the impacts of unprecedented events. It offers a holistic framework to evaluate the response of agricultural production to changes in availability and productivity of labor, machinery & equipment, land, fertilizer, seeds, and other inputs. This study presents new foundations for agricultural research communities to provide solutions to agricultural resilience challenges and highlights the significance of demand drivers, technological growth, and international trade in strengthening the food system. [Display omitted] • COVID-19 racial and ethnic health disparities require attention to production impacts linked to the vulnerable communities. • Novel modeling is introduced for immediate impact assessments of unprecedented shocks on labor productivity and production. • The COVID-19 burden on agricultural production is estimated to be around 2.63% of total farm outputs in the United States. • Continued growth in productivity, yields, and exports can eliminate the negative impacts of COVID-19 on agriculture. • The impacts are heterogeneous across communities suggesting community-specific supportive policy. [ABSTRACT FROM AUTHOR]

Home health care is a rapidly growing healthcare sector worldwide. Home health professionals face unique challenges related to preventing and controlling infections, which are likely to amplify during an infectious disease outbreak (e.g. SARS-CoV-2). Little is known about the current state of infection prevention and control-related policies and outbreak preparedness at U.S. home health agencies. In this study, we conducted a national survey to assess infection prevention and control-related policies, infrastructure, and procedures prior to the SARS-CoV-2 pandemic. Cross-sectional study. Using a stratified random sample of 1506 U.S. home health agencies, we conducted a 61-item survey (paper and online) from November 9, 2018 to December 31, 2019. Survey data were linked to publicly-available data on the quality of patient care, patient satisfaction, and other agency characteristics. Probability weights were developed to account for sample design and nonresponse; Pearson's χ2, Fisher's exact, t-tests or linear regression were used to compare the universe of agencies/respondents and urban/rural agencies. 35.6% of agencies responded (n = 536). Most home health personnel in charge of infection prevention and control have other responsibilities; one-third have no formal infection prevention and control training. Rural agencies are more likely to not have anyone in charge of infection prevention and control compared to those in urban areas. About 22% of agencies implement recommended guidelines when administering antibiotics. Less than a third (26.4%) report that their staff vaccination rates were higher than 95% during the last flu season. Only 48.1% of agencies accept patients requiring ventilation, and of those, 40.9% located in rural areas do not have specific infection prevention and control policies for ventilated patients, compared to 20.8% in urban areas (p < 0.001). Only 39.7% of agencies provide N95 respirators to their clinical staff; rural agencies are significantly more likely to provide those supplies than urban agencies (50.7% vs. 37.7%, p = 0.004). Lastly, agencies report their greatest challenges with infection prevention and control are collecting/reporting infection data and adherence to/monitoring of nursing bag technique. Prior to the SARS-CoV-2 pandemic, we found that infection prevention and control was suboptimal among U.S. home health care agencies. Consequently, most agencies have limited capacity to respond to infectious disease outbreaks. Staff and personal protective equipment shortages remain major concerns, and agencies will need to quickly adjust their existing infection prevention and control policies and potentially create new ones. In the long-term, agencies also need to improve influenza vaccination coverage among their staff. Tweetable abstract: Infection prevention and control infrastructure, policies and procedures and outbreak preparedness at U.S. home health agencies was found to be suboptimal in nationally-representative survey conducted just prior to the COVID-19 pandemic. [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]

The COVID-19 pandemic has triggered a notable increase in the expression of prejudicial and xenophobic attitudes that threaten the wellbeing of minority groups and contribute to the overall public health toll of the virus. However, while there is evidence documenting the growth in discrimination and xenophobia, little is known about how the COVID-19 outbreak is activating the expression of such negative attitudes. The goal of the current paper therefore was to investigate what aspects of the COVID-19 pandemic may be contributing to this rise in expressions of prejudice and xenophobia. More specifically, this study used an experimental design to assess the effects of using stigmatized language to describe the virus as well as the threat to physical health and economic wellbeing posed by the virus on COVID-19 prejudice. Data were collected from a national sample of 1451 adults residing within the United States. Results from 2 × 2 x 2 between-subjects analyses of covariance demonstrated that emphasizing the connection between China and COVID-19, rather than framing the virus neutrally, increased negative attitudes toward Asian Americans, beliefs that resources should be prioritized for Americans rather than immigrants, and general xenophobia. Emphasizing the severity of the economic impact of the virus also increased beliefs that Asian Americans are a threat to resources and general xenophobia. In contrast, messages which emphasized the serious health risks of COVID-19 did not increase bias toward Asian Americans or xenophobia. Our findings suggest that specific types of public health messaging related to infectious diseases, especially framing the virus in terms of its country of origin or its likely economic impact, may elicit prejudice and xenophobia. Public health campaigns that emphasize the severity of the virus, however, are not likely to trigger the same negative attitudes. Implications for public health responses to health crises are discussed. • The framing of COVID-19 information shapes prejudice and xenophobia. • Emphasizing COVID-19's origin in China increases anti-Asian and xenophobic attitudes. • Emphasizing severe economic harm also increases negative outgroup attitudes. • Messaging about the health threat of the virus is unrelated to outgroup bias. [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]