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The purpose of this paper is to study the role of broadband in mitigating the economic losses resulting from COVID-19 in the United States by providing a necessary infrastructure to keep economic systems operating, albeit partially. The study is based on an empirical framework underlined by a Cobb–Douglas production function and estimated within a structural multi-equation model through the three-stage least squares approach. To consider the impact of COVID-19 on the economy, we rely on two main variables: an indicator of the quantity of deaths attributed to the disease for every 100,000 inhabitants; and the Stringency Index, a metric linked to the intensity of social restrictions imposed by national and local governments. The main contribution of this article is to provide robust evidence for how the heterogeneous effects of the pandemic across states are in part explained by differences in broadband adoption. Our results indicate that those states with higher broadband adoption were able to mitigate a larger portion of their economic losses derived from the pandemic-induced lockdowns. Addressing the digital divide and ensuring universal access to broadband represent critical goals for building economic resilience to face future emergencies. [ABSTRACT FROM AUTHOR]

Purpose: This research explores the impact of parental educational attainment, race, ethnicity, gender and employment on the time parents spend educating their children during the COVID-19 pandemic. Design/methodology/approach: School closures to prevent the spread of COVID-19 have affected billions of students worldwide, and have had an impact on the economy and the society. With classes being cancelled or taught remotely, the importance of parental intervention in children's education has accelerated. Findings: The authors find that more educated parents allocate more time on child education, while higher income and employment have an adverse effect. Fathers are likely to spend more time than mothers in teaching and educating their children during COVID-19. Practical implications: The findings have implications in identifying children whose education suffers the most in times of a pandemic and determining the main target group of policies designed to train children, encourage parental involvement and support children's educational development. Originality/value: This is the first paper that examines the variations in parental time with children across social and economic subgroups during the COVID-19 pandemic. The authors also focus on the time parents spend educating their children rather than just supervising them. The authors additionally examine the determinants of the time children study on their own. Finally, the analysis is novel because it is based on the newest available data collected to examine the trends and experiences of individuals in the United States triggered by COVID-19. [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]

• 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 and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic. [ABSTRACT FROM AUTHOR]

This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed. [ABSTRACT FROM AUTHOR]

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

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]

• 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]

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]