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COVID-19 impacts on residential occupancy schedules and activities in U.S. Homes in 2020 using ATUS
Many aspects of the daily lives of those living in the United States were substantially impacted by the COVID-19 pandemic in the year 2020. A broad diversity of measures was implemented to curb the spread of the virus, many of which included adjustments to where and how people worked, went to school, and otherwise conducted their daily lives compared to pre-pandemic times. This has impacted how residential buildings are used, how much time people spend in their homes, and as a result, how much energy these buildings consume. The main objective of this study is to analyze, at a national scale, the differences in the occupancy schedules and activities conducted in homes in the U.S., as compared to pre-pandemic. 15 years of American Time Use Survey and Current Population Survey data, from 2006 to 2020, was used in this study to analyze the occupancy schedules for both pandemic (2020) and pre-pandemic (2006–2019) times. These impacts were also analyzed with respect to variables including, weekday/weekend, month of the year, age of the occupants, household income, and household size. The impact of the pandemic on occupant schedules were most substantial in the initial months, whereas as the months progressed, these occupancy profiles slowly changed. Across 2020, people spent, on average, 8 % more time (1.9 h) in their home on weekdays, and 3–6 % (1.2 h) on weekend days. The percentage of time spent for different activities and locations within homes were also studied. For 1-member households, their time spent at home decreased whereas for 2-, 3-, and 4- member households, they spent more time at home. Overall, people spent around 45% more time doing office- and work-related activities at home compared to pre-pandemic, which is likely due to increased remote working and schooling. This research helps to improve the understanding of the occupancy presence and absence profiles in U.S. residential buildings due to the pandemic and provides new insights as to modified profiles for researchers, building designers, and policy makers.
Machine learning model to project the impact of COVID-19 on U.S. motor gasoline demand
Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.
COVID-19-Related Experiences and Perspectives of Peruvian College Students: A Descriptive Study
The COVID-19 pandemic drastically affected higher education and higher education students around the world, but few studies of college students’ experiences during the COVID-19 pandemic have been conducted in Latin America. This study describes the COVID-19-related experiences and perspectives of Peruvian college students. We surveyed 3,427 full-time college students (average age: 23 years) attending a multi-campus Peruvian university in fall 2020. Participants were recruited through the digital platform of the learning management system at their university, email, and social media. We asked participants how they were managing risks related to COVID-19; the continuity of social, educational, and work activities; and the psychological and economic impacts of the pandemic on their lives. Since March 2020, 73.0% of participants reported COVID-19-related symptoms, but only 33.9% were tested for COVID-19. During the national quarantine imposed by the Peruvian government (March 15–June 30, 2020), 64.3% of participants remained in their house. Furthermore, while 44.0% of participants were working in February 2020 (95% CI: [41.7%, 46.4%]), only 23.6% (95% CI: [21.7%, 25.7%]) were working immediately after the pandemic began (i.e., at the end of April 2020). Participants were more stressed about the health and educational implications of COVID-19 for Peruvian society and their families than about themselves. The public health, economic, and educational implications of COVID-19 on college students are continuing to unfold. This study informed Peruvian higher education institutions’ continued response to the COVID-19 pandemic, the progressive return to postpandemic activities, as well as other future pandemics and other crises.
Work from Home Patterns across COVID-19 Waves: Implications for Future Transportation
The unprecedented rise in work from home (WFH) during COVID-19 poses challenges for the transportation engineers and planners with the travel demand forecasting. If WFH persists post-pandemic, it could influence traffic patterns, reducing peak-hour congestion. However, the evolution of WFH decisions across pandemic phases and varying socio-economic contexts remains unclear. This study examines factors influencing WFH choices using data from the US Census Bureau’s Household Pulse Survey. Findings reveal a decline in WFH participation from 60% during the first wave to 38% by the third wave of the COVID-19 pandemic. A Geographically Weighted Regression model highlights the influence of socio-economic, household, and COVID-19-related variables, with notable spatial variability. Results show that younger individuals, females, and households with children are consistently more likely to WFH, while non-white and higher income individuals have an increasing likelihood for WFH as the pandemic progresses. These insights inform future transportation planning, emphasizing equity and decentralization strategies for post-pandemic commuting.
Changes in When and Where People are Spending Time in Response to COVID-19
The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior such as travel surveys and travel demand models are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky; and Des Moines, Iowa which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.
How Cooperative Engagement Programs Strengthen Sequencing Capabilities for Biosurveillance and Outbreak Response
The threat of emerging and re-emerging infectious diseases continues to be a challenge to public and global health security. Cooperative biological engagement programs act to build partnerships and collaborations between scientists and health professionals to strengthen capabilities in biosurveillance. Biosurveillance is the systematic process of detecting, reporting, and responding to especially dangerous pathogens and pathogens of pandemic potential before they become outbreaks, epidemics, and pandemics. One important tool in biosurveillance is next generation sequencing. Expensive sequencing machines, reagents, and supplies make it difficult for countries to adopt this technology. Cooperative engagement programs help by providing funding for technical assistance to strengthen sequencing capabilities. Through workshops and training, countries are able to learn sequencing and bioinformatics, and implement these tools in their biosurveillance programs. Cooperative programs have an important role in building and sustaining collaborations among institutions and countries. One of the most important pieces in fostering these collaborations is trust. Trust provides the confidence that a successful collaboration will benefit all parties involved. With sequencing, this enables the sharing of pathogen samples and sequences. Obtaining global sequencing data helps to identify unknown etiological agents, track pathogen evolution and infer transmission networks throughout the duration of a pandemic. Having sequencing technology in place for biosurveillance generates the capacity to provide real-time data to understand and respond to pandemics. We highlight the need for these programs to continue to strengthen sequencing in biosurveillance. By working together to strengthen sequencing capabilities, trust can be formed, benefitting global health in the face of biological threats.
Neutralizing Antibodies to SARS-CoV-2 Selected from a Human Antibody Library Constructed Decades Ago
Combinatorial antibody libraries not only effectively reduce antibody discovery to a numbers game, but enable documentation of the history of antibody responses in an individual. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted a wider application of this technology to meet the public health challenge of pandemic threats in the modern era. Herein, a combinatorial human antibody library constructed 20 years before the coronavirus disease 2019 (COVID-19) pandemic is used to discover three highly potent antibodies that selectively bind SARS-CoV-2 spike protein and neutralize authentic SARS-CoV-2 virus. Compared to neutralizing antibodies from COVID-19 patients with generally low somatic hypermutation (SHM), these three antibodies contain over 13–22 SHMs, many of which are involved in specific interactions in their crystal structures with SARS-CoV-2 spike receptor binding domain. The identification of these somatically mutated antibodies in a pre-pandemic library raises intriguing questions about the origin and evolution of these antibodies with respect to their reactivity with SARS-CoV-2.
The impacts of COVID-19 on clean energy labor markets: Evidence from multifaceted analysis of public health interventions and COVID-health factors
COVID-19 pandemic has affected clean energy labor market. Using real-time job vacancy data, this study analyzes the impacts of the pandemic on the U.S. clean energy labor market in 2020, including biomass, energy efficiency (EE), electric vehicle (EV), power/microgrid, solar, and wind industries. This study identifies how COVID-health factors and public health interventions influence clean energy job availability during the early COVID pandemic. Overall, California had the most energy jobs and experienced a significant decrease in April 2020. EV and solar had the highest percentages of job vacancies during the pandemic in general. Still, lockdowns had the most severe influence on EE and wind jobs. Stay-at-home orders negatively affected clean energy job vacancies in biomass, EV, power/microgrid, and wind. Social-gathering restrictions, however, did not have much influence. Increased COVID tests at the state level had the strongest and most positive influence on clean energy job postings, indicating the importance of a state's ability to manage public health infrastructure or crisis issues. COVID hospitalizations negatively influenced the job vacancies in biomass and wind but did not affect the other four sectors; conversely, as COVID death numbers increased, the number of jobs in biomass, EV, power grid, solar, and wind decreased, but not in EE jobs.
Shaping the Future of Science: COVID‐19 Highlighting the Importance of GeoHealth
Abstract From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID‐19) pandemic have brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real‐time policy. The lessons learned throughout our responses to the COVID‐19 pandemic are shaping the future of GeoHealth.
Singleplex, multiplex and pooled sample real-time RT-PCR assays for detection of SARS-CoV-2 in an occupational medicine setting
Abstract For workplaces which cannot operate as telework or remotely, there is a critical need for routine occupational SARS-CoV-2 diagnostic testing. Although diagnostic tests including the CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel (CDC Diagnostic Panel) (EUA200001) were made available early in the pandemic, resource scarcity and high demand for reagents and equipment necessitated priority of symptomatic patients. There is a clearly defined need for flexible testing methodologies and strategies with rapid turnaround of results for (1) symptomatic, (2) asymptomatic with high-risk exposures and (3) asymptomatic populations without preexisting conditions for routine screening to address the needs of an on-site work force. We developed a distinct SARS-CoV-2 diagnostic assay based on the original CDC Diagnostic Panel (EUA200001), yet, with minimum overlap for currently employed reagents to eliminate direct competition for limited resources. As the pandemic progressed with testing loads increasing, we modified the assay to include 5-sample pooling and amplicon target multiplexing. Analytical sensitivity of the pooled and multiplexed assays was rigorously tested with contrived positive samples in realistic patient backgrounds. Assay performance was determined with clinical samples previously assessed with an FDA authorized assay. Throughout the pandemic we successfully tested symptomatic, known contact and travelers within our occupational population with a ~ 24–48-h turnaround time to limit the spread of COVID-19 in the workplace. Our singleplex assay had a detection limit of 31.25 copies per reaction. The three-color multiplexed assay maintained similar sensitivity to the singleplex assay, while tripling the throughput. The pooling assay further increased the throughput to five-fold the singleplex assay, albeit with a subtle loss of sensitivity. We subsequently developed a hybrid ‘multiplex-pooled’ strategy to testing to address the need for both rapid analysis of samples from personnel at high risk of COVID infection and routine screening. Herein, our SARS-CoV-2 assays specifically address the needs of occupational healthcare for both rapid analysis of personnel at high-risk of infection and routine screening that is essential for controlling COVID-19 disease transmission. In addition to SARS-CoV-2 and COVID-19, this work demonstrates successful flexible assays developments and deployments with implications for emerging highly transmissible diseases and future pandemics.
Sequencing and analysis of 131 SARS-CoV-2 isolates in previously sampled and unsampled regions of Jordan from 2020 to 2023
The Hashemite Kingdom of Jordan remains an understudied country for next generation sequencing analysis of SARS-CoV-2 genomes collected during the 2019 pandemic. Here we provide 131 additional reference genomes collected between 2020–2023 from SARS-CoV-2-positive patients across Jordan. Phylogenetic analysis supports existing pandemic narratives of changing clade dominance over time and adds genomes in novel Jordanian locations and timepoints to make Jordan SARS-CoV-2 databases more comprehensive. Samples from the less-sequenced cities of Ajloun, Jaresh, Karak, and Madaba identified previously unreported lineages while Amman, Irbid, and Zarqa have existing sequencing efforts bolstered. Despite many incomplete patient records and a relatively small sample size, we observe interesting symptom patterns that support existing global and Jordanian pandemic narratives. We note how in-country COVID-19 pandemic genomic studies showcase Jordan’s efforts to expand next generation sequencing capabilities, especially through the leveraging of EDGE COVID-19, a bioinformatics platform for performing rapid, batched analysis of SARS-CoV-2 sequencing that streamlines sample processing prepared from a network of hospital locations.
Leveraging Flexible Smart Manufacturing to Accelerate Industrial Supply Chain Recovery
Any major crisis such as the latest coronavirus disease pandemic will have a monumental effect on the worldwide economy and on the international supply chain. Much of the global supply chain relies on China, Germany, and the United States for manufacturing and distribution; this fragile system is subject to failure. Climactic changes in demand and rapid shortages in necessities are the results of the pandemic’s disruption. The recovery forecast for the ongoing pandemic is uncertain and, therefore, any recovery effort will need to be adaptable to build the supply chain resiliency. A wide adoption of flexible smart automated technologies in the manufacturing sector are helping to build robust supply chains and assisting in recovery. Here, a brief discussion of these smart manufacturing technologies is presented, along with a list of potential supply chain issues and corresponding solutions. Such smart manufacturing technologies possess the potential to empower and revolutionize the traditional manufacturing environment by enhancing its resiliency and flexibility. These smart technologies will play a crucial role in accelerating the worldwide supply chain recovery in and after a pandemic.
The molecular basis of the neutralization breadth of the RBD-specific antibody CoV11
SARS-CoV-2, the virus behind the COVID-19 pandemic, has changed over time to the extent that the current virus is substantially different from what originally led to the pandemic in 2019–2020. Viral variants have modified the severity and transmissibility of the disease and continue do so. How much of this change is due to viral fitness versus a response to immune pressure is hard to define. One class of antibodies that continues to afford some level of protection from emerging variants are those that closely overlap the binding site for angiotensin-converting enzyme 2 (ACE2) on the receptor binding domain (RBD). Some members of this class that were identified early in the course of the pandemic arose from the VH 3-53 germline gene (IGHV3-53*01) and had short heavy chain complementarity-determining region 3s (CDR H3s). Here, we describe the molecular basis of the SARS-CoV-2 RBD recognition by the anti-RBD monoclonal antibody CoV11 isolated early in the COVID-19 pandemic and show how its unique mode of binding the RBD determines its neutralization breadth. CoV11 utilizes a heavy chain VH 3-53 and a light chain VK 3-20 germline sequence to bind to the RBD. Two of CoV11’s four heavy chain changes from the VH 3-53 germline sequence, $Thr^{28}_{FWRH1}$ to Ile and $Ser^{31}_{CDRH1}$ to Arg, and some unique features in its CDR H3 increase its affinity to the RBD, while the four light chain changes from the VK 3-20 germline sequence sit outside of the RBD binding site. Antibodies of this type can retain significant affinity and neutralization potency against variants of concern (VOCs) that have diverged significantly from original virus lineage such as the prevalent omicron variant. We also discuss the mechanism by which VH 3-53 encoded antibodies recognize spike antigen and show how minimal changes to their sequence, their choice of light chain, and their mode of binding influence their affinity and impact their neutralization breadth.
Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability
“Stay-at-home” orders and other health precautions enacted during the COVID-19 pandemic have led to substantial changes in residential electricity usage. Here, we conduct a case study to analyze data from 390 apartments in New York City (NYC) to examine the impacts of two key drivers of residential electricity usage: COVID-19 case-loads and the outdoor temperature. We develop a series of regression models to predict two characteristics of residential electricity usage on weekdays: The average occupied apartment’s consumption (kWh) over a 9am-5pm window and the hourly peak demand (Watt) over a 12pm-5pm window. Via a Monte Carlo simulation, we forecast the two usage characteristics under a possible scenario in which stay-at-home orders in NYC, or a similar metropolitan region, coincide with warm summer weather. Under the scenario, the 9am-5pm residential electricity usage on weekdays is predicted to be 15% – 24% higher than under prior, pre-pandemic conditions. This could lead to substantially higher utility costs for residents. Additionally, we predict that the residential hourly peak demand between 12pm and 5pm on weekdays could be 35% – 53% higher than that under pre-pandemic conditions. We conclude that the projected increase in peak demand - which might arise if stay-at-home guidelines coincided with hot weather conditions - could pose grid management challenges, especially for residential feeders. We also note that, if there is a longer lasting shift towards work and study-from-home, utilities will have to rethink load profile considerations. The applications of our predictive models to managing future smart-grid technology are also highlighted.
Potential impact of work from home jobs on residential energy bills: A case study in phoenix, AZ, USA
Nearly one-third of U.S. households face challenges paying energy bills. During the day, many residents have routine access to cooled environments provided by others—employers, shopping centers, and other public buildings. The COVID-19 pandemic, however, has significantly shifted the cost burden of air conditioning in hot cities. Specifically, during the pandemic, many companies either laid off employees or put them in work-from-home (WFH) assignments. Large tech companies are already promoting WFH as a long-term option for their employees, even after the pandemic. This change in the nature of the workforce might reduce daily travel expenses for workers, but could also significantly increase residential energy bills, particularly during summer in very hot climates. Here, this study uses building energy simulations to quantify the potential residential energy bill penalties resulting from WFH for typical residences in Phoenix. Four building archetypes are used in this study to represent variations in building vintage, occupancy, and characteristics. The results show that, for some single-family residences in Phoenix, WFH can increase annual energy bills by more than $1100 (up to a 70% increase). The study also demonstrates that building performance enhancement retrofit measures have the potential to reduce this WFH energy bill penalty for the existing buildings substantially.