A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Second Wave Mechanics(2024) Fabbri, Anthony; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The COVID-19 pandemic experienced very well-documented "waves" of the virus's progression, which can be analyzed to predict future wave behavior. This thesis describes a data analysis algorithm for analyzing pandemic behavior and other, similar problems. This involves splitting the linear and sinusoidal elements of a pandemic in order to predict the behavior of future "waves" of infection from previous "waves" of infection, creating a very long-term prediction of a pandemic. Common wave shape patterns can also be identified, to predict the pattern of mutations that have recently occurred, but have not become popularly known as yet, to predict the remaining future outcome of the wave. By only considering the patterns in the data that could possibly have acted in tandem to generate the observed results, many false patterns can be eliminated, and, therefore, hidden variables can be estimated to a very high degree of probability. Similar mathematical relationships can reveal hidden variables in other underlying differential equations.Item EXPLORING TEMPORAL AND SPATIAL VARYING IMPACTS ON COMMUTE TRIP CHANGE DUE TO COVID-19(2023) Saleh Namadi, Saeed; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)COVID-19 has deeply affected people’s daily life and travel behaviors. This study uses large-scale mobile device location data at the U.S. county level in the DMV area to reveal the impacts of demographic and socioeconomic variables on commute trip change. The study investigates the contribution of these variables to the temporal and spatial varying impacts on commuter trips. It reflects the short and long-term impact of COVID-19 on travel behavior via linear regression and geographically weighted regression models. The results indicate that commute trips decreased with more white-collar jobs, while blue-collar sectors demonstrated the opposite effect. Unexpectedly, elderly individuals, who were highly vulnerable to COVID-19, negatively correlated with decreased commute trips. Moreover, in the DMV area, counties with a higher proportion of Democratic voters also showed a negative correlation with reduced commute trips. Notably, the pandemic's impact on commuting behaviors was global at the onset of COVID-19. Still, the effects exhibited local correlations as the pandemic evolved, suggesting a geographical impact pattern.Item Sample-to-Answer Immuno-Magnetic Assay Using Thermally Responsive Alkane Partitions(MDPI, 2022-11-17) Everitt, Micaela L.; Boegner, David J.; White, Ian M.To combat pandemics, there is a need for rapid point-of-care diagnostics to identify infected patients and to track the spread of the disease. While recent progress has been made in response to COVID-19, there continues to be a need for point-of-care diagnostics capable of detecting biomarkers—such as antibodies—in whole blood. We have recently reported the development of thermally responsive alkane partitions (TRAPs) for the automation of point-of-care immuno-magnetic assays. Here, we demonstrate the use of TRAPs to enable sample-to-answer detection of antibodies against the SARS-CoV-2 virus in whole blood samples. We report a limit of detection of 84 pg/mL, well below the clinically relevant threshold. We anticipate that the TRAP-enabled sample-to-answer immunoassay can be used to track the progression of future pandemics, leading to a more informed and robust clinical and societal response.Item Facility design and worker justice: COVID-19 transmission in meatpacking plants(Wiley, 2023-06-17) Lou, Jiehong; Borjigin, Sachraa; Tang, Connie; Saadat, Yalda; Hu, Ming; Niemeier, Deb A.Background Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output. Methods Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions. Results Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space. Conclusion Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.Item Implementing wastewater surveillance for SARS-CoV-2 on a university campus: Lessons learned(Wiley, 2022-10-21) Wartell, Brian A.; Proano, Camila; Bakalian, Lena; Kaya, Devrim; Croft, Kristen; McCreary, Michael; Lichtenstein, Naomi; Miske, Victoria; Arcellana, Patricia; Boyer, Jessica; Van Benschoten, Isabelle; Anderson, Marya; Crabb, Andrea; Gilson, Susan; Gourley, Anthony; Wheeler, Tim; Trest, Brian; Bowman, Glynnis; Kjellerup, Birthe V.Wastewater surveillance, also known as wastewater-based epidemiology (WBE), has been successfully used to detect SARS-CoV-2 and other viruses in sewage in many locations in the United States and globally. This includes implementation of the surveillance on college and university campuses. A two-phase study was conducted during the 2020–2021 academic year to test the feasibility of a WBE system on campus and to supplement the clinical COVID-19 testing performed for the student, staff, and faculty body. The primary objective during the Fall 2020 semester was to monitor a large portion of the on-campus population and to obtain an understanding of the spreading of the SARS-CoV-2 virus. The Spring 2021 objective was focused on selected residence halls and groups of residents on campus, as this was more efficient and relevant for an effective follow-up response. Logistical problems and planning oversights initially occurred but were corrected with improved communication and experience. Many lessons were learned, including effective mapping, site planning, communication, personnel organization, and equipment management, and obtained along the way, thereby paving an opportune guide for future planning efforts.Item REDUCING CONGESTION POST-COVID-19 THROUGH TELECOMMUTING AND HOV LANES(2021) Ugwu, Nneoma Maxine; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The historic low traffic during the COVID-19 pandemic reignited interest in telecommuting as a low-cost effective Travel Demand Management (TDM) strategy. Telecommuting, introduced as a TDM in 1970, has been studied extensively but there has never been an opportunity of this magnitude to investigate its potential. As the percentage of teleworkers increased from five percent to over 50 percent in 2020, commuter traffic in the D.C.-Maryland-Virginia region was almost non-existent. We argue that increased telecommuting played a significant role in the traffic reduction during the pandemic, and that continued sustainable and equitable telecommuting coupled with implementing more High Occupancy Vehicle (HOV) lanes could significantly remove traffic bottlenecks. This study uses mobility data from the University of Maryland COVID-19 platform and traffic data from the Maryland Department of Transportation to specify a regression model that estimates roadway performance in hypothetical telecommuting and HOV scenarios. The investigation showed that the reduced work-related trips were a major cause of the congestion reduction in 2020. With only 20 percent more of the population telecommuting than in 2019, there was a significant improvement in roadway congestion on almost all major roadways. We propose two low-cost sustainable transportation strategies to maintain the reduced congestion post-COVID-19: promoting telecommuting and implementing HOV lanes. Policies through which the government and employers can support telecommuting are also recommended.