Viral pathogens have plagued human civilizations since ancient times and continue to pose a serious and constant global threat to not only human health but all facets of life. To date, more than 200 viruses capable of infecting humans have been identified, and the combined efforts of the academic and pharmaceutical sectors have yielded both extensive understanding of the biology and pathology of the viral infections as well as breakthrough interventions against a number of devastating diseases such as those caused by HIV (human immunodeficiency virus) and HCV (hepatitis C virus). In late 2019, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the etiological agent of COVID-19 (coronavirus disease 2019), rapidly spread worldwide, leading to detrimental public health and socioeconomic crises. While the immediate response of the scientific community to the pandemic, which involved investigation of the disease and discovery of several therapeutic options at an unprecedented pace, has been impressive, this recent experience exposes the serious need to continuously fortify our fundamental knowledge of virology and equip our antiviral arsenal in preparation for future outbreaks. Moreover, given the scale of the challenge at hand, it highlights the value in the development and application of experimental approaches that accelerate the rate at which this information is obtained. In this dissertation, we utilize various techniques that allow high-throughput analysis of the SARS-CoV-2 3CL (3-chymotrypsin-like) protease to better understand its functional landscape as a favorable therapeutic target of the virus, and to investigate its response in developing resistance against the clinically used protease inhibitor, nirmatrelvir, at scale. We further expand our efforts to develop a platform for multiplexed drug screening that has the capacity to detect viral protease inhibitors for not only coronaviruses but also other targets across six additional virus families. Using this approach, we are able to rapidly identify broad-acting inhibitors, which are favorable for pandemic preparedness purposes where the exact nature of the future threat is difficult to predict a priori. To perform our studies, we make use of a variety of model systems, from a simple yeast-based system for detecting viral protease activity to the passaging of live virus within cultured human cells. Utilizing our yeast-based reporters, we comprehensively profile the activity landscape of all possible single mutants of the SARS-CoV-2 3CL protease via deep mutational scanning (DMS), uncovering its general malleability while also identifying several immutable regions within the enzyme that can serve as targets for the design of the next generation of protease inhibitors. Among the sites that show tolerance to changes, we predict E166 to be a residue that may confer nirmatrelvir resistance upon mutation based on available structural data which reveal its critical role in the binding of the drug to the active site. We prove this to be true by demonstrating a 265-fold loss in EC50 for the E166V mutant relative to the wild type protease within the recombinant virus. Recognizing that the plasticity of the enzyme could translate to a lower genetic barrier to resistance, we extend our investigation to study the whole virus response to nirmatrelvir at scale via in vitro passaging of SARS-CoV-2 in increasing concentrations of the drug. Upon examining 53 independent viral lineages to explore the ways by which resistance can be acquired, we identify a total of 23 mutations that arise in often non-overlapping combinations, with T21I, P252L, and T304I being the most common precursor mutations within all analyzed mutational trajectories. Validation of select single, double, and triple mutants based on the frequency of their appearance reveals that most single mutations, including the aforementioned founder mutations, confer low-level resistance (~5 – 6 fold) while greater resistance is acquired with the accumulation of additional mutations. Moreover, some mutations, such as T21I and L50F, appear to mediate, through a compensatory mechanism, the acquisition of secondary mutations such as E166V, which alone may confer much greater resistance but also cause significant loss in replicative fitness. Overall, the myriad of solutions that exist for the virus to escape the drug further corroborate the malleability of the SARS-CoV-2 3CL protease as established by our initial DMS study. These findings also establish a foundation for extended analysis of the mechanism of resistance and informed drug design. Lastly, by introducing additional viral proteases into our yeast cellular chassis and labelling each model with a set of unique DNA-barcodes, we develop a method of screening a pool of 40 unique protease targets simultaneously against small molecule libraries. Using this platform, we screen 2,480 structurally diverse compounds, and identify and orthogonally validate a series of broad-acting coronavirus 3CL protease inhibitors with a chromen-2-one structure. Together, the work described in this thesis underline the importance of innovative high-throughput approaches to investigating biology as demonstrated by their application to viral protease research.

The long-term care (LTC) sector in Canada has experienced high numbers of COVID-19 deaths. However, there is a paucity of data on the impact of COVID-19 in LTC by different socio- demographic variables and in LTC homes within different regions. Additionally, the question remains as to how exactly and by how much the pandemic has impacted mortality in LTC in comparison to previous years’ mortality. Ranges for expected mortality by sex, province, and age, for the 2020-21 fiscal year were determined by creating forecasts and confidence intervals based on mortality trends in the preceding four fiscal years. These ranges were then compared to the actual mortality data in 2020-21. Comparisons between expected ranges and actual data were also conducted for the number of active residents, admissions, and discharges in LTC by sex, province, and age. Further, mortality ratios were created and studied by sex, province, age, and health region/authority/local health integration network. Overall, the number of deaths in LTC in Canada increased beyond the expected ranges in quarter one and three of 2020-21, and the patterns in death ratios were similar. Increases were exceptional in comparison to the peaks in deaths in previous years for specific variables, but not all variables. Most commonly, the number of active residents and admissions decreased in 2020-21 and the number of discharges from LTC did not change in quarter one and three and decreased in quarter two and four. However, importantly, these trends also varied across variables. This was the first study to comprehensively examine mortality due to COVID-19 in LTC overall, and by multiple socio- demographic variables while elucidating the complexity in the study of mortality in LTC. Further research is required to concretely understand mortality in LTC by different variables and regions. / Thesis / Master of Arts (MA) / This study examined mortality due to COVID-19 from April 2020 to March 2021 in Canadian long-term care (LTC) homes by sex, age, province, and health region. Ranges of predicted values for mortality were created from mortality data from previous years and then compared with actual mortality. The number of active residents, admissions, and discharges were also examined by sex, age, and province to factor for changes in the population at risk. Overall, mortality increased in some quarters (April-June 2020 and October-December 2020) but was not always exceptional, as similar mortality rates had been observed in the four years prior to the pandemic. Also, the increase in mortality was seen mostly among younger residents (65 to 85); mortality remaining stable for the 85+. Further research is still required to better understand mortality in LTC by regional characteristics.

This dissertation is a tale of two emerging human pathogens. The first is a genus of viruses, orthoebolaviruses, which periodically cause outbreaks in humans in central and western Africa following spillover from animal reservoirs. Outbreaks of orthoebolaviruses have high rates of morbidity and mortality and can cause symptoms ranging from vomiting and diarrhea to hemorrhage. Understanding both how the virus evolves to fit its host as well as how the host reacts to viral infection is paramount to understanding what determines whether an infected patient will die or survive orthoebolavirus infection. To understand how orthoebolavirus genomic plasticity allows the virus to optimize itself to its host, I analyzed viral genomic sequencing data from two Orthoebolavirus species during serial passage in tissue culture: Ebola virus and Sudan virus. In low-passage Sudan virus, I discovered a true viral quasispecies in which three to four viral genotypes circulated within the same stock. I then examined how that quasispecies reacted when put into a nonhuman primate model (NHP) of infection; unexpectedly, we saw that the mix of genotypes in the challenge stock matched the mix of genotypes seen at clinical endpoint. To begin to understand what a successful immune response to orthoebolavirus infection entails, I characterized the circulating transcriptomic response in two survival models of Ebola virus disease. In a uniform survival model where NHPs were challenged with Bombali virus, I showed that NHPs have a clear and robust response to infection despite varying symptom severity. In a Taï Forest virus challenge model with ~44% survival, I showed that NHPs that succumb do so in a uniform manner consistent with other models of Ebola virus disease. In contrast, survivors were highly variable in their response to infection: some mimicked the non-survivor response but recovered in time, while others hardly responded at all. After covering orthoebolavirus genomic plasticity and the host response to infection in the first and second sections, respectively, I will then shift to the other focus of my dissertation work: SARS-CoV-2 and molecular epidemiology. SARS-CoV-2 swept the globe in 2020 following spillover into humans from an animal reservoir in late 2019, and surveillance sequencing of viral genomes early in the pandemic showed the virus was rapidly adapting to its new host. I leveraged this high mutation rate to spin up a molecular epidemiology operation for Boston Medical Center (BMC) and Boston University (BU). From mid-2020 through spring 2022, I catalogued, processed, sequenced, and analyzed samples and viral genomes from over 7,000 SARS-CoV-2 patient swabs. I worked with contact tracing teams, physicians, and infection control from BU and BMC to quantify viral introductions, identify transmission chains, and integrate the genetic linkages with traditional epidemiological data. / 2025-01-11T00:00:00Z

Introduktion: Genom att ett virus (Covid-19) upptäcktes i Kina i slutet av 2019 som senare ledde till en pandemi, blev den fysiska aktiviteten inte bätt- re. Restriktioner kom efter varandra, äldre människor rekommenderades att inte vistas bland andra människor, skolor fick stänga ner och många idrotter fick inte utföras vilket ledde till att den fysiska aktiviteten minskades väldigt mycket. Syfte: Syftet med arbetet var att ta reda på om/hur träningen för 10-15 åring- ar har förändrats i en lokal fotbollsklubb på grund av pandemin ur ett tränar- perspektiv. Även hur tränarna jobbat för att bibehålla det goda träningsinne- hållet. Metod: I denna studie har en kvalitativ forskningsmetod genomförts i form av en enkät. Enkätfrågorna var av formen öppna frågor och analyserades med hjälp av innehållsanalys genom att dra slutsatser och genom tolkning av sva- ren från deltagarna. Frågorna skickades ut till 17st deltagare varav 15st av dessa valde att vara med i studien och inkom med svar på de utskickade frå- gorna. Resultat: Det visar sig att grupperna inte påverkas så mycket av pandemin. Tränarna har inte märkt någon märkbar förändring vad gäller antal deltagare i träningsgruppen om de jämför med deras grupp nu och innan pandemin. Trä- ningsmängden har snarare ökat istället för att minska för att inomhusträningar fick stänga ner men gjordes om till fler utomhusträningar. Slutsats: Av de svar som kom in från deltagarna som var med i studien visa- de det sig att i den lokala fotbollsklubben som studien är analyserad kring har Covid-19-pandemin inte påverkat barn och ungdomar vad gäller träning av fotboll. Restriktioner genomfördes för träningsgrupperna men påverkade inte grupperna i så stor utsträckning att de blev påverkade negativt. Nyckelord: Covid-19, deltagare, pandemi, träning, träningsgrupp

Die Evaluation des psychischen Belastungserlebens bei Intensivpersonal zeigte während der ersten Covid-19 Welle eine stabile psychosoziale Situation mit Angst um Angehörige und soziale Isolierung als Hauptbelastungsfaktoren. Weiterhin konnten bestimmte vulnerable Gruppen identifiziert werden. / The evaluation of the psychological stress experienced by intensive care staff during the first Covid-19 wave showed a stable psychosocial situation with fear for relatives and social isolation as the main stress factors. Furthermore, certain vulnerable groups were identified

how does it work to lead employees through the pixels in the computer screen? Remote work's impact on managers and its leadership, with focus on the time after the Covid-19 pandemic.   Today's society is becoming more digitized, and the growth has a constant impact on the corporate world. As a result of the Covid-19 pandemic, the rise of digitization was forced, and companies were faced with a change from the physical work environment to the remote work environment. Remote work and remote leadership therefore became more common in the Swedish labor market.   The aim of the study is to gain a deeper understanding of how leadership has been affected by remote work, with a focus on the time after the Covid-19 pandemic. This will be investigated by obtaining knowledge from previous research. Further by examining the experiences of managers and employees. The study does not seek employees' experiences regarding remote work but aims to acquire information about the impact of remote leadership, from an additional dimension.   The theory chapter highlights what knowledge has previously been obtained in the subject. The theory is divided into three primary components based on the three research questions that the study addresses. Next, the method chapter describes the various methods and approaches the study has applied. The method applied in the essay is a qualitative approach with interview as method choice.   This study has gained a deeper understanding of remote leadership by studying the phenomenon both theoretically and empirically. The three given components are intertwined and depending on how each part is handled it affects the outcome of an effective remote manager. Overall, the study shows that a successful remote manager must be committed to both the development and the employees. Another success factor for a remote manager is to use the trust that the manager creates towards the work team. According to the study, continuous varied communication must be applied where the remote manager makes use of the opportunity of new emerging digital media.   The study indicates that companies should consider when evaluating remote work the business financial advantages that can be capitalized on. They should also pay further attention to the challenges that may arise; however, these challenges should not alarm managers, but rather be seen as a motivation.

The need for delivery grew significantly during the COVID-19 pandemic because people avoided activities in public to limit the spread of the virus. The purpose of this research was to evaluate how the pandemic influenced many individual's delivery preferences through the administration of a stated preference survey targeted at residents in the New River Valley, Virginia. Conclusions revealed from the survey show that people want more efficient and accessible delivery services. A new delivery ecosystem called Delivery as a Service (DaaS) was developed using the input from the survey, existing service-based models being widely implemented in many industries, and emerging technologies. This thesis details a framework for DaaS derived by defining major actors, characteristics, and a method to measure the effectiveness of a DaaS system. This comprehensive definition of integrated delivery services illustrates areas for future research to further optimize the DaaS system. DaaS has the potential to significantly change the current delivery ecosystem through increased delivery accessibility and efficiency. Goods can be brought to users at a faster rate and on a larger scale. Autonomous vehicle and drone delivery technologies can significantly reduce the cost while correspondingly reducing the time of delivery. DaaS is a concept that is needed for people to thrive in modern times and brings the opportunity to provide added benefits to even rural areas. / Master of Science / The need for delivery grew significantly during the COVID-19 pandemic because people avoided activities in public to limit the spread of the virus. The purpose of this research was to evaluate how the pandemic influenced many individual's delivery preferences through the administration of a stated preference survey targeted at residents in the New River Valley, Virginia. Conclusions revealed from the survey show that people want more efficient and accessible delivery services. A new delivery ecosystem called Delivery as a Service (DaaS) was developed using the input from the survey, existing service-based models being widely implemented in many industries, and emerging technologies. This thesis details a framework for DaaS derived by defining major actors, characteristics, and a method to measure the effectiveness of a DaaS system. This comprehensive definition of integrated delivery services illustrates areas for future research to further optimize the DaaS system. DaaS has the potential to significantly change the current delivery ecosystem through increased delivery accessibility and efficiency. Goods can be brought to users at a faster rate and on a larger scale. Autonomous vehicle and drone delivery technologies can significantly reduce the cost while correspondingly reducing the time of delivery. DaaS is a concept that is needed for people to thrive in modern times and brings the opportunity to provide added benefits to even rural areas.

The COVID-19 pandemic and subsequent shutdown and regulations have drastically altered the world of competitive sports. The global shutdown beginning in March 2020 put a significant strain on athlete's ability to train, as many fitness centers were closed to prevent disease transmission. When it was deemed that athletic competition was safe to resume, there were still strict regulations in place to support public health efforts. This retrospective study primarily aims to evaluate the effectiveness of COVID-19 safety regulations in competitive sports. Specifically, the successfulness to correctly implement social distancing guidelines is of high interest. A secondary aim of this study is to assess changes in workload during preseason training before COVID-19, during the time of heavily enforced COVID-19 regulations, and following strict COVID-19 restrictions, as workload can be a predictor of athletic injury. Participants in this study included Virginia Tech Women's Soccer athletes and data were analyzed from the first 9 preseason training sessions during the 2019, 2020, 2021 and 2022 seasons. Data were generated from participants wearing the STATSports Apex device during training. A custom MATLAB spatiotemporal program developed by the Williams Research Group was utilized to determine player proximity. Total distance (m) and high metabolic load (HMLD) (au), and high-speed distance (HSD) (m) metrics were analyzed to understand changes in participant workload. It was found that overall the Virginia Tech Women's Soccer Team's implementation of the guidelines was effective, as there were no invasion violations during the 2020 preseason sample. / Master of Science / The COVID-19 pandemic has forever changed the world as we know it. Competitive sports are no exception. The worldwide shutdown as a result of COVID-19 made it difficult for athletes to train while in isolation, as most facilities were closed to prevent disease transmission. When sports were able to continue again, there were still barriers preventing normal practices and competition. Athletes were asked to make every effort to maintain social distancing, even during training sessions. The main purpose of this study is to evaluate how well NCAA Division 1 Women's Collegiate Soccer players maintained social distancing during preseason practices. A secondary goal of this study is to uncover changes in workload from before COVID-19 (2019), during strict COVID-19 regulations (2020), and when COVID-19 restrictions had been less enforced (2021 and 2022). The reasoning for this is that how much work an athlete does can be indicative of risk for injury. If an athlete does significantly more work than usual, there is a higher risk of injury. This study will use global positioning systems (GPS) and measures that reflect workload collected from Virginia Tech Women's Soccer players. It was found that the players remained farther apart during the 2020 season due to the emphasis on social distancing, and that the workload will show a gradual increase to prevent injury. Overall, the study found that the COVID-19 regulations were effectively implemented among the Virginia Tech Women's Soccer Team in the 2020 preseason when restrictions were the highest.

When the novel coronavirus (COVID-19) outbreak began to ring alarm bells worldwide, rapid, efficient diagnosis was critical to the emergency response. The limited ability of medical systems and the increasing number of daily cases pushed researchers to investigate automated models. The use of deep neural networks to help doctors make the correct diagnosis has dramatically reduced the pressure on the healthcare system. Promoting the improvement of diagnosis networks depends not only on the network structure design but also on the activation function performance. To identify an optimal activation function, this study investigates the correlation between the activation function selection and image classification performance in balanced or imbalanced datasets. Our analysis evaluates various network architectures for both commonly used and novel datasets and presents a comprehensive analysis of ten widely used activation functions. The experimental results show that the swish and softplus functions enhance the classification ability of state-of-the-art networks. Finally, this thesis distinguishes the neural networks using ten activation functions, analyzes their pros and cons, and puts forward detailed suggestions on choosing appropriate activation functions in future work. / Master of Science / When the novel coronavirus (COVID-19) outbreak began to ring alarm bells worldwide, the rapid, efficient diagnosis was critical to the emergency response. The manual diagnosis of chest X-rays by radiologists is time and cost-consuming. Compared with traditional diagnostic technology, the artificial intelligence medical system can simultaneously analyze and diagnose hundreds of medical images and speedily obtain high precision and high-efficiency returns. As we all know, machines are brilliant in learning new things and never sleep. Suppose machines can be used to replace human beings in some positions. In that case, it can significantly relieve the pressure on the medical system and buy time for medical practitioners to concentrate more on the research of new technologies. We need to know that the critical decision unit of the intelligent diagnosis system is the activation function. Therefore, this work provides an in-depth evaluation and comparison of the traditional and widely used activation functions with the emerging activation functions, which helps to improve the accuracy of the most advanced diagnostic model on the COVID-19 image dataset. Besides, the results of this study also summarize the cons and pros of using various neural functions and provide many suggestions for future work.

The influence of the COVID-19 pandemic on unemployment rates in several US locations is examined in this research article. We investigate the intricate relationships between COVID-19 cases, mortality, GDP per capita, and unemployment rates through a thorough study of the data. The study sheds insight into the complex interaction between health crises and labor markets by revealing considerable differences in how these factors affect unemployment in various geographic locations.Unexpectedly, key findings show that places with higher COVID-19 cases frequently have lower unemployment rates. This trend is related to several variables, including critical sectors and public health activities. The positive correlation between increased COVID-19 deaths and increased unemployment highlights the significant economic impact of the COVID-19 pandemic. The findings suggest that the relationship between GDP per capita and unemployment rates during the COVID-19 pandemic varied across different regions of the United States. These findings have broad ramifications, highlighting the connection between the economy and public health. Policymakers are urged to consider regional differences when creating focused measures to solve problems with the job market brought on by the pandemic. This study advances the knowledge of the COVID-19 pandemic's impact on the labor market. It emphasizes the value of concerted actions to save people's lives and way of life in times of crisis.