COVID-19 has taken a major toll on the tourism industry. Traveler confidence has decreased as travel restrictions and fears regarding the virus have increased. As tourist destinations rely more on local tourism to survive the current crisis, communicating COVID-19 procedures is vital to mitigate tourist ́s safety concerns. Therefore, the objective of this study was to use content analysis to identify COVID-19 policy initiatives highlighted within tourism company websites and to compare regions to identify differences in website communication content. The study analyzed 100 various tourism company websites throughout Sweden. The findings and the supporting literature indicating that the websites should have information available about booking policy, operation limitations, social distance policy, and customer/staff responsibility to properly communicate their COVID-19 policies. Overall, the tourism company websites in this study were lacking in the communication of COVID-19 policies. To ameliorate communication of COVID-19 procedures, this study offers recommendations for tourism firms to incorporate.

COVID-19 continues to have a major impact on US health and society. Robust research on the epidemiology of acute and post-acute COVID-19 remains fundamentally important to informing policy makers, scientists, as well as the public. This dissertation reports on the development of a large, diverse, United States general population-based meta-cohort with standardized, prospective ascertainment of SARS-CoV-2 and COVID-19, integrated with comprehensive pre-pandemic phenotyping from 14 extant cohort studies. Meta-cohort data were used to investigate risk and resilience factors for incident severe (hospitalized or fatal) and non-severe COVID-19 and correlates of time-to-recovery from SARS-CoV-2 infection. Results support the major acute and post-acute public health impact of COVID-19 and the vital role of modifiable (e.g., obesity, diabetes, cardiovascular disease) and non-modifiable (e.g., age, sex) risk factors for adverse COVID-19 outcomes. Findings suggest that standard primary care interventions—including obesity and cardiometabolic disease prevention and treatment, depression care, and vaccination—remain fundamental to COVID-19 risk mitigation among US adults. Given its longitudinal design and comprehensive pre-pandemic and pandemic-era measurements, the meta-cohort is well suited to support ongoing work regarding the public health impact of SARS-CoV-2 infection, COVID-19, post-acute sequelae, and pandemic-related social and behavioral changes across multiple health domains.

Background: The COVID-19 pandemic posed an unparalleled challenge to worldwide public health systems, characterized by its high transmissibility and the initial absence of accessible testing, treatments, and vaccines. The deficiency in public awareness and the scarcity of readily available public health information regarding this century's disaster further intensified the critical need for innovative solutions to bridge these gaps. In response, Shaman Labs1,2, leveraging its deep expertise in forecasting for influenza3, Ebola, and various SARS viruses, initiated the development of country-wide COVID-19 projections within weeks following the WHO's declaration of the pandemic4–6. Almost immediately thereafter, it became necessary to create a sophisticated online platform—a system capable of displaying county-specific COVID-19 forecasts, including daily estimated infections, cases, and deaths. This platform was designed to allow users to select any county, state, or national geography and compare it with another, under various scenarios of social distancing measures. Additionally, the architecture of this system was required to facilitate the regular integration of updated data, ensuring the tool's ongoing relevance and utility. Columbia University's data visualization system aimed to communicate epidemiological forecasts to various stakeholders. At the onset of the COVID-19 pandemic, amid escalating uncertainty and the pressing need for reliable data, Dr. Rundle played a pivotal role in briefing key stakeholders on the unfolding crisis. His efforts were directed towards providing Congressman Ron Johnson, Chairman of the U.S. Senate Committee on Homeland Security & Governmental Affairs, and Congresswoman Anna Eshoo, as well as their staff, with up-to-date projections and analyses derived from the Classic Data Visualization tools. Dr. Rundle’s consultative role extended to a diverse array of institutions including the U.S. Army Corps of Engineers, the U.S. Air Force, and the Federal Reserve Board, as well as advising private entities such as Pfizer, MetLife, and Unilever. His expertise facilitated informed planning and response efforts across various levels of government and sectors, underscoring the critical role of sophisticated data visualization from the earliest stages of the pandemic. This Integrated Learning Experience (ILE) examines the development and implementation of the Time Machine platform, focusing on its application in visualizing and analyzing COVID-19 epidemiological forecasts. The study explores methods for improving forecast data presentation, analysis, and accuracy assessment. Methods: The body of this work unfolds through a series of critical chapters that collectively address the multifaceted functionality and impact of the Time Machine platform. Initially, the work focuses on the construction of the Time Machine platform, a web-based R interactive user interface coupled with cloud-based database system, specifically tailored for the intuitive visualization of epidemiological forecasts, detailing the technical and design considerations essential for enabling users to interpret complex data more effectively. Following this, the implementation of a rigorous data-discovery framework is presented, examining case reporting inconsistencies across different regions, using low-level GitHub and Windows scripting technologies, thereby highlighting the significance of accurate data collection and the impact of discrepancies on public health decisions. The narrative then transitions to the implementation of advanced statistical models, such as strictly proper scoring and weighted interval scoring, to assess the accuracy of the forecasts provided by the Time Machine platform, using a dedicated R library and testing with the help of MS Excel sandbox, underscoring the importance of reliable predictions in the management of public health crises. Lastly, a detailed analysis is conducted, encompassing countrywide data (3142 counties) over an extended period (147 weeks), utilizing Generalized Estimating Equations (GEE) to identify key predictors that influence forecast accuracy, offering valuable insights into the factors that either enhance or detract from the reliability of epidemiological predictions. Results: The deployment of the Classic Data Visualization and the subsequent evolution of the Time Machine platform have significantly advanced epidemiological forecast visualization capabilities. The Time Machine platform was designed with an automated data refresh system, allowing for regular updates of epidemiological forecast data and reported actuals. The project developed tools for monitoring and evaluating the quality of public health reporting, aiming to improve the accuracy and timeliness of data used in public health decisions. Additionally, the research implemented methods for standardizing forecast accuracy assessments, including the normalization of scores to enable comparisons across different geographical scales. These approaches were designed to support both local and national-level pandemic response efforts. The accuracy analyses throughout different phases of the pandemic revealed a 42% improvement in forecast accuracy from Phase 1 to Phase 7. Larger populations (27% increase per unit increase on a base-10 logarithmic scale) and higher county-level activity (45% increase from the lowest to the highest quartile) resulted in better estimations. Additionally, the analysis highlighted the significant impact of reporting quality on forecast accuracy. On the other hand, the study identified the challenges in predicting case surges, showing a 27% decline in accuracy during periods of rising infections compared to declining periods. The regression results highlight the potential benefits of improving data collection and providing timely feedback to forecasting teams. Conclusion: This study demonstrates the potential of advanced data visualization and accuracy measurement techniques in improving epidemiological forecasting. The findings suggest that factors such as urbanicity, case reporting quality, and pandemic phase significantly influence forecast accuracy. Further research is needed to refine these models and enhance their applicability across various public health scenarios.

Background: The COVID-19 pandemic has had an impact on nearly all people. Vaccines provide an effective tool to combat the pandemic, however, vaccination hesitancy remains an issue. This study aims to investigate (a) students' attitudes toward the pandemic, (b) potential differences in attitudes between university students and the general population, and (c) to examine predictors of vaccination intention in both samples. Methods: In this cross-sectional study data from two research projects were analyzed and compared. First, attitudes toward the COVID-19 pandemic in German university students were assessed within a cross-sectional anonymous online survey (March-April 2021, N = 5,639) and analyzed quantitatively and also qualitatively (free text field answers examined positive and negative aspects of the pandemic). Second, data from a cross-sectional survey within the COVID-19 Snapshot Monitoring project (COSMO; 29th wave of data collection, December 2020, N = 1,387) in the German general population was analyzed. Both samples, were compared in sharedly used variables, regarding attitudes toward the pandemic and vaccination intention, and factors associated with vaccination (logistic regression analyses). Results: In comparison to the general population, university students were significantly more likely to report being worried about/thinking about the coronavirus and to perceive the coronavirus as overrepresented in the media (all p < 0.001). University students reported a more supportive attitude toward vaccinations in general (students: M = 4.57, SD = 0.85; general population: M = 3.92, SD = 1.27) and a significantly higher vaccination intention (students: n = 4,438, 78.7%; general population: n = 635, 47.7%) than the general population (p < 0.001). Regression analyses revealed that in university students, vaccination intention was significantly predicted by not having children, a supporting attitude toward vaccinations in general, the belief that the coronavirus is overrepresented in the media, and less thinking about/worrying about the coronavirus (all p < 0.05). In the general population, vaccination intention was significantly associated with male gender, higher age, not having children, a supporting attitude toward vaccinations in general, and the belief that the coronavirus is overrepresented in the media (p < 0.05). The qualitative analysis among university students revealed that the most frequently stated positive aspect of the pandemic was to be more flexible due to digitalization (n = 1,301 statements, 22.2%) and the most frequently stated negative aspect was restriction in social life (n = 3,572 statements, 24.2%). Conclusion: The results indicate differences in the attitudes toward the pandemic between university students and the general population. In addition, differences regarding factors associated with vaccination intention were found in both samples. These results could be important to be considered when designing and targeting vaccination campaigns aiming at informing different population or age groups.

Small molecules are powerful tools to probe biological systems and cure diseases. In the scope of this dissertation, small molecules were applied to study three distinct disease models: cancer, Sedaghatian-type spondylometaphyseal dysplasia (SSMD), and COVID-19. First, encouraged by the recently reported vulnerability of drug-resistant, metastatic cancers to GPX4 (Glutathione Peroxidase 4) inhibition, we examined the basis for nanomolar potency of proof-of-concept GPX4 inhibitors, which revealed an unexpected allosteric binding site. Through hierarchical screening of a lead-optimized compound library, we identified novel small molecules binding to this allosteric site. Second, a homozygous point mutation in the GPX4 gene was identified in three living patients with SSMD. With a structure-based analysis and cell models of the patient-derived variant, we found that the missense variant significantly changed the protein structure and caused substantial loss of enzymatic function. Proposed proof-of-concept treatments were subsequentially validated in patient fibroblasts. Our further structural investigation into the origin of the reduced enzymatic activity revealed a key residue modulating GPX4 enzymatic function. We also found that the variant alters the degradation of GPX4, unveiling the native degradation mechanism of GPX4 protein. Third, driven by the recent urgent need for COVID-19 antiviral therapeutics, we utilized the conservation of 3CL protease substrate-binding pockets across coronaviruses to identify four structurally divergent lead compounds that inhibit SARS-CoV-2 3CL protease. With structure-based optimization, we ultimately identified drug-like compounds with < 10 nM potency for inhibiting the SARS-CoV-2 3CL protease and blocking SARS-CoV-2 replication in human cells.

This study investigates the resilience of coffee cooperatives and producer organizations in the context of the COVID-19 pandemic and explores their adaptations to the context in relation to their livelihood capitals. The changes to their operations are analyzed through the contexts of shocks, trends and stresses and how they perceived these threats. The topic of research is relevant given the economic and social importance of cooperatives in these communities and potential impacts to their operations during COVID-19, which is likely to have long-term impacts locally and within the global setting.There is a lack of consensus regarding the classification of cooperatives as resilient organizations, with much of the previous research focusing on financial crisis or natural disasters. Furthermore, the COVID-19 pandemic has been an unprecedented event on a global scale with far-reaching impacts into social, economic and political spheres, and examining these effects is still a developing realm within academic research. The relationship of coffee producers and their organizations within the global commodity chains renders such organizations particularly vulnerable to the effects of COVID-19 and government policy interventions. Investigating how coffee cooperatives in Honduras have been operating throughout the COVID-19 pandemic assesses their potential capacity for resilience by examining how they have been impacted and the manners in which they have overcome these challenges. This further allows for increased understanding of cooperative resilience and ways in which cooperatives’ capital have the potential to impact their resilience.This research follows an abductive qualitative case study and utilizes semi-structured interviews from various coffee cooperatives and organizations in Honduras as primary sources with existing literature as secondary sources. The interviews were conducted remotely. The findings include accounts from cooperatives and producer organizations, which focus primarily on coffee production, in addition to reports from a privately owned coffee production enterprise and a cooperative member. The Vulnerability Context and Asset Pentagon, components of the Sustainable Rural Livelihoods Framework as described by the Department for International Development, were used to analyze the data, along with variables to assess organizational resilience. The study finds that investments to organizations’ human and social capital were prioritized and heavily relied upon during this crisis and the more established organizations had a larger range of resources from which to draw upon. Nevertheless, by continuing to develop and expand on human and social capital, cooperative organizations can increase their capacity for resilience.

Conversatorio de estudiantes, docentes y egresados de la Carrera de Comunicación e Imagen Empresarial de la UPC como parte del #ImagenWiiik: Inspira, Innova, Imagina.

Boletín que incluye información científica sobre el COVID-19, incluye artículos científicos y artículos preprint actualizados al 27 de Mayo de 2020.

This thesis explores boosting to improve decision-making in the context of the COVID-19 pandemic using a structured introspection. Structured introspection is an intervention where individuals are prompted with and are asked to estimate the importance of a set of attributes relevant to the decision in order to limit the prevalence of potential cognitive biases. To test the intervention, 281 participants divided into an intervention and control group answered an online survey with a dilemma about COVID-19. The dilemma was whether Sweden should shut down the economy or keep it open during the COVID-19 pandemic. The intervention group was asked to rate how important the attributes “saving lives”, “saving the economy”, “concern for the health of the elderly and risk groups”, and “concern for the quality of life and well-being of all citizens” should be for their decision. The control group was only prompted with the question and asked to think carefully. All participants were asked a set of control variables such as risk perception for self and others and emotions when thinking about COVID-19. The results did not show a significant influence on choice on decisions based on the intervention. They did however show a significant correlation with choice on risk perception as well as a correlation between choice on the dependent variable and the attributes in the intervention group.             The conclusion of the thesis is that structured introspection may not be suitable on a contemporary issue affecting participants directly, as they may already have strong opinions about the issue. Further and broader research needs to be conducted to determine in which circumstances this boost can be effective.

Covid-19 has had a major impact on the spring of 2020. Many countries around the world are fighting to suppress the spread of the virus through various means. One major way is to restrict people form traveling and meeting. This has had a large impact on certain aspects of society. One of those is the closure of events big and small. Every big event in Sweden has been closed and almost every small event also. This has a big impact on business that depend on events to stay successful. This thesis explores the effect the closure of all events, big and small, has on Wargamingshops that usually organise many small events every week. The thesis also seeks to catalogue the geographical spread different kinds of Wargaming shops have in Sweden and to understand the reasoning behind the owner’s choice of location. The study finds that Wargamingshops currently have not felt a major economic impact from the Covid-19 situation and that the shops are evenly distributed in Sweden’s cities and towns except for a few clusters, mainly in the south part, Skåne. The study also finds an interesting connection between the shop owners and customers wanting to support the shops during hard times.