In late 2019, scientists discovered a novel strain of coronavirus originating out of Wuhan, China. By January 2020, the United States (U.S.) saw its first positive case of coronavirus and the public was still unsure how serious of an epidemic this would be. By the time the World Health Organization declared the novel coronavirus disease (COVID-19) epidemic a “global pandemic” on March 11, 2020, world leaders, public health experts, scientists, and public administrators kicked into high gear to start serious pandemic response efforts. The public sector is typically the responsible authority for the preparedness, response, mitigation, and recovery of such a public health crisis. The purpose of this paper is to build on French’s (2011) study on the characterization of pandemic planning preparedness of the 50 most populous U.S. cities to assess the impacts of a real-time pandemic, COVID-19, upon the public health outcomes of these same cities. While certain public health data (e.g., disease-specific mortality data) are typically indicators of the impacts of diseases, this study acknowledges and cautions that taking these indicators into consideration is not a direct reflection of any one government’s performance in the face of disaster. A plethora of factors (e.g., social, economic, geographic, environmental, biological) and choices at the societal, community, and individual levels can impact the health outcomes of those populations. The COVID-19 mortality data show that many of the U.S. cities with large populations were hit hard and fast during the beginning of the pandemic. It was anticipated that as a city’s preparedness level increased, its COVID-19 mortality rate would decrease. However, results indicated that cities with the highest level of preparedness actually experienced higher COVID-19 death rates than their less prepared counterparts. This analysis aims to shed light on the potential impact that local government management and leadership, especially by way of pandemic planning, has upon a real-time pandemic event and provide information relevant to best practices.

The COVID-19 pandemic disrupted the theatrical landscape, prompting theatre artists to reinvent how they connect with audiences and each other in physical spaces. While research has delved into post-pandemic theatre making, much of this area remains unexplored, particularly concerning young people, how the loss of community has impacted them, and what society can do to help. This thesis investigates how professional directors and dramaturgs can effectively engage young people in theatre making post-pandemic by drawing on three specific productions — Camelot and Camelittle, WROL (Without Rule of Law), and Alice and the Wonderland Parties. Utilizing theories drawn from The Art of Active Dramaturgy by Lenora Inez Brown and Directing Young People in Theatre by Samantha Lane — this thesis examines how dramaturgical collaboration can be implemented to foster community and positive relationships among young participants in the theatre-making process. The research in this thesis argues that by employing dramaturgical collaboration in the production process, young people can form meaningful connections with their peers, combating the isolation experienced during the pandemic. Beyond the pandemic context, this type of collaboration offers young people avenues for deeper engagement with the theatrical text itself. This thesis contributes to the discourse on post-pandemic theatre-making by providing practical insights into cultivating connection and community among young participants. It highlights the importance of dramaturgical collaboration as an enrichment tool in theatre making and fostering meaningful relationships in a post-pandemic world.

The purpose of this study was to understand how secondary mathematics teachers in underserved communities learned to teach online during the COVID-19 pandemic. The study used three different methods of data collection: surveys, critical incident questionnaires, and semi-structured interviews. The survey sample consisted of 51 participants, with 20 of those participants opting to complete the critical incident questionnaire in addition to the survey. 39 of the 51 survey participants volunteered to participate in semi-structured interviews. The researcher conducted approximately 18 hours of interviews with 20 of these volunteer participants. The findings were analyzed using inductive and deductive coding techniques, as well as within and cross case analysis to identify trends and themes across participant data. Teachers learned to teach their respective secondary math content in an online setting during the pandemic through a mostly self-directed, trial-and-error process with some support from colleagues. Teachers were given limited guidance and direction as to how to approach facilitating their content online from school administrators and district leaders. Teachers and students in underserved communities faced many barriers in the transition from in-person to online instruction during the pandemic, including limited access to the necessary tools and technology to fully engage with online learning content, a lack of adult supervision in home learning environments, and a lack of experience with online learning for both teachers and students alike. In addition to these barriers, teachers and students endured extreme personal stress throughout the pandemic, stress that may have been exacerbated by the scarcity of resources available to schools in underserved communities. Teachers were able to find some success in translating their specific math content areas and practices to an online setting. Through personal research and experimentation, teachers discovered online tools and learning platforms that empowered them to engage in the transition. The ability for teachers to teach effectively, however, was limited by low student attendance rates and low rates of student engagement and participation throughout the pandemic in underserved communities. In addition to these challenges, teachers were unable to translate specific secondary math content standards and units to the online setting, and in some cases, were forced to completely remove these topics from their curriculum during the unit. The combination of these challenges may have led to the gaps in student learning that emerged throughout the pandemic, particularly among underserved communities. Recommendations included for current and prospective teachers to gain experience with specific online tools and platforms that may have applications to both in-person and online teaching, for school administrators and district leaders to develop more detailed emergency plans and support systems for teachers in the event of a future crisis like the COVID-19 pandemic, and for teacher preparation programs to include at least one course in the curricula devoted to training candidates on how to teach and use online tools and learning platforms.

With 3.5 million Chinese language speakers in the U.S. as reported by Zeigler & Camarota (2019), the dynamics of retaining, diminishing, or enhancing Chinese language proficiency within American society have been a focal point of research (Liang, 2020; Liao & Larke, 2008; Zhang, 2010). This phenomenon, when viewed through a wider historical and social lens, mirrors the experiences of other minority languages and communities facing language attrition and cultural assimilation. Notably, there is a notable gap in literature that celebrates the dedication, endeavors, and optimism towards the preservation, growth, or resurgence of the Chinese language. This gap contributes to reinforcing the dominance of the English language, the normalization of monolingualism, and Eurocentric perspectives. Additionally, research exploring how the evolution of the Chinese language interacts with the identity formation of second-generation Chinese American children—especially against the backdrop of China's rising global influence and the surge in anti-Asian/Chinese sentiment during and post-COVID-19—remains insufficient. The purpose of this study is to document and explore how a group of Chinese immigrant families with first-generation parents and second-generation children experience the maintenance and development of Chinese language in the U.S. and how they perceive the meaning of their effort and commitment, particularly in light of China's growing global presence and the increase in anti-Asian/Chinese sentiment amid and following the COVID-19 pandemic. Bringing together Chinese Heritage Language development theory (He, 2006) and AsianCrit framework (Museus & Iftikar, 2013) and inspired by translanguaging (García & Wei, 2015; Wei, 2018) and multimodality lenses (Kress, 2010), this study used ethnographic and multimodal approaches. Especially, innovative, child-friendly methods were adopted for including even the youngest members of the group in my study. The study concludes by highlighting the significant impact of the Let's Chinese workshops, an informal, parent-led Chinese heritage program, on the curriculum and pedagogical approaches related to Chinese language, culture, and identity construction. It also showcases the diverse strategies employed by first-generation parents in their efforts to maintain and develop Chinese heritage language, their thoughts on the interplay between language, identity, and resistance to racial discrimination, and how the ideology of monolingualism influences their views and approaches. Additionally, the research sheds light on the active role of second-generation children in navigating their heritage language and culture learning, underscoring their agency in shaping their identity understanding and construction.

The COVID-19 pandemic prompted a widespread shift to teletherapy, a historically underutilized yet potent modality that can help increase access to mental health treatment. While numerous studies have investigated the adaptation to teletherapy from the perspective of therapists, there remains a paucity of research on client perspectives. To address this gap, the present study examined the teletherapy process and outcome from the perspective of 2,118 clients who shifted to teletherapy during the pandemic with the same therapist they had been seeing in-person pre-pandemic. Specifically, this study investigated client-perceived changes, concerns, disclosure patterns, benefits, drawbacks, and treatment preferences in teletherapy. Findings revealed that the majority of clients reported teletherapy to be as engaging, effective, and valuable as in-person therapy, with therapeutic boundaries remaining intact. However, several factors, including positive regard, privacy concerns, and technological distractions, significantly influenced clients’ treatment engagement, effectiveness, and future treatment preferences. In addition, clients experiencing elevated distress reported teletherapy as less engaging and effective than in-person treatment. Implications for therapists and clients to optimize teletherapy, as well as directions for future research, are discussed.

Yes / Covid-19, a corona virus, has maintained its momentum in spreading among communities. In this context of social crisis, this study seeks to identify the reasons for the partial failure to fulfill the intended goal of lockdown, and to formulate an inclusive behavioral model reflecting comprehensive human behavior and social psychology. In order to answer the research questions, this study has conducted extensive interviews among individuals who were targets of the lockdown system. From this exploratory and qualitative investigation, researchers have recognized four paradigms as the key to understanding human behavior and social psychology in violating lockdown as a social isolation system during this period of crisis. The identified parameters depicting social behavior are: Derogation and Argument (SDA), Tangible Need and Deficiency (TND), Intangible Desire and Expectancy (IDE), and Evaluation of Benefit and Loss (UBL). Finally, as a comprehensive guideline, a grounded theory of the social behavior ‘paradigm for lockdown violation (PLV)’ is explored as the reason for the violation of the social system.

Yes / This study adopts a theory of reasoned action approach to understand consumers’ mask wearing when shopping in the context of the COVID-19 pandemic. We investigated mask wearing while shopping as a prosocial consumption behaviour whereby self-oriented benefits and others-oriented benefits are added as proposed drivers of attitudes and perceived social norms. Empirical evidence from a survey in France and Germany confirms a strong effect of social norms on mask-wearing intentions. Moreover, altruistic benefits predict mask-wearing intentions, with attitude and subjective norms as mediators. In contrast, self-expression benefits of mask wearing only influence perceived social norms and not attitudes; this effect differs between the countries. Our findings guide scholars, policy makers and practitioners to steer consumers’ mask wearing as a prosocial behaviour. / Received support from central internationalization funds of Universität Hamburg.

Yes / This article examines local non-governmental organizations (LNGOs) approaches to women's empowerment amid crisis and the implications for decolonizing women's empowerment praxis. The article draws on lessons from the Covid-19 pandemic and decolonial critique of development praxis to analyse LNGOs approach to women's empowerment. The study relied on snowballing to select twenty-six LNGOs operating in northern Ghana for in-depth interviews. Our study found that LNGOs deploy local and international frameworks on women's rights and combine these with indigenous knowledge principles and economic empowerment. Yet there are tensions between negotiating culturally appropriate approaches and meeting the interests of philanthro-capitalist donor agencies. The LNGOs are dependent on Western donors for financial resources and have become more vulnerable due to Covid-19 pandemic and its aftermath as funding for women's empowerment work continues to dwindle. LNGOs have modified their interventions to address specific needs of beneficiaries arising from the consequences of the crises. This study enriches understandings of the specific vulnerabilities of LNGOs in northern Ghana, resource-scarce and semi-arid settings across the global South. For women's empowerment to achieve meaningful results, especially during and after crisis, activism needs to be centred on indigenous knowledge. This is central to building the resilience of LNGOs and women beneficiaries to effectively position themselves to absorb the shocks that attend crisis and to cope more effectively with it.

The surge in the application of big data and predictive analytics in fields of crisis management, such as pandemics and epidemics, highlights the vital need for advanced research in these areas, particularly in the wake of the COVID-19 pandemic. Traditional methods, which typically rely on historical data to forecast future trends, fall short in addressing the complex and ever-changing nature of challenges like pandemics and public health crises. This inadequacy is further underscored by the pandemic's significant impact on various sectors, notably healthcare, government, and the hotel industry. Current models often overlook key factors such as static spatial elements, socioeconomic conditions, and the wealth of data available from social media, which are crucial for a comprehensive understanding and effective response to these multifaceted crises. This thesis employs spatial forecasting and predictive analytics to address crisis management in several distinct but interrelated contexts: the COVID-19 pandemic, the opioid crisis, and the impact of the pandemic on the hotel industry. The first part of the study focuses on using big data analytics to explore the relationship between socioeconomic factors and the spread of COVID-19 at the zip code level, aiming to predict high-risk areas for infection. The second part delves into the opioid crisis, utilizing semi-supervised deep learning techniques to monitor and categorize drug-related discussions on Reddit. The third part concentrates on developing spatial forecasting and providing explanations of the rising epidemic of drug overdose fatalities. The fourth part of the study extends to the realm of the hotel industry, aiming to optimize customer experience by analyzing online reviews and employing a localized Large Language Model to generate future customer trends and scenarios. Across these studies, the thesis aims to provide actionable insights and comprehensive solutions for effectively managing these major crises. For the first work, the majority of current research in pandemic modeling primarily relies on historical data to predict dynamic trends such as COVID-19. This work makes the following contributions in spatial COVID-19 pandemic forecasting: 1) the development of a unique model solely employing a wide range of socioeconomic indicators to forecast areas most susceptible to COVID-19, using detailed static spatial analysis, 2) identification of the most and least influential socioeconomic variables affecting COVID-19 transmission within communities, 3) construction of a comprehensive dataset that merges state-level COVID-19 statistics with corresponding socioeconomic attributes, organized by zip code. For the second work, we make the following contributions in detecting drug Abuse crisis via social media: 1) enhancing the Dynamic Query Expansion (DQE) algorithm to dynamically detect and extract evolving drug names in Reddit comments, utilizing a list curated from government and healthcare agencies, 2) constructing a textual Graph Convolutional Network combined with word embeddings to achieve fine-grained drug abuse classification in Reddit comments, identifying seven specific drug classes for the first time, 3) conducting extensive experiments to validate the framework, outperforming six baseline models in drug abuse classification and demonstrating effectiveness across multiple types of embeddings. The third study focuses on developing spatial forecasting and providing explanations of the escalating epidemic of drug overdose fatalities. Current research in this field has shown a deficiency in comprehensive explanations of the crisis, spatial analyses, and predictions of high-risk zones for drug overdoses. Addressing these gaps, this study contributes in several key areas: 1) Establishing a framework for spatially forecasting drug overdose fatalities predominantly affecting U.S. counties, 2) Proposing solutions for dealing with scarce and heterogeneous data sets, 3) Developing an algorithm that offers clear and actionable insights into the crisis, and 4) Conducting extensive experiments to validate the effectiveness of our proposed framework. In the fourth study, we address the profound impact of the pandemic on the hotel industry, focusing on the optimization of customer experience. Traditional methodologies in this realm have predominantly relied on survey data and limited segments of social media analytics. Those methods are informative but fall short of providing a full picture due to their inability to include diverse perspectives and broader customer feedback. Our study aims to make the following contributions: 1) the development of an integrated platform that distinguishes and extracts positive and negative Memorable Experiences (MEs) from online customer reviews within the hotel industry, 2) The incorporation of an advanced analytical module that performs temporal trend analysis of MEs, utilizing sophisticated data mining algorithms to dissect customer feedback on a monthly and yearly scale, 3) the implementation of an advanced tool that generates prospective and unexplored Memorable Experiences (MEs) by utilizing a localized Large Language Model (LLM) with keywords extracted from authentic customer experiences to aid hotel management in preparing for future customer trends and scenarios. Building on the integrated predictive modeling approaches developed in the earlier parts of this dissertation, this final section explores the significant impacts of the COVID-19 pandemic on the airline industry. The pandemic has precipitated substantial financial losses and operational disruptions, necessitating innovative crisis management strategies within this sector. This study introduces a novel analytical framework, EAGLE (Enhancing Airline Groundtruth Labels and Review rating prediction), which utilizes Large Language Models (LLMs) to improve the accuracy and objectivity of customer sentiment analysis in strategic airline route planning. EAGLE leverages LLMs for zero-shot pseudo-labeling and zero-shot text classification, to enhance the processing of customer reviews without the biases of manual labeling. This approach streamlines data analysis, and refines decision-making processes which allows airlines to align route expansions with nuanced customer preferences and sentiments effectively. The comprehensive application of LLMs in this context underscores the potential of predictive analytics to transform traditional crisis management strategies by providing deeper, more actionable insights. / Doctor of Philosophy / In today's digital age, where vast amounts of data are generated every second, understanding and managing crises like pandemics or economic disruptions has become increasingly crucial. This dissertation explores the use of advanced predictive modeling and analytics to manage various crises, significantly enhancing how predictions and responses to these challenges are developed. The first part of the research uses data analysis to identify areas at higher risk during the COVID-19 pandemic, focusing on how different socioeconomic factors can affect virus spread at a local level. This approach moves beyond traditional methods that rely on past data, providing a more dynamic way to forecast and manage public health crises. The study then examines the opioid crisis by analyzing social media platforms like Reddit. Here, a method was developed to automatically detect and categorize discussions about drug abuse. This technique aids in understanding how drug-related conversations evolve online, providing insights that could guide public health responses and policy-making. In the hospitality sector, customer reviews were analyzed to improve service quality in hotels. By using advanced data analysis tools, key trends in customer experiences were identified, which can help businesses adapt and refine their services in real-time, enhancing guest satisfaction. Finally, the study extends to the airline industry, where a model was developed that uses customer feedback to improve airline services and route planning. This part of the research shows how sophisticated analytics can help airlines better understand and meet traveler needs, especially during disruptions like the pandemic. Overall, the dissertation provides methods to better manage crises and illustrates the vast potential of predictive analytics in making informed decisions that can significantly mitigate the impacts of future crises. This research is vital for anyone—from government officials to business leaders—looking to harness the power of data for crisis management and decision-making.

Yes / This study contributes to the existing literature on the relationship between oil market shocks and the green bond market by investigating the impact of the COVID-19 pandemic on their dynamic correlation. We first decompose the oil market shocks into components using a time-frequency framework. Then, we combine wavelet decomposition and quantile coherence and causality methods to discuss changes during the COVID-19 era. We observe positive effects of both supply-driven and demand-driven oil shocks on the green bond market at most quantile levels. However, supply-driven oil price changes play a major role. The results also indicate that long-term changes have a greater impact than short-term changes on the connection between oil and green bond markets. Nevertheless, the COVID-19 pandemic changed the nature of the causal relationship, as we observed no relationship under extreme market conditions during the pandemic era. We argue that the economic and social impacts of the COVID-19 pandemic have left investors focusing on the short-term substitution between oil and green bond markets. / This research was supported by the Major Projects of the National Natural Science Fund of China [NO. 71991483], the Natural Science Fund of Hunan Province [NO. 2022JJ40647] and the Fundamental Research Funds for the Central Universities of Central South University [NO. 2022ZZTS0353].