Yes / The issues of food loss and waste (FLW) in the global supply chains have recently attracted attention. However, the causes of and strategies for mitigating FLW at different stages of the supply chains remain under researched. Our research aims to address these gaps in knowledge in a three-fold way: i) we identified the key causes (through root-cause analysis) of FLW in the supply chain of developed and less developed countries; ii) we systematically classified measures and policies that have been implemented to mitigate FLW; and iii) we developed an interdisciplinary conceptual framework for waste utilisation practices that can contribute towards the triple bottom-line in food systems. A root-cause analysis was performed and mitigation strategies identified by systematically analysing and synthesising the research published over the past 20 years (1998 to 2018) in the areas of FLW in the supply chain. We propose a conceptual model for the prevention of FLW utilising a systems approach through the concept of a circular economy. Since the agri-food sector is largely interdisciplinary, in our proposed model, we have also demonstrated a method of integrating contributions from multiple disciplines towards achieving total depollution (zero waste) in the supply chain. / Support provided by the British Academy/Leverhulme Small Research Grant, Reference No: SG160072, for the development of the study.

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.

Detta examensarbete ålägger en omfattande studie riktad mot att granska de komplexiteter och utmaningar som förekommer vid utveckling av robusta och effektiva verktyg som upptäcker säkerhetsrisker i kod. Genom att bestyra en jämförande analys av redan existerande säkerhetsverktyg, och engagera sig i ett försök av att utveckla ett säkerhetsverktyg från en grundläggande nivå, strävar detta arbete efter att uppenbara de underliggande anledningarna bakom varför det, inom cybersäkerhet, ännu är en stor utmaning att ligga steget före skadliga aktörer. Inledande bidrar forskningen med en överblick av aktuella säkerhetsverktyg, och samtidigt undersöks deras effektivitet, metoder, samt de typer av sårbarheter som verktygen är designade för att upptäcka. Genom systematiska mätningar betonar studien styrkor och svagheter av säkerhetsverktygen, och samtidigt dokumenteras utvecklingsprocessen av ett nytt säkerhetsverktyg med syfte att upptäcka liknande sårbarheter som de jämförda verktygen. De bemötta utmaningarna vid utvecklande—som att behandla moderna säkerhetshot, och integrera komplexa upptäckningsalgoritmer—diskuteras för att förevisa de övertygande hinder som utvecklare påträffar. Därutöver bedöms viktigheten av att effektivt kunna upptäcka sårbarheter, och hur det kan hjälpa att bevara integritet och pålitlighet av applikationer. Examensarbetet siktar mot att bidra med viktig insyn i området cybersäkerhet, samt stödja fortsatt utveckling i mån av att mildra säkerhetshot. Sammanfattningsvis visar resultatet från denna studie att det krävs både kunskap och ambition för att utveckla ett säkerhetsverktyg från grunden, eftersom nya hot uppstår nästan varenda dag. Studien avslöjar också att skadliga aktörer är kända för att regelbundet leta efter sårbarheter i system, och är en av de ledande anledningarna till varför det är så svårt att bekämpa cyberhot. / This thesis stipulates a comprehensive study aimed at examining the complexities and challenges in developing robust and effective tools for detecting security vulnerabilities in code. By performing a comparative analysis of already existing security tools, and engaging in an attempt of developing a security tool from a foundational level, this work strives to disclose the underlying reasons as to why staying one step ahead of malicious actors remains a difficult challenge in cybersecurity. Introductory, the study provides an overview of current security tools while examining their effectiveness, methodologies, and the types of vulnerabilities they are designed to detect. Through systematic measurements, the study highlights strengths and weaknesses of the security tools while, simultaneously, documenting the process of developing a new security tool designed to detect similar vulnerabilities to the compared tools. The challenges faced during development—such as treating modern security threats, and integrating complex detection algorithms—are discussed to portray the compelling hurdles that developers encounter. Moreover, this thesis assesses the importance of effectively detecting vulnerabilities, and how it can aid in maintaining integrity and trustworthiness of applications. The thesis aims to contribute with valuable insight into the field of cybersecurity and support continued development for mitigating cyber threats. In conclusion, the outcome from this study shows that developing a security tool from a foundational level requires both knowledge and ambition, since new threats occur almost every day. The study also reveals that malicious actors are known for frequently looking for vulnerabilities in systems, making it one of the leading reasons why it is difficult to fight cyber threats.

Research Background: There is a growing focus on the sustainability implications of digitalisation in research, industry and politics. While digitalisation offers economic benefits and potential environmental solutions, it also brings unintended consequences known as rebound effects. These effects, amplified by the widespread impact of digitalisation on economies and societies, have drawn attention to the need for mitigation strategies. However, current research primarily focuses on defining and studying rebound effects rather than on mitigation. Existing mitigation strategies mainly involve fiscal and policy measures, but alternative approaches that address underlying principles are needed. Further research is crucial for exploring effective strategies to mitigate rebound effects caused by digitalisation.  Research Purpose: This thesis aims to identify mitigation strategies for the digital rebound effect employed by companies by understanding the contributing factors to this complex phenomenon.    Method: A qualitative method was used to investigate strategies for companies to mitigate the digital rebound effect amid digitalisation and sustainability concerns. Through exploratory research, we aimed to comprehensively understand underlying factors, mitigation strategies and associated challenges. Semi-structured remote interviews were chosen for data collection to provide detailed insights. Purposive sampling was employed to identify suitable participants for the research topic. Our analysis and presentation of empirical findings followed an abductive approach.  Conclusion: Our framework, based on Bohnsack et al. (2021) model, delves into the unintended consequences of digital technology by incorporating stakeholder perspectives. Key contributing factors include personal challenges and resource-related issues. Addressing these factors requires fostering a learning culture and technical competence. Mitigation strategies in the thesis focus on the firm and individual levels. Stakeholder involvement is crucial for effective problem-solving. Our framework aligns with stakeholder theory, enhancing understanding and mitigation of digital rebound effects.

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As a result of climate change and urbanization, rising temperatures are causing increasing concern about extreme heat in cities worldwide. Urban extreme heat like other climate-related phenomena is a complex problem that requires expertise from a range of disciplines and multi-faceted solutions. Therefore, this study aims to develop a comprehensive understanding of urban heat issue by taking a multi-level approach that integrates science, technology, and policy. Throughout the three main papers of this dissertation, a variety of quantitative and qualitative methods, such as microclimate modeling, machine learning, statistical analysis, and policy content analysis, are used to analyze urban heat from different perspectives. The first paper of this dissertation focuses on the street canyon scale, aiming to identify the physical and vegetation parameters that have the greatest impact on changing thermal conditions in urban environments and to understand how these parameters interact with each other. Moving towards identifying applicable heat-related data and measurement techniques, the second paper assesses whether lower-resolution temperature data and novel sources of vulnerability indicators can effectively explain intra-urban heat variations. Lastly, the third paper of this dissertation reviews heat-related plans and policies at the Planning Districts level in Virginia, providing insights into how extreme heat is framed and addressed at the regional and local levels. This analysis is particularly important for states such as Virginia, which historically have not experienced multiple days of extreme heat during summers, as is common in southern and southwestern states of the United States. The results of this study provide insights into the contributing and mitigating factors associated with extreme heat exposure, novel heat-related data and measurement techniques, and the types of analysis and information that should be included in local climate-related plans to better address extreme heat. This dissertation explores new avenues for measuring, understanding, and planning extreme heat in cities, thereby contributing to the advancement of knowledge in this field. / Doctor of Philosophy / Due to climate change and fast city growth, temperatures are rising, and extreme heat is becoming a big worry in cities worldwide. Urban extreme heat is a challenging problem that needs expertise from different majors and diverse solutions. This dissertation aims to understand urban heat better by integrating science, technology, and policy. The three main research papers of this dissertation use various methods like modeling, statistics, and policy analysis to study urban heat from different angles. The first paper focuses on city streets and how certain physical features and vegetation affect citizens' thermal comfort. The second paper explores new ways to measure heat in urban areas, including using new sources of data and the application of lower-resolution data. Finally, the third paper reviews heat-related plans and policies in Virginia, helping us understand how extreme heat is addressed in areas that might not be accustomed to high temperatures. This dissertation's findings provide useful insights into why the severity of extreme heat is not the same in different parts of cities, present new ways to measure this difference and find solutions to lessen the negative impacts of exposure to heat. It also shows what information needs to be included in plans and policies to better deal with extreme hot weather at the local level such as in towns and cities. By exploring new ways to understand and handle extreme heat in cities, this research helps make progress in this important field. The goal of this research is to help cities prepare for and cope with urban extreme heat, keeping people safe and creating sustainable cities for the future.

Achieving energy neutrality has shifted focus towards aeration systems optimization, due to the high energy consumption of aeration processes in modern advanced wastewater treatment plants. The activated sludge wastewater treatment process is dependent on aeration efficiency which supplies the oxygen needed in the treatment process. The process is a complex heterogeneous mixture of microorganisms, bacteria, particles, colloids, natural organic matter, polymers and cations with varying densities, shapes and sizes. These activated sludge parameters have different impacts on aeration efficiency defined by the OTE, % and alpha. Oxygen transfer efficiency (OTE) is the mass of oxygen transferred into the liquid from the mass of air or oxygen supplied, and is expressed as a percentage (%). OTE is the actual operating efficiency of an aeration system. The alpha Factor (α) is the ratio of standard oxygen transfer efficiency at process conditions (αSOTE) to standard oxygen transfer efficiency of clean water (SOTE). It is also referred to as the ratio of process water volumetric mass transfer coefficient to clean water volumetric mass transfer coefficient. The alpha factor accounts for wastewater contaminants (i.e. soap and detergent) which have an adverse effect on oxygen transfer efficiency. Understanding their different impacts and how different treatment technologies affect aeration efficiency will help to optimize and improve aeration efficiency so as to reduce plant operating costs. A pilot scale study of fine pore diffuser fouling and mitigation, quantified by dynamic wet pressure (DWP), oxygen transfer efficiency and alpha measurement were performed at Blue Plains, Washington DC. In the study a mechanical cleaning method, reverse flexing (RF), was used to treat two diffusers (RF1, RF2) to mitigate fouling, while two diffusers were kept as a control with no reverse flexing. A 45 % increase in DWP of the control diffuser after 17 month of operation was observed, an indication of fouling. RF treated diffusers (RF1 and RF2) did not show any significant increase in DWP, and in comparison to the control diffuser prevented a 35 % increase in DWP. Hence, the RF fouling mitigation technique potentially saved blower energy consumption by reducing the pressure burden on the air blower and the blower energy requirement. However, no significant impact of the RF fouling mitigation treatment technique in preventing a decrease in alpha-fouling (𝝰F) of the fine pore diffusers over time of operation was observed. This was because either the RF treatment method maintained wide pore openings after cleaning over time, or a dominant effect of other wastewater characteristics such as the surfactant concentration or particulate COD could have interfered with OTE. Further studies on the impact of wastewater characteristics (i.e., surfactants and particulate COD) and operating conditions on OTE and alpha were carried out in another series of pilot and batch scale tests. In this study, the influence of different wastewater matrices (treatment phases) on oxygen transfer efficiency (OTE) and alpha using full-scale studies at the Blue Plains Treatment Plant was investigated. A strong relationship between the wastewater matrices with oxygen transfer characteristics was established, and as expected increased alphas were observed for the cleanest wastewater matrices (i.e., with highest effluent quality). There was a 46 % increase in alpha as the total COD and surfactant concentrations decreased from 303 to 24 mgCOD/L and 12 to 0.3 mg/L measured as sodium dodecyl sulphate (SDS) in the nitrification/denitrification effluent with respect to the raw influent. The alpha improvement with respect to the decrease in COD and surfactant concentration suggested the impact of one or more of the wastewater characteristics on OTE and alpha. Batch testing conducted to characterize the mechanistic impact of the wastewater contaminants present in the different wastewater matrices found that the major contaminants influencing OTE and alpha were surfactants and particulate/colloidal material. The volumetric mass transfer coefficient (kLa) measurements from the test also identified surfactant and colloidal COD as the major wastewater contaminants present in the influent and chemically enhanced primary treatment (CEPT) effluent wastewaters impacting OTE and alpha. Soluble COD was observed to potentially improve OTE and alpha due to its contribution in enhancing the oxygen uptake rate (OUR). Although the indirect positive impact of OUR on alpha observed in this study contradicts some other studies, it shows the need for further investigation of OUR impacts on oxygen transfer. Importantly, the mechanistic characterization and quantitative correlation between wastewater contaminants and aeration efficiency found in this study will help to minimize overdesign with respect to aeration system specification, energy wastage, and hence the cost of operation. This study therefore shows new tools as well as the identification of critical factors impacting OTE and alpha in addition to diffuser fouling. Gas transfer depression caused by surfactants when they accumulate at the gas-liquid interface during the activated sludge wastewater treatment process reduces oxygen mass transfer rates, OTE and alpha which increases energy cost. In order to address the adverse effect of surfactants on OTE and alpha, another study was designed to evaluate 4 different wastewater secondary treatment strategies/technologies that enhances surfactant removal through enhanced biosorption and biodegradation, and to also determine their effect on oxygen transfer and alpha. A series of pilot and batch scale tests were conducted to compare and correlate surfactant removal efficiency and alpha for a) conventional high-rate activated sludge (HRAS), b) optimized HRAS with contactor-stabilization technology (HRAS-CS), c) optimized HRAS bioaugmented (Bioaug) with nitrification sludge (Nit S) and d) optimized bioaugmented HRAS with an anaerobic selector phase technology (An-S) reactor system configuration. The treatment technologies showed surfactant percentage removals of 37, 45, 61 and 87 %, and alphas of 0.37 ±0.01, 0.42 ±0.02, 0.44 ±0.01 and 0.60 ±0.02 for conventional HRAS, HRAS-CS, Bioaug and the An-S reactor system configuration, respectively. The optimized bioaugmented anaerobic selector phase technology showed the highest increased surfactant removal (135 %) through enhanced surfactant biosorption and biodegradation under anaerobic conditions, which also complemented the highest increased alpha (62 %) achieved when compared to the conventional HRAS. This study showed that the optimized bioaugmented anaerobic selector phase reactor system configuration is a promising technology or strategy to minimize the surfactant effects on alpha during the secondary aeration treatment stage / Ph. D. / In the activated sludge process, the energy requirement for aeration which also includes nitrogen removal is a major operating expense for utilities, and it has limited the ability of most water and wastewater reclamation facilities to achieve energy neutrality. Aeration has therefore become one of the most energy and capital intensive aspects of wastewater treatment. There are still knowledge gaps and mechanistic understanding of the impact of wastewater characteristics and treatment processes on aeration efficiency, which past and current studies are yet to provide. Aeration efficiency is defined by oxygen transfer efficiency and alpha (an indicator of wastewater contaminant effect on aeration efficiency). This study provided an insight into important wastewater characteristics, treatment processes and operational parameters contributing to aeration cost. An understanding of the impacts of wastewater characteristics and how different treatment technologies affect aeration efficiency as discussed in this study will help design engineers and operators to optimize and improve aeration efficiency, so as to reduce plant operating costs. The first study objective on fine bubble diffuser fouling dynamics and physical treatment method quantified by dynamic wet pressure (DWP), oxygen transfer efficiency and alpha measurement was carried out in a pilot reactor. DWP quantified the fouling dynamics of fine pore diffusers. A diffuser fouling physical treatment (reverse flexing, RF) method was able to mitigate fouling of the fine pore diffusers by preventing an increase in DWP normally observed in fouled fine pore diffusers. The RF treatment method reduced fouling by 35 % as compared to the control diffuser (without reverse flexing). This will reduce the pressure burden and air blower energy requirement. The second study objective evaluated the impact of different wastewater characteristics and removal in different stages on aeration efficiency. Test results in this study showed that surfactant and particulate COD fractions were the major characteristics constituents contained in wastewater that depressed aeration efficiency defined by OTE and alpha. Soluble COD did not show any inhibiting effect on OTE and alpha. The third study objective evaluated three different optimized wastewater treatment technologies of surfactant removal during aeration treatment process; 1) High rate activated sludge (HRAS) with contactor-stabilization technology (The contactor stabilization process) (HRAS-CS); 2) HRAS bioaugmented (BioAug) with nitrification sludge (Nit S); and 3) Bioaugmented HRAS with an anaerobic selector phase (An-S) configuration. All three technologies increased surfactant removal through enhanced biosorption and biodegradation to various degrees when compared the conventional high rate activated sludge treatment, but the An-S treatment technology achieved the highest surfactant removal and alpha improvement. The study also established the optimum performance process conditions for each optimized treatment technology.

The radio-frequency spectrum is a precious resource, with many applications and users, especially with the recent spectrum auction in the United States. Future platforms and devices, such as radars and radios, need to be adaptive to their spectral environment in order to continue serving the needs of their users. This thesis considers an environment with one tracking radar, a single target, and a communications system. The radar-communications coexistence problem is modeled as a Markov decision process (MDP), and reinforcement learning is applied to drive the radar to optimal behavior. / Master of Science / The radio-frequency electromagnetic spectrum is a precious resource, in which users and operators are assigned frequency slots in which they can operate. The federal spectrum auction in the United States freed up some of the spectrum for shared use. The implications of this are the spectrum will become more dense; there will be more devices and users in the same amount of spectrum. The devices and platforms of this spectrum need to be more adaptive and agile in order to (1) not be interfered by other systems, (2) cause interference to other systems, and (3) continue to meet the needs of users (e.g. cell phone users) and operators (e.g. military radar). The work presented in this thesis applies Markov decision process and reinforcement learning to solve the problem.

This project deals with exploring 1) travel-related health risk perception, and 2) actions taken to mitigate that health risk. Ordered logistic regression models were used to identify factors associated with the perceived risk of contracting influenza at work, school, daycare, stores, restaurants, libraries, hospitals, doctor’s offices, public transportation, and family or friends’ homes. Based on the models, factors influencing risk perception of contracting influenza in public places for discretionary activities (stores, restaurants, and libraries) are consistent but differ from models of discretionary social visits to someone’s home. Mandatory activities (work, school, daycare) seem to have a few unique factors (e.g., age, gender, work exposure), as do different types of health-related visits (hospitals, doctors’ offices). Across all of the models, recent experience with the virus, of either an individual or a household member, was the most consistent set of factors increasing risk perception. Using such factors in examining transportation implications will require tracking virus outbreaks for use in conjunction with other factors. Subsequently, social-health risk mitigation strategies were studied with the objective of understanding how risk perception influences an individual’s protective behavior. For this objective, this study analyzes travel-actions associated with two scenarios during an outbreak of influenza: 1) A sick person avoiding spreading the disease and 2) A healthy person avoiding getting in contact with the disease. Ordered logistic regression models were used to identify factors associated with mitigation behavior in the first scenario: visiting a doctor’s office, avoiding public places, avoiding public transit, staying at home; and in the second scenario: avoiding public places, avoiding public transit, staying at home. Based on the models for Scenario 1, the factors affecting the decision of avoiding public places, avoiding public transit, and staying at home were fairly consistent but differ for visiting a doctor’s office. However, Scenario 2 models were consistent with their counterpart mitigation models in Scenario 1 except for two factors: gender and household characteristics. Across all the models from Scenario 1, gender was the most significant factor, and for Scenario 2, the most significant factor was the ratio of household income to the household size. / Master of Science / Transmission of a communicable disease depends on the social interactions of the members of society. Generally, individuals associate their health-protection behavior to the perception of health risk associated with that activity. Hence, individuals with high health-risk perception are likely to participate in a protective action to reduce the threat of getting infected with influenza. However, in some cases, even if a high health risk is perceived, an individual might have a decreased likelihood to take actions to mitigate that risk. This behavior could be associated with their inability to carry out recommendations, such as vaccination (due to the cost of vaccination) or adopting protective behaviors such as social isolation (switching from public transit to personal vehicle due to the associated cost). This behavior, of either adopting or rejecting protective action, can be explained by protection motivation theory. This theory explains the individual’s perception of the severity of an event (i.e., threat appraisal), and individual’s expectancy of carrying out recommendations (risk mitigation strategies) to reduce threat (i.e., coping appraisal). Both, health risk perception and risk-mitigation strategies are studied for changes in travel decisions.

We present a comprehensive approach to designing and optimizing infectious disease mitigation strategies, with a focus on COVID-19 and closed communities like college campuses. By integrating vaccination and routine screening, we first develop a model to evaluate the efficacy of various strategies in reducing infections, hospitalizations, and deaths on a college campus during the Fall 2021 semester. The findings emphasize the importance of customizing interventions based on factors such as initial vaccine coverage, vaccine effectiveness, compliance rates, and disease transmission dynamics. As COVID-19 variants continue to emerge, we highlight the necessity for adaptive screening strategies that account for the existing variants and differences in transmission and outcomes among population groups, such as faculty/staff, and students, based on their vaccination status and level of natural immunity. Using the Spring 2022 academic semester as a case study, we study various routine screening strategies and find that screening faculty and staff less frequently than students, and/or screening the boosted and vaccinated less frequently than the unvaccinated, may avert a higher number of infections per test compared to universal screening of the entire population at a common frequency. We also discuss key policy issues, including the need to revisit the mitigation objectives over time and determine if and when screening alone can compensate for low booster coverage. In contexts where mandates are not feasible and vaccine hesitancy is prevalent, we explore the role of voluntary vaccination compliance, supported by monetary incentives and routine screening. We introduce an optimization framework that considers the dual role of screening as both a mitigation tool and a non-monetary incentive. This framework necessitates a novel optimization model for incentive design, integrated with a utility-based decision model that accounts for resource constraints and uncertainties in community response to mitigation efforts. We establish structural properties of Pareto sets of strategies and analyze how they adjust with community characteristics, leading to key insights. Our findings offer actionable strategies for diverse communities and underscore the substantial value of tailoring mitigation efforts to community characteristics and incorporating the incentive effect of routine screening. Overall, this research provides actionable insights into the development of targeted and adaptive mitigation strategies that can be applied in diverse community settings, ensuring safe operations and effective disease control amidst evolving epidemiological challenges. The methodologies and insights from our study are poised to inform and guide the design of mitigation strategies in a variety of institution and community settings, contributing significantly to the collective efforts against infectious diseases. / Doctor of Philosophy / This research focuses on developing strategies to reduce the spread of infectious diseases like COVID-19, particularly in communities such as college campuses. We explore how combining vaccination and regular testing can help reduce the number of infections, hospitalizations, and deaths. By studying different approaches during the Fall 2021 semester, we found that strategies need to be adjusted based on factors like how many people are vaccinated, how effective the vaccines are, and how willing people are to follow the guidelines. As new COVID-19 variants appear, it is important to adapt testing plans based on how these variants spread and how they affect different groups, such as students and faculty, depending on their vaccination and immunity levels. In our study of the Spring 2022 semester, we found that testing faculty less frequently than students, or testing those who are vaccinated less often than those who are unvaccinated, can be more effective than testing everyone at the same rate. We also discuss when testing alone might be enough if vaccination rates are low. In situations where vaccines aren't mandatory and some people are hesitant to get vaccinated, we explore how offering a monetary incentive and regular testing can encourage more people to get vaccinated. We introduce a model that helps decision makers choose the best monetary incentive amount and testing rate, considering the dual role of testing both as a health measure and as an incentive to encourage vaccination. Our findings show that communities can benefit from strategies that are tailored to their specific needs and that include both vaccination incentives and testing. Overall, this research provides practical recommendations for creating flexible strategies that help communities stay safe and control the spread of disease, even as conditions change.