Hajj, the annual Muslim pilgrimage to Mecca, Saudi Arabia is a unique mass gathering event that attracts approximately 3 million pilgrims from around the globe. This diverse pilgrim population coupled with the nature of the performed activities raise major public health concerns in the host country with potential global implications. Although gastroenteritis and diarrhea are common among the pilgrims performing Hajj, the microbial etiologies of these infections are still unknown. We used molecular and antigenic approaches to identify the main pathogens associated with Hajj diarrhea. 544 fecal samples from pilgrims suffering from diarrhea whilst performing Hajj during three consecutive seasons (2011-2013) and 99 control samples from 2011 were screened for 16 pathogens that include bacterial, parasitic and viral etiologies that are commonly associated with diarrheal infections. At least one of the screened pathogens could be detected in 42% (n=228) of the samples from the diarrheal cases. Bacteria were the main agents detected in 83% (n=189) of the positive samples, followed by viral and parasitic agents detected in 6% (n=14) and 5% (n=12) respectively. We have also standardized a 16S-based metagenomic approach to identify the gut microbiome in diarrheal cases and non-diarrheal controls in 76 samples. Also, we have standardized a shotgun metagenomics protocol for the direct characterization (diagnosis) of enteric pathogens without cultivation. This approach was used successfully to identify viral (adenovirus) and bacterial causes of Enterotoxigenic E. coli diarrhea from Hajj samples. The findings in this study fill in clear gaps in our knowledge of the etiologies associated with diarrheal infections during Hajj. Foodborne bacteria were the major contributors to Hajj-diarrheal infections. This was coupled with the increased incidences of antimicrobial resistance loci associated with the identified bacteria. These findings would help the public health policy makers to develop and introduce appropriate public health measures to improve the food safety during Hajj.

Hajj, the annual Islamic pilgrimage to Makkah, Saudi Arabia, is a unique mass gathering event that brings more than 2 million individuals from around the world. Several public health considerations, such as the spread of infectious diseases, must be taken into account with this large temporary influx of people. Gastrointestinal diseases, such as diarrhea, are common at Hajj, yet little is known about the etiology. The human gut microbiome, collection of organisms residing within the intestinal tract, has been under intense study recently, since next generation DNA sequencing technologies allow for extensive surveying of genetic material found in complex biological samples, such as those containing many different organisms. Thus, using 16S rRNA and metagenomic shotgun sequencing, we have characterized the gut microbiome of over 612 pilgrims with and without diarrhea. Several metadata factors, such as hospitalization and different comorbidities, were found to have significant effects on the overall gut microbiome composition. Metagenomic shotgun sequencing efforts revealed the presence of antimicrobial resistance genes originating from disparate regions from around the world. This study provides a snapshot of information concerning the health status of the gut microbiome of Hajj pilgrims and provides more context to the investigation of how to best prepare for mass gathering events.

This dissertation employs management systems engineering principles, data science, and industrial systems engineering techniques to address pressing challenges in emergency department (ED) efficiency, infectious disease management at mass gatherings, and student self-efficacy. It is structured into three essays, each contributing to a distinct domain of research, and utilizes industrial and systems engineering approaches to provide data-driven insights and recommend solutions. The first essay used data analytics and regression analysis to understand how patient length of stay (LOS) in EDs could be influenced by multi-level variables integrating patient, service, and organizational factors. The findings suggested that specific demographic variables, the complexity of service provided, and staff-related variables significantly impacted LOS, offering guidance for operational improvements and better resource allocation. The second essay utilized system dynamics simulations to develop a modified SEIR model for modeling infectious diseases during mass gatherings and assessing the effectiveness of commonly implemented policies. The results demonstrated the significant collective impact of interventions such as visitor limits, vaccination mandates, and mask wearing, emphasizing their role in preventing health crises. The third essay applied machine learning methods to predict student self-efficacy in Muslim societies, revealing the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It provided a basis for identifying students with varying levels of self-efficacy and developing tailored strategies to enhance their academic and personal success. Collectively, these essays underscore the value of data-driven and evidence-based decision- making. The dissertation's broader impact lies in its contribution to optimizing healthcare operations, informing public health policy, and shaping educational strategies to be more culturally sensitive and psychologically informed. It provides a roadmap for future research and practical applications across the healthcare, public health, and education sectors, fostering advancements that could significantly benefit society. / Doctor of Philosophy / Divided into three essays, this dissertation uses industrial and systems engineering and data science to help make emergency departments more efficient, manage the spread of diseases at large events, and predict students' belief in their abilities. The first essay investigates factors that influence how long patients stay in emergency departments, including patient demographics, triage level, the complexity of care they receive, and number of emergency department staff when patient arrived. The essay offers suggestions to improve these services and better manage resources. The second essay models the spread of COVID-19 during the Hajj, a religious mass gathering, and evaluates the effectiveness of three safety measures: limiting the number of attendees, vaccinations, and wearing masks. This essay shows how different strategies can work together to prevent outbreaks. The third essay uses artificial intelligence and machine learning to understand what affects Muslim students' confidence in their abilities, focusing on emotional intelligence, thinking skills, and self-discipline. The findings could help to identify students who need extra support and to create more personalized programs that will help them succeed. Overall, this dissertation contributes to advancing industrial and systems engineering and data science knowledge by addressing complex issues in healthcare, public health, and education, leading to more informed decisions and better strategies. Its broader impact includes improving hospital operations, guiding public health decisions, and helping develop educational programs and interventions that consider cultural and psychological factors.

The aim of the thesis is to analyse and compare two mass gymnastic exercises in terms of spatial choreography, the Spartakiade exercise for younger pupils from 1980 and the mass exercise of the Czech Association of Sport for all teenage pupils, girls and boys from 2012 called Between stars. The thesis also maps the development of mass gymnastic exercises in Czechoslovakia and later in the Czech Republic and generally describes the mass gymnastic exercise and the principles of its creation. The practical part of the work deals with Spartakiade exercise for younger pupils from 1980 and mass exercise of the Czech Association of Sport for all teenage pupils, girls and boys from 2012. The aim of the practical part is to characterise and compare the synchronised exercises in terms of place of the exercise, number of participants, sex, length of the exercise, background music and its intelligibility for the given age, clothing, equipment, spatial choreography and movement difficulty for individuals as well as evaluate content based on creation principles. KEYWORDS mass gymnastic exercise, Spartakiade, the Sokol Slet ( mass gathering of gymnasts organised by the Czech Sokol Movement ), younger teenage pupils, choreography

The thesis investigates how Agent-Based Modeling and Simulation (ABMS) and Queueing Theory (QT) techniques help manage mass gathering (MG) crowds. The techniques are applied to Hajj MG, which is one of the most complex annual MG, with a focus on its challenging Tawaf ritual. The objective is to develop a Tawaf Decision Support System (DSS) to better understand Tawaf crowd dynamics and discover decisions that lead to superior performance. TawafSIM is an ABMS model in the DSS, which simulates macro-level Tawaf crowd dynamics through micro-level pilgrim modeling to explore the impact of crowd characteristics, facility layout, and management preferences on emergent crowd behaviours with respect to throughput, satisfaction, health, and safety. Whereas, TawafQT is a QT model in the DSS to explore the impact of pilgrim arrival rate and Tawaf throughput on expected arrival, departure, and waiting times along with average queue length in the Tawaf waiting area. The thesis provides several contributions, including the following. First, it is the only Tawaf research to use a hybrid ABMS and QT approach. Second, TawafSIM is a comprehensive Tawaf simulator. It incorporates features for pilgrim characteristics, facility design, and management preferences. It calculates eight metrics for Tawaf performance, which includes one for throughput, three for satisfaction, one for health, and three for safety. It is the only Tawaf simulator to estimate satisfaction and spread of infectious disease. It conducts 42 simulation experiments in 12 categories. It generates observations for emergent, tipping point, expected, and counter intuitive behaviours. It recommends a default scenario as the best decision along with a small subset of alternative scenarios, which provide above average Tawaf performance. It generates a Tawaf Crowd Management Guide to better understand Tawaf crowd dynamics and how to pursue above average Tawaf performance under different conditions. Third, TawafQT is the only study of the Tawaf waiting area. It uses an accurate queueing model with finite source, single service, and PH type distribution, which is not only applicable to the Tawaf and other Hajj related queueing systems but also to any queueing system, which has finite population and single service characteristics.

The thesis investigates how Agent-Based Modeling and Simulation (ABMS) and Queueing Theory (QT) techniques help manage mass gathering (MG) crowds. The techniques are applied to Hajj MG, which is one of the most complex annual MG, with a focus on its challenging Tawaf ritual. The objective is to develop a Tawaf Decision Support System (DSS) to better understand Tawaf crowd dynamics and discover decisions that lead to superior performance. TawafSIM is an ABMS model in the DSS, which simulates macro-level Tawaf crowd dynamics through micro-level pilgrim modeling to explore the impact of crowd characteristics, facility layout, and management preferences on emergent crowd behaviours with respect to throughput, satisfaction, health, and safety. Whereas, TawafQT is a QT model in the DSS to explore the impact of pilgrim arrival rate and Tawaf throughput on expected arrival, departure, and waiting times along with average queue length in the Tawaf waiting area. The thesis provides several contributions, including the following. First, it is the only Tawaf research to use a hybrid ABMS and QT approach. Second, TawafSIM is a comprehensive Tawaf simulator. It incorporates features for pilgrim characteristics, facility design, and management preferences. It calculates eight metrics for Tawaf performance, which includes one for throughput, three for satisfaction, one for health, and three for safety. It is the only Tawaf simulator to estimate satisfaction and spread of infectious disease. It conducts 42 simulation experiments in 12 categories. It generates observations for emergent, tipping point, expected, and counter intuitive behaviours. It recommends a default scenario as the best decision along with a small subset of alternative scenarios, which provide above average Tawaf performance. It generates a Tawaf Crowd Management Guide to better understand Tawaf crowd dynamics and how to pursue above average Tawaf performance under different conditions. Third, TawafQT is the only study of the Tawaf waiting area. It uses an accurate queueing model with finite source, single service, and PH type distribution, which is not only applicable to the Tawaf and other Hajj related queueing systems but also to any queueing system, which has finite population and single service characteristics.