Pandemic outbreaks are unpredictable as to their virus strain, transmissibility, and impact on our quality of life. Hence, the decision support models for mitigation of pandemic outbreaks must be user-friendly and operational, and also incorporate valid estimates of disease transmissibility and severity. This dissertation research is aimed at 1) reviewing the existing pandemic simulation models to identify their implementation gaps with regard to usability and operability, and suggesting research remedies, 2) increasing operability of simulation models by calibrating them via an epidemiological model that estimates infection probabilities using viral shedding profiles of concurrent pandemic and seasonal influenza, and 3) developing a testing strategy for the state laboratories, with their limited capacities, to improve their ability to estimate evolving transmissibility parameters. Our review of literature (Aim 1) indicates the need to continue model enhancements in critical areas including updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, and improvement of computational efficiency and accessibility. As regards the ease of calibration (Aim 2), we demonstrate that the simulation models, when driven by the infection probabilities obtained from our epidemiological model, accurately reproduce the disease transmissibility parameters. Assuming the availability of sufficient disease reporting infrastructure and strong compliance by both infected population and healthcare providers, our testing strategy (Aim 3) adequately supports characterization of real-time epidemiological parameters. Future research on this topic will be aimed at integrating the laboratory testing strategy with our modeling and simulation approach to develop dynamic mitigation strategies for pandemic outbreaks.

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.