A Data-Driven Computational Framework to Assess the Risk of Epidemics at Global Mass Gatherings

• Main Author: Alshammari, Sultanah
• Other Authors: Mikler, Armin R.
• Format: Others
• Language: English
• Published: University of North Texas 2019
• Subjects: Modeling; Simulation; Agent-based Model; Data-driven; Computational Framework; Global Mass Gatherings; Hajj; Epidemic; Outbreak; Infectious Diseases; Public Health; Disease Control
• Online Access: https://digital.library.unt.edu/ark:/67531/metadc1505145/

This dissertation presents a data-driven computational epidemic framework to simulate disease epidemics at global mass gatherings. The annual Muslim pilgrimage to Makkah, Saudi Arabia is used to demonstrate the simulation and analysis of various disease transmission scenarios throughout the different stages of the event from the arrival to the departure of international participants. The proposed agent-based epidemic model efficiently captures the demographic, spatial, and temporal heterogeneity at each stage of the global event of Hajj. Experimental results indicate the substantial impact of the demographic and mobility patterns of the heterogeneous population of pilgrims on the progression of the disease spread in the different stages of Hajj. In addition, these simulations suggest that the differences in the spatial and temporal settings in each stage can significantly affect the dynamic of the disease. Finally, the epidemic simulations conducted at the different stages in this dissertation illustrate the impact of the differences between the duration of each stage in the event and the length of the infectious and latent periods. This research contributes to a better understanding of epidemic modeling in the context of global mass gatherings to predict the risk of disease pandemics caused by associated international travel. The computational modeling and disease spread simulations in global mass gatherings provide public health authorities with powerful tools to assess the implication of these events at a different scale and to evaluate the efficacy of control strategies to reduce their potential impacts.