Title: Modeling and Analytics for Resource Allocation, Interventions, and Equity in Public Health
Date: Thursday, September 5, 2024
Time: 8:45 – 10:45 AM EST
Location: Microsoft Teams meeting (Teams link)
Meeting ID: 299 659 656 147
Passcode: w6Wtxm
Committee:
Dr. Pinar Keskinocak (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Sarah Bowden, Division of Global Migration Health, Centers for Disease Control and Prevention
Dr. Laura Edison, Division of State and Local Readiness, Centers for Disease Control and Prevention
Dr. David Goldsman, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Dima Nazzal, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Abstract
Effective, efficient, and equitable planning is crucial during public health emergencies to minimize the loss of life, health, and well-being in our communities. This thesis explores the application of modeling and analytics to enhance public health decision-making in the infectious disease space. Specifically, we evaluate the allocation of limited resources, the effectiveness of public health interventions, and racial and ethnic disparities in health outcomes during the COVID-19 pandemic and infectious disease outbreaks.
In Chapter 2, we assess a resource allocation framework when resources are limited, using the allocation of COVID-19 vaccines as a case study. We develop an extended SIR compartmental model to evaluate the benefits of using serology testing to prioritize the vaccination of those who are susceptible to infection across different pandemic scenarios. These scenarios vary by disease transmissibility, vaccine quantity and timing of availability, and serology testing capacity.
In Chapter 3, we develop an agent-based model to simulate the spread of infectious diseases on cruise ships, incorporating the demographics of both the passengers and crew members. We construct contact networks that capture and replicate interactions on ships, and ultimately, how diseases spread within this environment. We present two case studies evaluating COVID-19 and norovirus outbreaks on cruise ships. The model is used to evaluate the effectiveness of pharmaceutical and non-pharmaceutical interventions in reducing cases on board.
In Chapter 4, we examine racial and ethnic disparities in COVID-19 vaccination, deaths, and hospitalizations in the state of Georgia. We evaluate whether certain racial and ethnic groups experienced disproportionately adverse outcomes during the pandemic when stratifying by county rural/urban classification and vaccination status. This approach, which evaluates disparities at a finer geographic scale while accounting for differences in vaccination uptake across racial/ethnic groups, provides additional insights into trends observed at the national and state levels.
In Chapter 5, we analyze patterns of data missingness in COVID-19 vaccination records in Georgia. We focus on the absence of residence information for vaccine recipients and its impact on estimating vaccination coverage rates across different population subgroups. We propose and compare data imputation methods to address these gaps and improve the accuracy of vaccination coverage estimates.