Managing emerging global healthcare threats using supply chain modeling and analytics
Creators: name: Kim, Eunseok
Publisher: name: No Publisher Supplied
Year: 2025

description: Healthcare systems are increasingly being challenged by crises, often unanticipated, requiring adaptive and effective responses. This dissertation examines two such major healthcare crises—the COVID-19 pandemic and the opioid crisis—through the lens of supply chain modeling and analytics, intending to minimize adverse effects through multi-faceted interventions.The first essay develops the Dynamic Pandemic Intervention Optimization Model (DPIOM), offering a framework for dynamic resource allocation during a pandemic. This study proposes a framework that integrates an epidemic model with a resource allocation model, resulting in an NP-hard non-convex bilinear optimization problem aimed at achieving equitable distribution of vaccines and treatments alongside efficient use of resources, contributing to improved public health outcomes. This study proposes heuristics for bilinear epidemic models that produce solutions with minimal optimality gap, enabling quantitative investigation of the influence of behavioral and operational factors—such as vaccine hesitancy, quarantine compliance, and centralized hospitalization treatment control—on intervention strategy and public health outcomes. The study validates the prioritization of elderly populations in intervention strategies, as they are most vulnerable to severe consequences, and improving quarantine compliance behavior in minimizing societal costs. In summary, the DPIOM helps establish effective intervention strategies that minimize adverse outcomes while ensuring fair access to healthcare resources, particularly for underserved populations impacted by COVID-19. The second essay analyzes the unintended consequences of restrictive supply-side interventions in the opioid crisis. By analyzing opioid-related empirical data (e.g., prescriptions, hospitalizations, overdose deaths, socioeconomic factors) from New Jersey and using the two-stage least square method to address endogeneity issues, the study argues that limiting access to prescription opioids may push users toward illicit drug abuse, contributing to increased rates of overdose, hospitalization, and death. The findings highlight the need for demand-side interventions, such as education and treatment programs, to complement supply-side restrictions and mitigate unintended adverse outcomes, especially for populations with lower education levels. Together, these two essays leverage supply chain and operations management tools for devising effective and efficient intervention strategies to tackle global health threats. This dissertation uses analytical and empirical methods and real-life data to provide rigorous and practical insights for minimizing societal harm and improving public health outcomes. It emphasizes the importance of using multiple dimensions—efficiency, equity, and population behavior—in the developed models. Further, this dissertation demonstrates the value of embedding available data during the model development process from inception. These findings contribute to developing a rigorous framework for designing robust and responsive interventions that more effectively address future healthcare challenges.