Abstract
Global health financing must continually stretch limited immunization resources across competing priorities, and Gavi, the Vaccine Alliance, plays a central role in this landscape. This work reframes Gavi’s funding decisions as a multi-objective portfolio optimization problem that jointly considers health impact, equity, cost-effectiveness, sustainability, and epidemiological interdependence. To operationalize this approach, this study develops a decision support system grounded in Modern Portfolio Theory, treating each proposal as an asset characterized by expected return and risk. Proposal returns are estimated through a heterogeneous data-fusion pipeline that generates quantitative, multi-objective scores aligned with Gavi’s strategic priorities, while epidemiological risk is quantified using a novel agent-based network model that infers cross-country outbreak spillover via an epidemiological gravity mechanism. The integrated system, combining scoring, risk modeling, and portfolio optimization, was evaluated using a full factorial experimental design spanning 9 combinations of strategic priorities, initial disease burden, and budget. The risk-aware allocation approach achieved a median 1.31% (~242,000 fewer cases in the nominal 18.5M population) reduction in new infections, with improvements up to 15.15% under severe outbreaks, while maintaining near-complete budget utilization. These findings demonstrate that explicitly incorporating epidemiological interdependence into funding allocation offers more consistent, transparent, and epidemiologically foresighted recommendations, providing a scalable quantitative baseline to complement and strengthen expert review within Gavi’s decision-making process.
Publication Date
12-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Artificial Intelligence (MS)
Department, Program, or Center
Information Sciences and Technologies
College
Golisano College of Computing and Information Sciences
Advisor
Ruben Proano
Advisor/Committee Member
Qi Yu
Advisor/Committee Member
Jamison Heard
Recommended Citation
Kumar, Ashutosh, "A Decision Support System for Global Vaccine Funding: Data-Driven Proposal Scoring, Epidemiological Risk Modeling, and Portfolio Optimization" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12369
Campus
RIT – Main Campus
