The Design and Analysis of Algorithmic Vaccine Allocation Frameworks

Abstract

The COVID-19 pandemic starkly demonstrated the importance of effective interventions for societal functioning, particularly through timely and strategic vaccine distribution. My research develops transparent algorithmic decision support tools to help public health officials make evidence-based resource allocation decisions during pandemic responses. These tools provide guidance informed by disease models, and their development focuses on improving our understanding of how vaccine allocation can be solved as a discrete optimization problem with approximation algorithms. This work revolves around three pillars: (1) Formulate vaccine allocation discrete optimization problems. (2) Design and analyze vaccine allocation approximation algorithms. (3) Develop network disease-spread models to inform the previous pillars. My progress on each of these pillars contribute to the goal of designing fast and interpretable algorithm frameworks for vaccine allocation with high quality solutions.