Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread

Abstract

The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such disparities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.

A Survey on Optimization and Machine Learning-Based Fair Decision Making in Healthcare

“…Mixed-integer programming. Mixed-integer programming has been widely used to ensure fairness in decision making [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77]. Emergency department overcrowding has become a nationwide crisis over the last decade [62].…”