Fair treatment allocations in social networks
This work addresses fairness in epidemic control for public health, but it is incremental as it builds on existing simulation tools without introducing new methods.
The paper tackles the fairness implications of vaccine allocation strategies in social networks during epidemics, using simulations to analyze how different approaches distribute disease burden across subgroups.
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been paid to understanding the fairness implications of different treatment strategies -- that is, how might such strategies distribute the expected disease burden differentially across various subgroups or communities in the population? In this work, we define the precision disease control problem -- the problem of optimally allocating vaccines in a social network in a step-by-step fashion -- and we use the ML Fairness Gym to simulate epidemic control and study it from both an efficiency and fairness perspective. We then present an exploratory analysis of several different environments and discuss the fairness implications of different treatment strategies.