Fair treatment allocations in social networks

TL;DR

This paper explores the fairness implications of vaccine allocation strategies in social networks, using simulations to analyze how different approaches impact equitable disease burden distribution.

Contribution

It introduces the precision disease control problem and employs the ML Fairness Gym to evaluate treatment strategies from efficiency and fairness perspectives.

Findings

Different treatment strategies have varying fairness impacts.

Simulation results highlight trade-offs between efficiency and fairness.

Analysis of environments reveals key fairness considerations.

Abstract

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.