  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.
