  Epidemics such as COVID-19 pose serious threats to public health and our
society, and it is critical to investigate effective methods to control the
spread of epidemics over networks. Prior works on epidemic control often assume
complete knowledge of network structures, a presumption seldom valid in
real-world situations. In this paper, we study epidemic control on networks
with unknown structures, and propose a hierarchical reinforcement learning
framework for joint network structure exploration and epidemic control. To
reduce the action space and achieve computation tractability, our proposed
framework contains three modules: the Policy Selection Module, which determines
whether to explore the structure or remove nodes to control the epidemic; the
Explore Module, responsible for selecting nodes to explore; and the Remove
Module, which decides which nodes to remove to stop the epidemic spread.
Simulation results show that our proposed method outperforms baseline methods.
