Active Screening for Recurrent Diseases: A Reinforcement Learning Approach. (arXiv:2101.02766v1 [cs.LG])

Active screening is a common approach in controlling the spread of recurring
infectious diseases such as tuberculosis and influenza. In this approach,
health workers periodically select a subset of population for screening.
However, given the limited number of health workers, only a small subset of the
population can be visited in any given time period. Given the recurrent nature
of the disease and rapid spreading, the goal is to minimize the number of
infections over a long time horizon. Active screening can be formalized as a
sequential combinatorial optimization over the network of people and their
connections. The main computational challenges in this formalization arise from
i) the combinatorial nature of the problem, ii) the need of sequential planning
and iii) the uncertainties in the infectiousness states of the population.

Previous works on active screening fail to scale to large time horizon while
fully considering the future effect of current interventions. In this paper, we
propose a novel reinforcement learning (RL) approach based on Deep Q-Networks
(DQN), with several innovative adaptations that are designed to address the
above challenges. First, we use graph convolutional networks (GCNs) to
represent the Q-function that exploit the node correlations of the underlying
contact network. Second, to avoid solving a combinatorial optimization problem
in each time period, we decompose the node set selection as a sub-sequence of
decisions, and further design a two-level RL framework that solves the problem
in a hierarchical way. Finally, to speed-up the slow convergence of RL which
arises from reward sparseness, we incorporate ideas from curriculum learning
into our hierarchical RL approach. We evaluate our RL algorithm on several
real-world networks.



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