Reinforced Contact Tracing and Epidemic Intervention

TL;DR

This paper introduces IDRLECA, a reinforcement learning framework that optimizes individual-level epidemic control strategies to effectively reduce COVID-19 infections while minimizing mobility restrictions.

Contribution

It develops a novel GNN-based infection risk estimation combined with RL to create smart, efficient epidemic interventions at the individual level.

Findings

IDRLECA suppresses infections effectively.

Retains over 95% of human mobility.

Outperforms baseline strategies in simulations.

Abstract

The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility.