Reinforced Contact Tracing and Epidemic Intervention
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 4 Feb 2021.
Builds on
13 of 32 references.
References · 13
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
- 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
- 2018Link Prediction Based on Graph Neural Networkscited by 829
- 2019Mastering Complex Control in MOBA Games with Deep Reinforcement Learningcited by 150
- 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 95
- 2018Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processingcited by 13
- 2020Reinforced Epidemic Control: Saving Both Lives and Economycited by 8
- 2020Evidence of economic segregation from mobility lockdown during COVID-19 epidemiccited by 6
1 more
Built on by
Not cited yet.