# Reinforced Epidemic Control: Saving Both Lives and Economy

arXiv: https://arxiv.org/abs/2008.01257 · HTML: https://mlsanity.com/paper/2008.01257/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Planning Multiple Epidemic Interventions with Reinforcement Learning](https://mlsanity.com/paper/2301.12802/) (2023)
- [EpiLearn: A Python Library for Machine Learning in Epidemic Modeling](https://mlsanity.com/paper/2406.06016/) (2024)
- [H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic](https://mlsanity.com/paper/2503.10907/) (2025)
- [Reinforced Contact Tracing and Epidemic Intervention](https://mlsanity.com/paper/2102.08251/) (2021)
- [Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods](https://mlsanity.com/paper/2111.10627/) (2021)
- [Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data](https://mlsanity.com/paper/2505.04161/) (2025)
- [Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models](https://mlsanity.com/paper/2606.04562/) (2026)
- [EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models](https://mlsanity.com/paper/2610.02744/) (2026)

## Abstract (arXiv)

Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns. In this paper, we propose a solution for the life-or-economy dilemma that does not require private data. We bypass the private-data requirement by suppressing epidemic transmission through a dynamic control on inter-regional mobility that only relies on Origin-Designation (OD) data. We develop DUal-objective Reinforcement-Learning Epidemic Control Agent (DURLECA) to search mobility-control policies that can simultaneously minimize infection spread and maximally retain mobility. DURLECA hires a novel graph neural network, namely Flow-GNN, to estimate the virus-transmission risk induced by urban mobility. The estimated risk is used to support a reinforcement learning agent to generate mobility-control actions. The training of DURLECA is guided with a well-constructed reward function, which captures the natural trade-off relation between epidemic control and mobility retaining. Besides, we design two exploration strategies to improve the agent's searching efficiency and help it get rid of local optimums. Extensive experimental results on a real-world OD dataset show that DURLECA is able to suppress infections at an extremely low level while retaining 76% of the mobility in the city. Our implementation is available at https://github.com/anyleopeace/DURLECA/.