# Deep reinforcement learning for large-scale epidemic control

arXiv: https://arxiv.org/abs/2003.13676 · HTML: https://mlsanity.com/paper/2003.13676/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning](https://mlsanity.com/paper/2204.05027/) (2022)
- [Planning Multiple Epidemic Interventions with Reinforcement Learning](https://mlsanity.com/paper/2301.12802/) (2023)
- [Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning](https://mlsanity.com/paper/2106.06009/) (2021)
- [Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration](https://mlsanity.com/paper/2301.12822/) (2023)
- [Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation](https://mlsanity.com/paper/2509.20008/) (2025)
- [Optimizing Interventions for Agent-Based Infectious Disease Simulations](https://mlsanity.com/paper/2604.02016/) (2026)
- [An Analysis of Reinforcement Learning for Malaria Control](https://mlsanity.com/paper/2107.08988/) (2021)
- [Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees](https://mlsanity.com/paper/2303.12558/) (2023)
- [A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis](https://mlsanity.com/paper/2311.00855/) (2023)
- [Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic](https://mlsanity.com/paper/2312.15163/) (2023)
- [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)
- [Learning Pareto-Optimal Pandemic Intervention Policies with MORL](https://mlsanity.com/paper/2510.03340/) (2025)
- [Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning](https://mlsanity.com/paper/2511.18000/) (2025)
- [On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19](https://mlsanity.com/paper/2511.18035/) (2025)
- [Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance](https://mlsanity.com/paper/2511.21356/) (2025)
- [Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers](https://mlsanity.com/paper/2604.11507/) (2026)

## Abstract (arXiv)

Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epidemics are non-linear and complex processes. For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza. Firstly, we construct a new epidemiological meta-population model, with 379 patches (one for each administrative district in Great Britain), that adequately captures the infection process of pandemic influenza. Our model balances complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable. Secondly, we set up a ground truth such that we can evaluate the performance of the 'Proximal Policy Optimization' algorithm to learn in a single district of this epidemiological model. Finally, we consider a large-scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established. This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space. Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies.