# EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

arXiv: https://arxiv.org/abs/2010.04452 · HTML: https://mlsanity.com/paper/2010.04452/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Goal-Conditioned Reinforcement Learning: Problems and Solutions](https://mlsanity.com/paper/2201.08299/) (2022)
- [Reinforcement Learning in Practice: Opportunities and Challenges](https://mlsanity.com/paper/2202.11296/) (2022)
- [Planning Multiple Epidemic Interventions with Reinforcement Learning](https://mlsanity.com/paper/2301.12802/) (2023)
- [A Direct Approximation of AIXI Using Logical State Abstractions](https://mlsanity.com/paper/2210.06917/) (2022)
- [Privacy Preserving Reinforcement Learning for Population Processes](https://mlsanity.com/paper/2406.17649/) (2024)
- [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)
- [Epidemic Control on a Large-Scale-Agent-Based Epidemiology Model using Deep Deterministic Policy Gradient](https://mlsanity.com/paper/2304.04475/) (2023)
- [Dynamic Knowledge Injection for AIXI Agents](https://mlsanity.com/paper/2312.16184/) (2023)
- [Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models](https://mlsanity.com/paper/2606.04562/) (2026)
- [Metric Aggregation Divergence: A Hidden Validity Threat in Agent-Based Policy Optimization and a Contractual Remedy](https://mlsanity.com/paper/2606.29038/) (2026)

## Abstract (arXiv)

Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc). Hand-designing such strategies is not trivial because of the number of possible interventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning algorithms such as deep reinforcement learning, might bring significant value. However, the specificity of each domain -- epidemic modelling or solving optimization problem -- requires strong collaborations between researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers in epidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on Q-Learning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies for dynamical on-off lock-down control under the optimization of death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choices to be made by health decision-makers.