  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.

Machine Learning;Optimization and Control;Populations and Evolution