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

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