EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
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.
Code (2)
Tasks
Deep Reinforcement LearningEpidemiologyEvolutionary AlgorithmsOpenAI GymQ-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)SpecificityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we devel…
BIG-bench Machine LearningEpidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel …
Bayesian OptimizationSafety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distanc…
Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning
Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. The impact of a pandemic can be minimized by enforcing certain restrictions on a community. However, while …
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningDeep reinforcement learning for large-scale epidemic control
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 process…
Computational EfficiencyDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1