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

• URL: http://arxiv.org/abs/2010.04452v1
• Date: Fri, 9 Oct 2020 09:25:41 GMT
• Title: EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
• Authors: C\'edric Colas, Boris Hejblum, S\'ebastien Rouillon, Rodolphe Thi\'ebaut, Pierre-Yves Oudeyer, Cl\'ement Moulin-Frier and M\'elanie Prague
• Abstract summary: EpidemiOptim is a Python toolbox that facilitates collaborations between researchers in epidemiology and optimization. It turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners. 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.
• Score: 12.748861129923348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• On the calibration of compartmental epidemiological models [4.2456818663079865]
  We present an overview of calibrating strategies that can be employed, including several optimization methods and reinforcement learning. We discuss the benefits and drawbacks of these methods and highlight relevant practical conclusions from our experiments. Further research is needed to validate the effectiveness and scalability of these approaches in different epidemiological contexts.
  arXiv  Detail & Related papers  (2023-12-09T03:57:06Z)
• Epidemic Control on a Large-Scale-Agent-Based Epidemiology Model using Deep Deterministic Policy Gradient [0.7244731714427565]
  lockdowns, rapid vaccination programs, school closures, and economic stimulus can have positive or unintended negative consequences. Current research to model and determine an optimal intervention automatically through round-tripping is limited by the simulation objectives, scale (a few thousand individuals), model types that are not suited for intervention studies, and the number of intervention strategies they can explore (discrete vs continuous). We address these challenges using a Deep Deterministic Policy Gradient (DDPG) based policy optimization framework on a large-scale (100,000 individual) epidemiological agent-based simulation.
  arXiv  Detail & Related papers  (2023-04-10T09:26:07Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series [1.9371782627708491]
  We propose a new high dimensional Bayesian Optimization algorithm combining Recurrent neural networks. The proposed RNN-BO algorithm can solve the optimal control problems in the lower dimension space. We also discuss the impacts of different numbers of the RNN layers and training epochs on the trade-off between solution quality and related computational efforts.
  arXiv  Detail & Related papers  (2022-01-01T08:40:17Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
  Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. BaBSim.Hospital is a tool for capacity planning based on discrete event simulation. We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
  arXiv  Detail & Related papers  (2021-05-16T12:38:35Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models [33.88734751290751]
  We propose a new approach for obtaining optimal policy recommendations based on epidemiological models. We find that such a look-ahead strategy infers non-trivial policies that adhere well to the constraints specified.
  arXiv  Detail & Related papers  (2020-10-16T16:27:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.