Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases

• URL: http://arxiv.org/abs/2103.15561v1
• Date: Wed, 24 Mar 2021 10:54:46 GMT
• Title: Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
• Authors: Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Bernhard Sch\"olkopf, Stefan Bauer
• Abstract summary: We introduce a simulator capable of modeling a population structure and controlling the disease's propagation at an individualistic level. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic. The simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic.
• Score: 16.28189705178286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compartment models that divide people into a few subsets and simulate the dynamics among those subsets using hypothesized differential equations. However, these models lack the requisite granularity to study the effect of intelligent policies that influence every individual in a particular way. In this work, we introduce a simulator software capable of modeling a population structure and controlling the disease's propagation at an individualistic level. In order to estimate the confidence of the conclusions drawn from the simulator, we employ a comprehensive probabilistic approach where the entire population is constructed as a hierarchical random variable. This approach makes the inferred conclusions more robust against sampling artifacts and gives confidence bounds for decisions based on the simulation results. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic, and the outcome of a wide range of control measures is investigated. Furthermore, the simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic. The obtained experimental results indicate the simulator's adaptability and capacity in making sound predictions and a successful policy derivation example based on real-world data. As an exemplary application, our results show that the proposed policy discovery method can lead to control measures that produce significantly fewer infected individuals in the population and protect the health system against saturation.

Related papers

• 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)
• Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles [82.65261980827594]
  We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. A multi-mode predictive control approach considers the possible intentions of the human drivers.
  arXiv  Detail & Related papers  (2023-03-08T17:14:57Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Multi-scale simulation of COVID-19 epidemics [0.0]
  We are still facing the COVID-19 epidemics over a year after the start of the COVID-19 epidemics. It is hard to correctly predict its future spread over weeks to come, as well as the impacts of potential political interventions. Current epidemic models mainly fall in two approaches: compartmental models, divide the population in epidemiological classes and rely on the mathematical resolution of differential equations.
  arXiv  Detail & Related papers  (2021-12-02T12:34:11Z)
• Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous Data [1.433758865948252]
  We show that state-of-the-art models for estimating epidemiological parameters, e.g.transmission rates, can be inappropriate when faced with complex systems. We generate three complex outbreak scenarios by combining incidence curves from multiple epidemics. We evaluate two data-generating models within this Bayesian inference framework.
  arXiv  Detail & Related papers  (2021-06-20T03:41:19Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation [51.091890311312085]
  We propose a new training scheme for auto-regressive sequence generative models, which is effective and stable when operating at large sample space encountered in text generation. Our method stably outperforms Maximum Likelihood Estimation and other state-of-the-art sequence generative models in terms of both quality and diversity.
  arXiv  Detail & Related papers  (2020-07-12T15:31:24Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)
• Simulation-Based Inference for Global Health Decisions [9.850436827722419]
  We discuss recent breakthroughs in machine learning, specifically in simulation-based inference. To further stimulate research, we are developing software interfaces that turn two cornerstone COVID-19 and malaria epidemiology models into probabilistic programs.
  arXiv  Detail & Related papers  (2020-05-14T15:29:45Z)
• Planning as Inference in Epidemiological Models [15.097226158765334]
  We demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. We illustrate the use of a probabilistic programming language that automates inference in existing simulators.
  arXiv  Detail & Related papers  (2020-03-30T05:10:26Z)

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.