Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard, Sch\"olkopf, Stefan Bauer

TL;DR
Pyfectious is an individual-level epidemic simulator that uses probabilistic modeling and reinforcement learning to discover optimal containment policies, demonstrating effectiveness with COVID-19 data and reducing infections.
Contribution
The paper introduces a detailed individual-level epidemic simulator with a hierarchical probabilistic framework and applies reinforcement learning to identify effective containment strategies.
Findings
The simulator accurately models COVID-19 dynamics.
Reinforcement learning finds policies reducing infections.
Proposed policies outperform baseline measures.
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…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Viral Infections and Outbreaks Research · Mental Health Research Topics
