Modeling epidemic spread with spike-based models

Kathleen Hamilton, Prasanna Date, Bill Kay, Catherine Schuman D.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

The Susceptible-Infected-Recovered/Removed model is a standard model for epidemiological spread of disease through vulnerable populations. In this paper we show how SIR network dynamics can be implemented using spiking neurons.

Original languageEnglish
Title of host publicationICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450388511
DOIs
StatePublished - Jul 28 2020
Event2020 International Conference on Neuromorphic Systems, ICONS 2020 - Virtual, Online, United States
Duration: Jul 28 2020Jul 30 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 International Conference on Neuromorphic Systems, ICONS 2020
Country/TerritoryUnited States
CityVirtual, Online
Period07/28/2007/30/20

Funding

Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
U. S. Department of Energy
U.S. Department of Energy
Office of Science
Advanced Scientific Computing ResearchDE-AC05-00OR22725
Oak Ridge National Laboratory

    Keywords

    • epidemiological modeling
    • neuromorphic algorithms
    • spiking neural networks

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