skip to main content
10.1145/3407197.3407219acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiconsConference Proceedingsconference-collections
short-paper
Public Access

Modeling epidemic spread with spike-based models

Published:28 July 2020Publication History

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.

References

  1. James B. Aimone, Kathleen E. Hamilton, Susan Mniszewski, Leah Reeder, Catherine D. Schuman, and William M. Severa. 2018. Non-Neural Network Applications for Spiking Neuromorphic Hardware. In Proceedings of the Third International Workshop on Post Moore’s Era Supercomputing, Jeffery S. Vetter and Satoshi Matsuoka (Eds.). Future Technologies Group, Oak Ridge National Laboratory, Oak Ridge, TN, 24–26. Issue FTGTR-2018-11.Google ScholarGoogle Scholar
  2. Daniel F Bernardes, Matthieu Latapy, and Fabien Tarissan. 2012. Relevance of sir model for real-world spreading phenomena: Experiments on a large-scale p2p system. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 327–334.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ottar N Bjørnstad, Bärbel F Finkenstädt, and Bryan T Grenfell. 2002. Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecological monographs 72, 2 (2002), 169–184.Google ScholarGoogle Scholar
  4. Brian J Coburn, Bradley G Wagner, and Sally Blower. 2009. Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1). BMC medicine 7, 1 (2009), 30.Google ScholarGoogle Scholar
  5. Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, 2018. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 1 (2018), 82–99.Google ScholarGoogle ScholarCross RefCross Ref
  6. Michael V DeBole, Brian Taba, Arnon Amir, Filipp Akopyan, Alexander Andreopoulos, William P Risk, Jeff Kusnitz, Carlos Ortega Otero, Tapan K Nayak, Rathinakumar Appuswamy, 2019. TrueNorth: Accelerating from zero to 64 million neurons in 10 years. Computer 52, 5 (2019), 20–29.Google ScholarGoogle ScholarCross RefCross Ref
  7. Duccio Fanelli and Francesco Piazza. 2020. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals 134 (2020), 109761.Google ScholarGoogle ScholarCross RefCross Ref
  8. Neil M Ferguson, Derek AT Cummings, Christophe Fraser, James C Cajka, Philip C Cooley, and Donald S Burke. 2006. Strategies for mitigating an influenza pandemic. Nature 442, 7101 (2006), 448–452.Google ScholarGoogle ScholarCross RefCross Ref
  9. Zoltán Fodor, Sándor D Katz, and Tamás G Kovacs. 2020. Why differential equation based models fail to describe the dynamics of epidemics?arXiv preprint arXiv:2004.07208(2020).Google ScholarGoogle Scholar
  10. Santo Fortunato. 2010. Community detection in graphs. Physics reports 486, 3 (2010), 75–174.Google ScholarGoogle Scholar
  11. Santo Fortunato. 2017. Santo Fortunato’s Website: Software. https://sites.google.com/site/santofortunato/inthepress2. Accessed: 2017-05-10.Google ScholarGoogle Scholar
  12. Wulfram Gerstner and Werner M Kistler. 2002. Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press.Google ScholarGoogle Scholar
  13. Dan Goodman and Romain Brette. 2008. Brian: A Simulator for Spiking Neural Networks in Python. Frontiers in Neuroinformatics 2 (2008).Google ScholarGoogle Scholar
  14. Kathleen E. Hamilton, Neena Imam, and Travis S. Humble. 2017. Community Detection with Spiking Neural Networks for Neuromorphic Hardware. In Proceedings of the Neuromorphic Computing Symposium (Knoxville, Tennessee) (NCS ’17). ACM, New York, NY, USA, Article 9, 8 pages. https://doi.org/10.1145/3183584.3183621Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Kathleen E Hamilton, Neena Imam, and Travis S Humble. 2018. Sparse hardware embedding of spiking neuron systems for community detection. ACM Journal on Emerging Technologies in Computing Systems (JETC) 14, 4(2018), 1–13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kathleen E Hamilton and Leonid P Pryadko. 2014. Tight lower bound for percolation threshold on an infinite graph. Physical review letters 113, 20 (2014), 208701.Google ScholarGoogle Scholar
  17. Herbert W Hethcote. 2000. The mathematics of infectious diseases. SIAM review 42, 4 (2000), 599–653.Google ScholarGoogle Scholar
  18. Brian Karrer, Mark EJ Newman, and Lenka Zdeborová. 2014. Percolation on sparse networks. Physical review letters 113, 20 (2014), 208702.Google ScholarGoogle Scholar
  19. Bill Kay, Prasanna Date, and Catherine D Schuman. 2020. Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees. In Proceedings of the 8th Annual Neuro-inspired Computational Elements Workshop. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. William Ogilvy Kermack and Anderson G McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character 115, 772 (1927), 700–721.Google ScholarGoogle Scholar
  21. Muhammad Mukaram Khan, David R Lester, Luis A Plana, A Rast, Xin Jin, Eustace Painkras, and Stephen B Furber. 2008. SpiNNaker: mapping neural networks onto a massively-parallel chip multiprocessor. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). Ieee, 2849–2856.Google ScholarGoogle ScholarCross RefCross Ref
  22. Isack E Kibona and Cuihong Yang. 2017. SIR model of spread of Zika virus infections: ZIKV linked to microcephaly simulations. Health 9, 8 (2017), 1190–1210.Google ScholarGoogle ScholarCross RefCross Ref
  23. Yu A Kuznetsov and Carlo Piccardi. 1994. Bifurcation analysis of periodic SEIR and SIR epidemic models. Journal of mathematical biology 32, 2 (1994), 109–121.Google ScholarGoogle ScholarCross RefCross Ref
  24. Warwick J McKibbin and Roshen Fernando. 2020. The global macroeconomic impacts of COVID-19: Seven scenarios. CAMA Working Paper 19/2020. Australian National University.Google ScholarGoogle Scholar
  25. Cristopher Moore and Mark EJ Newman. 2000. Epidemics and percolation in small-world networks. Physical Review E 61, 5 (2000), 5678.Google ScholarGoogle ScholarCross RefCross Ref
  26. Romualdo Pastor-Satorras and Alessandro Vespignani. 2001. Epidemic spreading in scale-free networks. Physical review letters 86, 14 (2001), 3200.Google ScholarGoogle Scholar
  27. Catherine D Schuman, Kathleen Hamilton, Tiffany Mintz, Md Musabbir Adnan, Bon Woong Ku, Sung-Kyu Lim, and Garrett S Rose. 2019. Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. In Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop. ACM, 3.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Catherine D. Schuman, Kathleen E. Hamilton, Tiffany Mintz, Md Musabbir Adnan, Bon Woong Ku, Sung-Kyu Lim, and Garrett S. Rose. 2018. Shortest Path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. In Proceedings of the Third International Workshop on Post Moore’s Era Supercomputing, Jeffery S. Vetter and Satoshi Matsuoka (Eds.). Future Technologies Group, Oak Ridge National Laboratory, Oak Ridge, TN, 27–29. Issue FTGTR-2018-11.Google ScholarGoogle Scholar
  29. Jae-Seung Yeom, Abhinav Bhatele, Keith Bisset, Eric Bohm, Abhishek Gupta, Laxmikant V Kale, Madhav Marathe, Dimitrios S Nikolopoulos, Martin Schulz, and Lukasz Wesolowski. 2014. Overcoming the scalability challenges of epidemic simulations on blue waters. In 2014 IEEE 28th International Parallel and Distributed Processing Symposium. IEEE, 755–764.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Kai Zhu and Lei Ying. 2014. Information source detection in the SIR model: A sample-path-based approach. IEEE/ACM Transactions on Networking 24, 1 (2014), 408–421.Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. Modeling epidemic spread with spike-based models

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICONS 2020: International Conference on Neuromorphic Systems 2020
      July 2020
      186 pages
      ISBN:9781450388511
      DOI:10.1145/3407197

      Copyright © 2020 ACM

      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 July 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate13of22submissions,59%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format