short-paper

Public Access

Modeling epidemic spread with spike-based models

Authors:

Kathleen Hamilton,

Catherine Schuman D.Authors Info & Claims

ICONS 2020: International Conference on Neuromorphic Systems 2020

Article No.: 28, Pages 1 - 5

https://doi.org/10.1145/3407197.3407219

Published: 28 July 2020 Publication History

All formats PDF

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.

[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.

Digital 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.

[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.

[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.

[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.

[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.

[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.

[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).

[10]

Santo Fortunato. 2010. Community detection in graphs. Physics reports 486, 3 (2010), 75–174.

[11]

Santo Fortunato. 2017. Santo Fortunato’s Website: Software. https://sites.google.com/site/santofortunato/inthepress2. Accessed: 2017-05-10.

[12]

Wulfram Gerstner and Werner M Kistler. 2002. Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press.

[13]

Dan Goodman and Romain Brette. 2008. Brian: A Simulator for Spiking Neural Networks in Python. Frontiers in Neuroinformatics 2 (2008).

[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/10.1145/3183584.3183621

Digital 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.

Digital 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.

[17]

Herbert W Hethcote. 2000. The mathematics of infectious diseases. SIAM review 42, 4 (2000), 599–653.

[18]

Brian Karrer, Mark EJ Newman, and Lenka Zdeborová. 2014. Percolation on sparse networks. Physical review letters 113, 20 (2014), 208702.

[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.

Digital 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.

[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.

[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.

[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.

[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.

[25]

Cristopher Moore and Mark EJ Newman. 2000. Epidemics and percolation in small-world networks. Physical Review E 61, 5 (2000), 5678.

[26]

Romualdo Pastor-Satorras and Alessandro Vespignani. 2001. Epidemic spreading in scale-free networks. Physical review letters 86, 14 (2001), 3200.

[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.

Digital 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.

[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.

Digital 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.

Digital Library

Cited By

Vetter JDate PFahim FKulkarni SMaksymovych PTalin ATallada MVanna-iampikul PYoung ABrooks DCao YGu-Yeon WLim SLiu FMarinella MSumpter BMiniskar N(2023)Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materialsThe International Journal of High Performance Computing Applications10.1177/1094342023117853737:3-4(351-379)Online publication date: 22-Jun-2023
https://doi.org/10.1177/10943420231178537
Date PGunaratne CR. Kulkarni SPatton RColetti MPotok TPayvand MParsa MSchuman C(2023)SuperNeuro: A Fast and Scalable Simulator for Neuromorphic ComputingProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3606000(1-4)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3606000
R. Kulkarni SYoung ADate PRao Miniskar NVetter JFahim FParpillon BDickinson JTran NYoo JMills CSwartz MMaksimovic PSchuman CBean APayvand MParsa MSchuman C(2023)On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics ExperimentsProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605976(1-8)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605976
Show More Cited By

Modeling epidemic spread with spike-based models
1. Computer systems organization
  1. Architectures
    1. Other architectures

Recommendations

Epidemiological modeling of news and rumors on Twitter
SNAKDD '13: Proceedings of the 7th Workshop on Social Network Mining and Analysis

Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and ...
Recognizing sound signals through spiking neurons and spike-timing-dependent plasticity
AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

Spiking Neural Networks (SNNs) are regarded as brain-inspired neural networks. Most SNNs described spiking neurons with the leaky integrate-and-fire model, which does not incorporate biological properties of real neurons. In this paper, a model motivated ...
Complex spiking models: a role for diffuse thalamic projections in complex cortical activity
ICONIP'10: Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I

Cortical activity exhibits complex, persistent self-sustained dynamics, which is hypothesised to support the brain's sophisticated processing capabilities. Prior studies have shown how complex activity can be sustained for some time in spiking neural ...

Comments

Information & Contributors

Information

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 for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

U.S. Department of Energy

Conference

ICONS 2020

ICONS 2020: International Conference on Neuromorphic Systems 2020

July 28 - 30, 2020

TN, Oak Ridge, USA

Acceptance Rates

Overall Acceptance Rate 13 of 22 submissions, 59%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
413
Total Downloads

Downloads (Last 12 months)119
Downloads (Last 6 weeks)22

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Vetter JDate PFahim FKulkarni SMaksymovych PTalin ATallada MVanna-iampikul PYoung ABrooks DCao YGu-Yeon WLim SLiu FMarinella MSumpter BMiniskar N(2023)Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materialsThe International Journal of High Performance Computing Applications10.1177/1094342023117853737:3-4(351-379)Online publication date: 22-Jun-2023
https://doi.org/10.1177/10943420231178537
Date PGunaratne CR. Kulkarni SPatton RColetti MPotok TPayvand MParsa MSchuman C(2023)SuperNeuro: A Fast and Scalable Simulator for Neuromorphic ComputingProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3606000(1-4)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3606000
R. Kulkarni SYoung ADate PRao Miniskar NVetter JFahim FParpillon BDickinson JTran NYoo JMills CSwartz MMaksimovic PSchuman CBean APayvand MParsa MSchuman C(2023)On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics ExperimentsProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605976(1-8)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605976
Date PKulkarni SYoung ASchuman CPotok TVetter J(2023)Encoding integers and rationals on neuromorphic computers using virtual neuronScientific Reports10.1038/s41598-023-35005-x13:1Online publication date: 6-Jul-2023
https://doi.org/10.1038/s41598-023-35005-x
Date PPotok TSchuman CKay B(2022)Neuromorphic Computing is Turing-CompleteProceedings of the International Conference on Neuromorphic Systems 202210.1145/3546790.3546806(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3546790.3546806
Patton RDate PKulkarni SGunaratne CLim SCong GYoung SColetti MPotok TSchuman C(2022)Neuromorphic Computing for Scientific Applications2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA)10.1109/RSDHA56811.2022.00008(22-28)Online publication date: Nov-2022
https://doi.org/10.1109/RSDHA56811.2022.00008
Date PKulkarni SYoung ASchuman CPotok TVetter J(2022)Virtual Neuron: A Neuromorphic Approach for Encoding Numbers2022 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC57508.2022.00017(100-105)Online publication date: Dec-2022
https://doi.org/10.1109/ICRC57508.2022.00017
Schuman CKulkarni SParsa MMitchell JDate PKay B(2022)Opportunities for neuromorphic computing algorithms and applicationsNature Computational Science10.1038/s43588-021-00184-y2:1(10-19)Online publication date: 31-Jan-2022
https://doi.org/10.1038/s43588-021-00184-y
Kay BSchuman CO'Connor JDate PPotok T(2021)Neuromorphic Graph Algorithms: Cycle Detection, Odd Cycle Detection, and Max FlowInternational Conference on Neuromorphic Systems 202110.1145/3477145.3477172(1-7)Online publication date: 27-Jul-2021
https://dl.acm.org/doi/10.1145/3477145.3477172
Patton RSchuman CKulkarni SParsa MMitchell JHaas NStahl CPaulissen SDate PPotok TSnyder S(2021)Neuromorphic Computing for Autonomous RacingInternational Conference on Neuromorphic Systems 202110.1145/3477145.3477170(1-5)Online publication date: 27-Jul-2021
https://dl.acm.org/doi/10.1145/3477145.3477170
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

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

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents