IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v96y2023i2d10.1140_epjb_s10051-023-00484-4.html
   My bibliography  Save this article

Multi-layer network approach in modeling epidemics in an urban town

Author

Listed:
  • Meliksah Turker

    (Bogazici University)

  • Haluk O. Bingol

    (Bogazici University)

Abstract

The last three years have been an extraordinary time with the COVID-19 pandemic killing millions, affecting and distressing billions of people worldwide. Authorities took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to mitigate the negative impact of the epidemics, researchers tried to estimate the future of the pandemic for different scenarios, using forecasting techniques and epidemics simulations on networks. Intending to better represent the real-life in an urban town in high resolution, we propose a novel multi-layer network model, where each layer corresponds to a different interaction that occurs daily, such as “household”, “work” or “school”. Our simulations indicate that locking down “friendship” layer has the highest impact on slowing down epidemics. Hence, our contributions are twofold, first we propose a parametric network generator model; second, we run SIR simulations on it and show the impact of layers. Graphic Abstract

Suggested Citation

  • Meliksah Turker & Haluk O. Bingol, 2023. "Multi-layer network approach in modeling epidemics in an urban town," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-13, February.
    • Handle: RePEc:spr:eurphb:v:96:y:2023:i:2:d:10.1140_epjb_s10051-023-00484-4
    DOI: 10.1140/epjb/s10051-023-00484-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-023-00484-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1140/epjb/s10051-023-00484-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Jessica T. Davis & Matteo Chinazzi & Nicola Perra & Kunpeng Mu & Ana Pastore y Piontti & Marco Ajelli & Natalie E. Dean & Corrado Gioannini & Maria Litvinova & Stefano Merler & Luca Rossi & Kaiyuan Su, 2021. "Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave," Nature, Nature, vol. 600(7887), pages 127-132, December.
    3. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    4. Sun, Qingyi & Wang, Zhishuang & Zhao, Dawei & Xia, Chengyi & Perc, Matjaž, 2022. "Diffusion of resources and their impact on epidemic spreading in multilayer networks with simplicial complexes," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    5. Atdag, Samet & Bingol, Haluk O., 2021. "Computational models for commercial advertisements in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    7. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
    8. Christel Kamp & Mathieu Moslonka-Lefebvre & Samuel Alizon, 2013. "Epidemic Spread on Weighted Networks," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-10, December.
    9. Shuo Feng & Zebang Feng & Chen Ling & Chen Chang & Zhongke Feng, 2021. "Prediction of the COVID-19 epidemic trends based on SEIR and AI models," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-15, January.
    10. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    11. Nina Haug & Lukas Geyrhofer & Alessandro Londei & Elma Dervic & Amélie Desvars-Larrive & Vittorio Loreto & Beate Pinior & Stefan Thurner & Peter Klimek, 2020. "Ranking the effectiveness of worldwide COVID-19 government interventions," Nature Human Behaviour, Nature, vol. 4(12), pages 1303-1312, December.
    12. Qi Su & Alex McAvoy & Yoichiro Mori & Joshua B. Plotkin, 2022. "Evolution of prosocial behaviours in multilayer populations," Nature Human Behaviour, Nature, vol. 6(3), pages 338-348, March.
    13. Marián Boguñá & Fragkiskos Papadopoulos & Dmitri Krioukov, 2010. "Sustaining the Internet with hyperbolic mapping," Nature Communications, Nature, vol. 1(1), pages 1-8, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martijn Warnier & Vincent Alkema & Tina Comes & Bartel Walle, 2020. "Humanitarian access, interrupted: dynamic near real-time network analytics and mapping for reaching communities in disaster-affected countries," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 815-834, September.
    2. Cheng, Le & Li, Xianghua & Han, Zhen & Luo, Tengyun & Ma, Lianbo & Zhu, Peican, 2022. "Path-based multi-sources localization in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    3. Xu, Yuan-Hao & Wang, Hao-Jie & Lu, Zhong-Wen & Hu, Mao-Bin, 2023. "Impact of awareness dissemination on epidemic reaction–diffusion in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    4. Yan, Li & Cao, Huiying & Gao, Chao & Wang, Zhen & Li, Xuelong, 2023. "Mining of book-loan behavior based on coupling relationship analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    5. Liu, Xiaoxiao & Sun, Shiwen & Wang, Jiawei & Xia, Chengyi, 2019. "Onion structure optimizes attack robustness of interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Ma, Lili, 2019. "Studying node centrality based on the hidden hyperbolic metric space of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 426-434.
    7. Peng, Peng & Poon, Jessie P.H. & Yang, Yu & Lu, Feng & Cheng, Shifen, 2019. "Global oil traffic network and diffusion of influence among ports using real time data," Energy, Elsevier, vol. 172(C), pages 333-342.
    8. William Schueller & Christian Diem & Melanie Hinterplattner & Johannes Stangl & Beate Conrady & Markus Gerschberger & Stefan Thurner, 2022. "Propagation of disruptions in supply networks of essential goods: A population-centered perspective of systemic risk," Papers 2201.13325, arXiv.org.
    9. Alexandru Topîrceanu, 2022. "Benchmarking Cost-Effective Opinion Injection Strategies in Complex Networks," Mathematics, MDPI, vol. 10(12), pages 1-16, June.
    10. Blazquez-Soriano, Amparo & Ramos-Sandoval, Rosmery, 2022. "Information transfer as a tool to improve the resilience of farmers against the effects of climate change: The case of the Peruvian National Agrarian Innovation System," Agricultural Systems, Elsevier, vol. 200(C).
    11. Martin L. Weitzman, 2015. "A Voting Architecture for the Governance of Free-Driver Externalities, with Application to Geoengineering," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(4), pages 1049-1068, October.
    12. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    13. Sanjeev Goyal & Fernando Vega-Redondo, 2000. "Learning, Network Formation and Coordination," Econometric Society World Congress 2000 Contributed Papers 0113, Econometric Society.
    14. Tomasz Jałowiec & Henryk Wojtaszek, 2021. "Analysis of the RES Potential in Accordance with the Energy Policy of the European Union," Energies, MDPI, vol. 14(19), pages 1-33, September.
    15. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    16. Quayle, A.P. & Siddiqui, A.S. & Jones, S.J.M., 2006. "Preferential network perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 823-840.
    17. Chen, Lei & Yue, Dong & Dou, Chunxia, 2019. "Optimization on vulnerability analysis and redundancy protection in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1216-1226.
    18. Marco Minozzo & Luca Bagnato, 2021. "A unified skew‐normal geostatistical factor model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    19. Guo Weilong & Minca Andreea & Wang Li, 2016. "The topology of overlapping portfolio networks," Statistics & Risk Modeling, De Gruyter, vol. 33(3-4), pages 139-155, December.
    20. Bálint Mészáros & István Simon & Zsuzsanna Dosztányi, 2009. "Prediction of Protein Binding Regions in Disordered Proteins," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurphb:v:96:y:2023:i:2:d:10.1140_epjb_s10051-023-00484-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.