IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v24y2021i3d10.1007_s10729-021-09558-0.html
   My bibliography  Save this article

Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases

Author

Listed:
  • Matthew Eden

    (University of Massachusetts Amherst)

  • Rebecca Castonguay

    (University of Massachusetts Amherst)

  • Buyannemekh Munkhbat

    (University of Massachusetts Amherst)

  • Hari Balasubramanian

    (University of Massachusetts Amherst)

  • Chaitra Gopalappa

    (University of Massachusetts Amherst)

Abstract

Agent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

Suggested Citation

  • Matthew Eden & Rebecca Castonguay & Buyannemekh Munkhbat & Hari Balasubramanian & Chaitra Gopalappa, 2021. "Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases," Health Care Management Science, Springer, vol. 24(3), pages 623-639, September.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:3:d:10.1007_s10729-021-09558-0
    DOI: 10.1007/s10729-021-09558-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-021-09558-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-021-09558-0?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. I. Vieira & R. Cheng & P. Harper & V. Senna, 2010. "Small world network models of the dynamics of HIV infection," Annals of Operations Research, Springer, vol. 178(1), pages 173-200, July.
    2. Fredrik Liljeros & Christofer R. Edling & Luís A. Nunes Amaral & H. Eugene Stanley & Yvonne Åberg, 2001. "The web of human sexual contacts," Nature, Nature, vol. 411(6840), pages 907-908, June.
    3. Babak Fotouhi & Michael Rabbat, 2013. "Degree correlation in scale-free graphs," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(12), pages 1-19, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mark Tuson & Paul Harper & Daniel Gartner & Doris Behrens, 2023. "Understanding the Impact of Social Networks on the Spread of Obesity," IJERPH, MDPI, vol. 20(15), pages 1-22, July.

    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. Jing Yang & Yingwu Chen, 2011. "Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-5, July.
    2. Courtney D. Corley & Diane J. Cook & Armin R. Mikler & Karan P. Singh, 2010. "Text and Structural Data Mining of Influenza Mentions in Web and Social Media," IJERPH, MDPI, vol. 7(2), pages 1-20, February.
    3. Scotti, Marco & Bondavalli, Cristina & Bodini, Antonio, 2009. "Linking trophic positions and flow structure constraints in ecological networks: Energy transfer efficiency or topology effect?," Ecological Modelling, Elsevier, vol. 220(21), pages 3070-3080.
    4. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    5. Sander, L.M & Warren, C.P & Sokolov, I.M, 2003. "Epidemics, disorder, and percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 325(1), pages 1-8.
    6. Yeşim Güney & Yetkin Tuaç & Olcay Arslan, 2017. "Marshall–Olkin distribution: parameter estimation and application to cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2238-2250, September.
    7. László Á. Kóczy, 2022. "Core-stability over networks with widespread externalities," Annals of Operations Research, Springer, vol. 318(2), pages 1001-1027, November.
    8. Lorenzo Amir Nemati Fard & Michele Starnini & Michele Tizzoni, 2023. "Modeling adaptive forward-looking behavior in epidemics on networks," Papers 2301.04947, arXiv.org.
    9. Claes Andersson & Koen Frenken & Alexander Hellervik, 2006. "A Complex Network Approach to Urban Growth," Environment and Planning A, , vol. 38(10), pages 1941-1964, October.
    10. Benoit Mahault & Avadh Saxena & Cristiano Nisoli, 2017. "Emergent inequality and self-organized social classes in a network of power and frustration," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-23, February.
    11. Eva Enns & Margaret Brandeau, 2011. "Inferring model parameters in network-based disease simulation," Health Care Management Science, Springer, vol. 14(2), pages 174-188, June.
    12. Matteo Smerlak & Brady Stoll & Agam Gupta & James S Magdanz, 2015. "Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    13. Zhangbo Yang & Jingen Song & Shanxing Gao & Hui Wang & Yingfei Du & Qiuyue Lin, 2021. "Contact network analysis of Covid-19 in tourist areas——Based on 333 confirmed cases in China," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-13, December.
    14. M. L. Bertotti & G. Modanese, 2019. "The Bass Diffusion Model on Finite Barabasi-Albert Networks," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    15. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.
    16. Kephart, Curtis & Friedman, Daniel & Baumer, Matt, 2015. "Emergence of networks and market institutions in a large virtual economy," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2015-502, WZB Berlin Social Science Center.
    17. Jose L. Salmeron & Marisol B. Correia & Pedro R. Palos-Sanchez, 2019. "Complexity in Forecasting and Predictive Models," Complexity, Hindawi, vol. 2019, pages 1-3, June.
    18. Susanne F Awad & Sema K Sgaier & Bushimbwa C Tambatamba & Yousra A Mohamoud & Fiona K Lau & Jason B Reed & Emmanuel Njeuhmeli & Laith J Abu-Raddad, 2015. "Investigating Voluntary Medical Male Circumcision Program Efficiency Gains through Subpopulation Prioritization: Insights from Application to Zambia," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-25, December.
    19. Mirjam Kretzschmar & Jacco Wallinga, 2007. "Editorial," Mathematical Population Studies, Taylor & Francis Journals, vol. 14(4), pages 203-209, November.
    20. Benjamin Armbruster & Ekkehard Beck & Mustafa Waheed, 2014. "The importance of extended high viremics in models of HIV spread in South Africa," Health Care Management Science, Springer, vol. 17(2), pages 182-193, June.

    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:kap:hcarem:v:24:y:2021:i:3:d:10.1007_s10729-021-09558-0. 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.