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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
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    References listed on IDEAS

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    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.
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    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.

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