IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2007-34-2.html

Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society

Author

Listed:

Abstract

EpiSimS is a massive simulation of the movements, activities, and social interactions of individuals in realistic synthetic populations, and of the dynamics of contagious disease spread on the resulting social contact network. This paper describes the assumptions and methodology in the EpiSimS model. It also describes and presents a simulation of the spatial dynamics of pandemic influenza in an artificial society constructed to match the demographics of southern California. As an example of the utility of the massive simulation approach, we demonstrate a strong correlation between local demographic characteristics and pandemic severity, which gives rise to previously unanticipated spatial pandemic hotspots. In particular, the average household size in a census tract is strongly correlated with the clinical attack rate computed by the simulation. Public heath agencies with responsibility for communities having relatively high population per household should expect to be more severely hit by a pandemic.

Suggested Citation

  • Phillip Stroud & Sara Del Valle & Stephen Sydoriak & Jane Riese & Susan Mniszewski, 2007. "Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-9.
    • Handle: RePEc:jas:jasssj:2007-34-2
    as

    Download full text from publisher

    File URL: https://www.jasss.org/10/4/9/9.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chung-Yuan Huang & Chuen-Tsai Sun & Ji-Lung Hsieh & Holin Lin, 2004. "Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(4), pages 1-2.
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Jill Bigley Dunham, 2005. "An Agent-Based Spatially Explicit Epidemiological Model in MASON," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-3.
    4. Benjamin M. Eidelson & Ian Lustick, 2004. "VIR-POX: An Agent-Based Analysis of Smallpox Preparedness and Response Policy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(3), pages 1-6.
    Full references (including those not matched with items on IDEAS)

    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics

    Citations

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


    Cited by:

    1. Geoffrey Fairchild & Kyle S. Hickmann & Susan M. Mniszewski & Sara Y. Del Valle & James M. Hyman, 2014. "Optimizing human activity patterns using global sensitivity analysis," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 394-416, December.
    2. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    3. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    4. Myong-Hun Chang & Troy Tassier, 2021. "Spatially Heterogeneous Vaccine Coverage and Externalities in a Computational Model of Epidemics," Computational Economics, Springer;Society for Computational Economics, vol. 58(1), pages 27-55, June.
    5. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    6. Kyle S Hickmann & Geoffrey Fairchild & Reid Priedhorsky & Nicholas Generous & James M Hyman & Alina Deshpande & Sara Y Del Valle, 2015. "Forecasting the 2013–2014 Influenza Season Using Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-29, May.
    7. repec:plo:pntd00:0005252 is not listed on IDEAS
    8. Dionne M. Aleman & Theodorus G. Wibisono & Brian Schwartz, 2011. "A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak," Interfaces, INFORMS, vol. 41(3), pages 301-315, June.

    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. David Fajardo & Lauren Gardner, 2013. "Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data," Networks and Spatial Economics, Springer, vol. 13(4), pages 399-426, December.
    2. Alexandru Topîrceanu, 2023. "On the Impact of Quarantine Policies and Recurrence Rate in Epidemic Spreading Using a Spatial Agent-Based Model," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    3. Wu, Tsung-Pao & Zheng, Yi & Wu, Hung-Che & Deng, Ruixin, 2024. "The causal relationship between the COVID-19, Delta and Omicron pandemic and the air transport industry: Evidence from China," Journal of Air Transport Management, Elsevier, vol. 116(C).
    4. Jeremy Hadidjojo & Siew Ann Cheong, 2011. "Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-10, July.
    5. 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.
    6. Xiao Xue & Shufang Wang & Baoyun Lu, 2015. "Computational Experiment Approach to Controlled Evolution of Procurement Pattern in Cluster Supply Chain," Sustainability, MDPI, vol. 7(2), pages 1-26, January.
    7. Mao, Liang & Yang, Yan, 2012. "Coupling infectious diseases, human preventive behavior, and networks – A conceptual framework for epidemic modeling," Social Science & Medicine, Elsevier, vol. 74(2), pages 167-175.
    8. Teruhiko Yoneyama & Sanmay Das & Mukkai Krishnamoorthy, 2012. "A Hybrid Model for Disease Spread and an Application to the SARS Pandemic," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-5.
    9. repec:plo:pone00:0045113 is not listed on IDEAS
    10. repec:plo:pone00:0043528 is not listed on IDEAS
    11. repec:plo:pone00:0014505 is not listed on IDEAS
    12. Catalina Amuedo-Dorantes & Neeraj Kaushal & Ashley N. Muchow, 2021. "Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1445-1472, October.
    13. Felipe Carozzi & Sandro Provenzano & Sefi Roth, 2024. "Urban density and COVID-19: understanding the US experience," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 72(1), pages 163-194, January.
    14. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    15. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    16. Susan M. Rogers & James Rineer & Matthew D. Scruggs & William D. Wheaton & Phillip C. Cooley & Douglas J. Roberts & Diane K. Wagener, 2014. "A Geospatial Dynamic Microsimulation Model for Household Population Projections," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 119-146.
    17. Joost R. Santos & John Frederick D. Tapia & Albert Lamberte & Christine Alyssa Solis & Raymond R. Tan & Kathleen B. Aviso & Krista Danielle S. Yu, 2022. "Uncertainty Analysis of Business Interruption Losses in the Philippines Due to the COVID-19 Pandemic," Economies, MDPI, vol. 10(8), pages 1-18, August.
    18. Jan B. Broekaert & Davide La Torre & Faizal Hafiz, 2025. "The impact of the psychological effect of infectivity on Nash-balanced control strategies for epidemic networks," Annals of Operations Research, Springer, vol. 351(3), pages 1743-1765, August.
    19. Sewon Hur, 2023. "The Distributional Effects Of Covid‐19 And Optimal Mitigation Policies," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(1), pages 261-294, February.
    20. Toxvaerd, Flavio & Rowthorn, Robert, 2022. "On the management of population immunity," Journal of Economic Theory, Elsevier, vol. 204(C).
    21. Lazebnik, Teddy, 2023. "Computational applications of extended SIR models: A review focused on airborne pandemics," Ecological Modelling, Elsevier, vol. 483(C).
    22. Lei Zheng & Miao Miao & JiYoon Lim & Maorui Li & Shu Nie & Xiaojun Zhang, 2020. "Is Lockdown Bad for Social Anxiety in COVID-19 Regions?: A National Study in The SOR Perspective," IJERPH, MDPI, vol. 17(12), pages 1-12, June.
    23. Chun-Hsiang Chan & Tzai-Hung Wen, 2021. "Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China," IJERPH, MDPI, vol. 18(12), pages 1-17, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:jas:jasssj:2007-34-2. 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: Francesco Renzini (email available below). General contact details of provider: .

    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.