IDEAS home Printed from https://ideas.repec.org/a/fip/fedcec/88676.html
   My bibliography  Save this article

Improving Epidemic Modeling with Networks

Author

Listed:
  • Ben R. Craig
  • Tom Phelan
  • Jan-Peter Siedlarek
  • Jared Steinberg

Abstract

Many of the models used to track, forecast, and inform the response to epidemics such as COVID-19 assume that everyone has an equal chance of encountering those who are infected with a disease. But this assumption does not reflect the fact that individuals interact mostly within much narrower groups. We argue that incorporating a network perspective, which accounts for patterns of real-world interactions, into epidemiological models provides useful insights into the spread of infectious diseases.

Suggested Citation

  • Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2020. "Improving Epidemic Modeling with Networks," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2020(23), pages 1-8, September.
    • Handle: RePEc:fip:fedcec:88676
    DOI: 10.26509/frbc-ec-202023
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-ec-202023
    File Function: Full Text
    Download Restriction: no

    References listed on IDEAS

    as
    1. Facundo Piguillem & Liyan Shi, 2020. "Optimal COVID-19 Quarantine and Testing Policies," EIEF Working Papers Series 2004, Einaudi Institute for Economics and Finance (EIEF), revised Apr 2020.
    2. Thomas House & Matt J Keeling, 2010. "The Impact of Contact Tracing in Clustered Populations," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-9, March.
    3. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    4. Glenn Ellison, 2020. "Implications of Heterogeneous SIR Models for Analyses of COVID-19," NBER Working Papers 27373, National Bureau of Economic Research, Inc.
    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. Christian L Althaus & Janneke C M Heijne & Sereina A Herzog & Adrian Roellin & Nicola Low, 2012. "Individual and Population Level Effects of Partner Notification for Chlamydia trachomatis," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-10, December.
    2. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    3. McKinley Trevelyan & Cook Alex R & Deardon Robert, 2009. "Inference in Epidemic Models without Likelihoods," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-40, July.
    4. 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.
    5. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    6. Joshua V Ross & Thomas House & Matt J Keeling, 2010. "Calculation of Disease Dynamics in a Population of Households," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-9, March.
    7. Laura Alfaro & Anusha Chari & Andrew N. Greenland & Peter K. Schott, 2020. "Aggregate and Firm-Level Stock Returns During Pandemics, in Real Time," NBER Working Papers 26950, National Bureau of Economic Research, Inc.
    8. Elizabeth Hunter & Brian Mac Namee & John Kelleher, 2018. "An open-data-driven agent-based model to simulate infectious disease outbreaks," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-35, December.
    9. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    10. 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.
    11. Guido M. Kuersteiner & Ingmar R. Prucha, 2015. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," CESifo Working Paper Series 5445, CESifo.
    12. , 2020. "COVID-19: A View from the Labor Market," Working Papers 2010, Federal Reserve Bank of Dallas.
    13. Stefano Bosi & Carmen Camacho & David Desmarchelier, 2020. "Optimal lockdown in altruistic economies," PSE Working Papers halshs-02652165, HAL.
    14. Martín Gonzalez-Eiras & Dirk Niepelt, 2020. "Optimally Controlling an Epidemic," CESifo Working Paper Series 8770, CESifo.
    15. Ofosuhene O Apenteng & Noor Azina Ismail, 2014. "The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    16. Nan Kong, 2019. "Active Game‐Based Learning of Dynamics Modeling and Simulation in Biomedical Systems Engineering," INFORMS Transactions on Education, INFORMS, vol. 20(1), pages 16-25, September.
    17. Rock, K.S. & Keeling, M.J. & Wood, D.A., 2012. "Modelling the future of the Hawaiian honeycreeper: An ecological and epidemiological problem," Ecological Modelling, Elsevier, vol. 235, pages 26-35.
    18. M Gabriela M Gomes & Marc Lipsitch & Andrew R Wargo & Gael Kurath & Carlota Rebelo & Graham F Medley & Antonio Coutinho, 2014. "A Missing Dimension in Measures of Vaccination Impacts," PLOS Pathogens, Public Library of Science, vol. 10(3), pages 1-3, March.
    19. Rhodes, Christopher A. & House, Thomas, 2013. "The rate of convergence to early asymptotic behaviour in age-structured epidemic models," Theoretical Population Biology, Elsevier, vol. 85(C), pages 58-62.
    20. Firmino, Paulo Renato Alves & de Sales, Jair Paulino & Gonçalves Júnior, Jucier & da Silva, Taciana Araújo, 2020. "A non-central beta model to forecast and evaluate pandemics time series," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    More about this item

    Keywords

    COVID-19;

    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:fip:fedcec:88676. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/frbclus.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.