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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
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    File URL: https://doi.org/10.26509/frbc-ec-202023
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    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.
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    COVID-19;

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