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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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    References listed on IDEAS

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    1. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    2. Facundo Piguillem & Liyan Shi, 2022. "Optimal Covid-19 Quarantine and Testing Policies," The Economic Journal, Royal Economic Society, vol. 132(647), pages 2534-2562.
    3. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    4. Glenn Ellison, 2020. "Implications of Heterogeneous SIR Models for Analyses of COVID-19," NBER Working Papers 27373, National Bureau of Economic Research, Inc.
    5. 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.
    6. 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.
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    Citations

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    Cited by:

    1. L. S. Sanna Stephan, 2023. "Moment-Based Estimation of Diffusion and Adoption Parameters in Networks," Papers 2309.01489, arXiv.org.
    2. L. S. Sanna Stephan, 2023. "A Trimming Estimator for the Latent-Diffusion-Observed-Adoption Model," Papers 2309.01471, arXiv.org.
    3. Jamalzadeh, Saeed & Mettenbrink, Lily & Barker, Kash & González, Andrés D. & Radhakrishnan, Sridhar & Johansson, Jonas & Bessarabova, Elena, 2024. "Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2021. "Two Approaches to Predicting the Path of the COVID-19 Pandemic: Is One Better?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(10), pages 1-8, April.
    5. André, Keven R.A. & Arbex, Marcelo & Corrêa, Márcio V., 2023. "The economic implications of a network SIR-Macro model of epidemics," Economics Letters, Elsevier, vol. 225(C).
    6. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek, 2021. "Modeling Behavioral Responses to COVID-19," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(05), pages 1-6, March.

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