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Modeling influenza-like illnesses through composite compartmental models

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  • Levy, Nir
  • Iv, Michael
  • Yom-Tov, Elad

Abstract

Epidemiological models for the spread of pathogens in a population are usually only able to describe a single pathogen. This makes their application unrealistic in cases where multiple pathogens with similar symptoms are spreading concurrently within the same population. Here we describe a method which makes possible the application of multiple single-strain models under minimal conditions. As such, our method provides a bridge between theoretical models of epidemiology and data-driven approaches for modeling of influenza and other similar viruses.

Suggested Citation

  • Levy, Nir & Iv, Michael & Yom-Tov, Elad, 2018. "Modeling influenza-like illnesses through composite compartmental models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 288-293.
    • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:288-293
    DOI: 10.1016/j.physa.2017.12.052
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    References listed on IDEAS

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    1. Adams, Ben & Sasaki, Akira, 2009. "Antigenic distance and cross-immunity, invasibility and coexistence of pathogen strains in an epidemiological model with discrete antigenic space," Theoretical Population Biology, Elsevier, vol. 76(3), pages 157-167.
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    3. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    4. Sergey Kryazhimskiy & Ulf Dieckmann & Simon A Levin & Jonathan Dushoff, 2007. "On State-Space Reduction in Multi-Strain Pathogen Models, with an Application to Antigenic Drift in Influenza A," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-1, August.
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    Cited by:

    1. Sen Pei & Jeffrey Shaman, 2020. "Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
    2. Seroussi, Inbar & Levy, Nir & Yom-Tov, Elad, 2020. "Multi-season analysis reveals the spatial structure of disease spread," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).

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