1. Epidemiology and Global Health
  2. Medicine
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Conjunction of factors triggering waves of seasonal influenza

  1. Ishanu Chattopadhyay
  2. Emre Kiciman
  3. Joshua W Elliott
  4. Jeffrey L Shaman
  5. Andrey Rzhetsky  Is a corresponding author
  1. University of Chicago, United States
  2. Microsoft Research, United States
  3. Columbia University, United States
Research Article
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Cite this article as: eLife 2018;7:e30756 doi: 10.7554/eLife.30756

Abstract

Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of influenza epidemics. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions. The strongest predictor groups are as follows, ranked by importance: (1) the host population's socio- and ethno-demographic properties; (2) weather variables pertaining to specific humidity, temperature, and solar radiation; (3) the virus' antigenic drift over time; (4) the host populations land-based travel habits, and; (5) recent spatio-temporal dynamics, as reflected in the influenza wave auto-correlation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

Article and author information

Author details

  1. Ishanu Chattopadhyay

    Computation Institute, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Emre Kiciman

    Computational Epidemiology and Social Sciences, Microsoft Research, Redmont, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Joshua W Elliott

    Computation Institute, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jeffrey L Shaman

    Department of Environmental Health Sciences, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Andrey Rzhetsky

    Computation Institute, University of Chicago, Chicago, United States
    For correspondence
    arzhetsky@uchicago.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6959-7405

Funding

Defense Sciences Office, DARPA (W911NF1410333)

  • Andrey Rzhetsky

National Institutes of Health (R01HL122712)

  • Andrey Rzhetsky

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Mark Jit, London School of Hygiene & Tropical Medicine, and Public Health England, United Kingdom

Publication history

  1. Received: July 26, 2017
  2. Accepted: February 13, 2018
  3. Accepted Manuscript published: February 27, 2018 (version 1)
  4. Version of Record published: March 22, 2018 (version 2)

Copyright

© 2018, Chattopadhyay et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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    Methods: We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay.

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