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Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data

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  • Edward Goldstein
  • Benjamin J Cowling
  • Allison E Aiello
  • Saki Takahashi
  • Gary King
  • Ying Lu
  • Marc Lipsitch

Abstract

We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.

Suggested Citation

  • Edward Goldstein & Benjamin J Cowling & Allison E Aiello & Saki Takahashi & Gary King & Ying Lu & Marc Lipsitch, 2011. "Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0023380
    DOI: 10.1371/journal.pone.0023380
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    References listed on IDEAS

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    1. Anne M Presanis & Daniela De Angelis & The New York City Swine Flu Investigation Team 3 ¶ & Angela Hagy & Carrie Reed & Steven Riley & Ben S Cooper & Lyn Finelli & Paul Biedrzycki & Marc Lipsitch, 2009. "The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis," PLOS Medicine, Public Library of Science, vol. 6(12), pages 1-12, December.
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    Cited by:

    1. Kyriaki Kalimeri & Matteo Delfino & Ciro Cattuto & Daniela Perrotta & Vittoria Colizza & Caroline Guerrisi & Clement Turbelin & Jim Duggan & John Edmunds & Chinelo Obi & Richard Pebody & Ana O Franco , 2019. "Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-21, April.

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