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The use of ambient humidity conditions to improve influenza forecast

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  • Jeffrey Shaman
  • Sasikiran Kandula
  • Wan Yang
  • Alicia Karspeck

Abstract

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1–4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.Author summary: Laboratory and epidemiological evidence indicate that atmospheric absolute humidity conditions modulate the survival, transmission, incidence and seasonality of influenza. Absolute humidity (AH) conditions are often incorporated as a forcing factor in mathematical models used to describe and forecast influenza incidence. Here we examine whether the inclusion of absolute humidity forcing improves influenza forecast accuracy. We perform retrospective influenza forecasting over 10 seasons for 95 cities using 4 different forms of AH forcing: 1) no AH forcing; 2) optimization and forecast with local climatological AH forcing; 3) optimization and forecast with local observed AH forcing; and 4) optimization with observed AH forcing and forecast with climatological AH forcing. We find that humidity forcing improves forecast performance and that forecasts generated using climatological humidity forcing generally outperform forecasts that utilize observed humidity forcing.

Suggested Citation

  • Jeffrey Shaman & Sasikiran Kandula & Wan Yang & Alicia Karspeck, 2017. "The use of ambient humidity conditions to improve influenza forecast," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-16, November.
    • Handle: RePEc:plo:pcbi00:1005844
    DOI: 10.1371/journal.pcbi.1005844
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    References listed on IDEAS

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    1. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    2. 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.
    3. Wan Yang & Alicia Karspeck & Jeffrey Shaman, 2014. "Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-15, April.
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    Cited by:

    1. Sarah C Kramer & Jeffrey Shaman, 2019. "Development and validation of influenza forecasting for 64 temperate and tropical countries," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-20, February.

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