IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005964.html
   My bibliography  Save this article

What to know before forecasting the flu

Author

Listed:
  • Prithwish Chakraborty
  • Bryan Lewis
  • Stephen Eubank
  • John S Brownstein
  • Madhav Marathe
  • Naren Ramakrishnan

Abstract

No abstract is available for this item.

Suggested Citation

  • Prithwish Chakraborty & Bryan Lewis & Stephen Eubank & John S Brownstein & Madhav Marathe & Naren Ramakrishnan, 2018. "What to know before forecasting the flu," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-7, October.
    • Handle: RePEc:plo:pcbi00:1005964
    DOI: 10.1371/journal.pcbi.1005964
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005964
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005964&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1005964?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roger A Morbey & Andre Charlett & Iain Lake & James Mapstone & Richard Pebody & James Sedgwick & Gillian E Smith & Alex J Elliot, 2020. "Can syndromic surveillance help forecast winter hospital bed pressures in England?," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-11, February.
    2. Prashant Rangarajan & Sandeep K Mody & Madhav Marathe, 2019. "Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-24, November.
    3. Yu-Chih Wei & Yan-Ling Ou & Jianqiang Li & Wei-Chen Wu, 2022. "Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion," Sustainability, MDPI, vol. 14(5), pages 1-24, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jeffrey S Chrabaszcz & Joe W Tidwell & Michael R Dougherty, 2017. "Crowdsourcing prior information to improve study design and data analysis," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    2. Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    3. David C Farrow & Logan C Brooks & Sangwon Hyun & Ryan J Tibshirani & Donald S Burke & Roni Rosenfeld, 2017. "A human judgment approach to epidemiological forecasting," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-19, March.
    4. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    5. 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.
    6. Arthur Novaes de Amorim & Rob Deardon & Vineet Saini, 2021. "A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-15, March.
    7. Jialiang Liu & Sumihiro Suzuki, 2022. "Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance," IJERPH, MDPI, vol. 19(6), pages 1-9, March.
    8. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    9. 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.
    10. Teresa K Yamana & Sasikiran Kandula & Jeffrey Shaman, 2017. "Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-17, November.
    11. Sequoia I Leuba & Reza Yaesoubi & Marina Antillon & Ted Cohen & Christoph Zimmer, 2020. "Tracking and predicting U.S. influenza activity with a real-time surveillance network," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-14, November.
    12. 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.
    13. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2018. "Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-29, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1005964. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.