IDEAS home Printed from https://ideas.repec.org/a/sae/anname/v669y2017i1p75-92.html
   My bibliography  Save this article

Predicting Asthma Prevalence by Linking Social Media Data and Traditional Surveys

Author

Listed:
  • Hongying Dai
  • Brian R. Lee
  • Jianqiang Hao

Abstract

Asthma is one of the most common chronic diseases that has a profound impact on people’s well-being and our society. In this study, we link multiple large-scale data sources to construct an epidemiological model to predict asthma prevalence across geographic regions. We use: (1) the Social Media Monitoring (SMM) data from Twitter ( N = 500 million tweets/day), (2) the 2014 Behavioral Risk Factor Surveillance System (BRFSS) ( N = 464,664), and (3) the 2014 American Community Survey (ACS) conducted by the U.S. Census Bureau ( N = 3.5 million per year). We predict asthma prevalence in the traditional survey (BRFSS) using social media information collected from Twitter and socioeconomic factors collected from ACS. The evidence suggests that monitoring asthma-related tweets may provide real-time information that can be used to predict outcomes from traditional surveys.

Suggested Citation

  • Hongying Dai & Brian R. Lee & Jianqiang Hao, 2017. "Predicting Asthma Prevalence by Linking Social Media Data and Traditional Surveys," The ANNALS of the American Academy of Political and Social Science, , vol. 669(1), pages 75-92, January.
    • Handle: RePEc:sae:anname:v:669:y:2017:i:1:p:75-92
    DOI: 10.1177/0002716216678399
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0002716216678399
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0002716216678399?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. Whittemore, A.S. & Korn, E.L., 1980. "Asthma and air pollution in the Los Angeles area," American Journal of Public Health, American Public Health Association, vol. 70(7), pages 687-696.
    2. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    3. Oddy, W.H. & Sherriff, J.L. & De Klerk, N.H. & Kendall, G.E. & Sly, P.D. & Beilin, L.J. & Blake, K.B. & Landau, L.I. & Stanley, F.J., 2004. "The relation of breastfeeding and body mass index to asthma and atopy in children: A prospective cohort study to age 6 years," American Journal of Public Health, American Public Health Association, vol. 94(9), pages 1531-1537.
    4. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    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. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    2. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    3. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    4. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    5. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    6. Kristina Gligorić & Arnaud Chiolero & Emre Kıcıman & Ryen W. White & Robert West, 2022. "Population-scale dietary interests during the COVID-19 pandemic," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    8. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
    9. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    10. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    11. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    12. Samuel V Scarpino & James G Scott & Rosalind M Eggo & Bruce Clements & Nedialko B Dimitrov & Lauren Ancel Meyers, 2020. "Socioeconomic bias in influenza surveillance," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-19, July.
    13. 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.
    14. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    15. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    16. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    17. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    18. Han Wang & Geng Peng & Benfu Lv, 2018. "Effect of Retail Investor Attention on Chinas A-Share Market Under a Strengthening Financial Regulatory Policy," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(10), pages 1274-1297, October.
    19. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    20. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.

    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:sae:anname:v:669:y:2017:i:1:p:75-92. 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: SAGE Publications (email available below). General contact details of provider: .

    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.