IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2803-d760594.html
   My bibliography  Save this article

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

Author

Listed:
  • Yu-Chih Wei

    (Department of Information and Finance Management, National Taipei University of Technology, Taipei City 10608, Taiwan)

  • Yan-Ling Ou

    (Department of Information and Finance Management, National Taipei University of Technology, Taipei City 10608, Taiwan)

  • Jianqiang Li

    (Faculty of Information, Beijing University of Technology, Beijing 100124, China)

  • Wei-Chen Wu

    (Department of Finance, National Taipei University of Business, Taipei City 10051, Taiwan)

Abstract

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2803-:d:760594
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2803/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/5/2803/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Svitlana Volkova & Ellyn Ayton & Katherine Porterfield & Courtney D Corley, 2017. "Forecasting influenza-like illness dynamics for military populations using neural networks and social media," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-22, December.
    2. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    4. 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.
    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.
    6. Nungruthai Suntronwong & Preeyaporn Vichaiwattana & Sirapa Klinfueng & Sumeth Korkong & Thanunrat Thongmee & Sompong Vongpunsawad & Yong Poovorawan, 2020. "Climate factors influence seasonal influenza activity in Bangkok, Thailand," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-15, September.
    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. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    2. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    3. 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.
    4. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    5. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    6. Daniel E. O'Leary, 2024. "Toward an extended framework of exhaust data for predictive analytics: An empirical approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    7. Aksoy, Cevat Giray & Ganslmeier, Michael & Poutvaara, Panu, 2020. "Public Attention and Policy Responses to COVID-19 Pandemic," IZA Discussion Papers 13427, Institute of Labor Economics (IZA).
    8. Daniele Barchiesi & Helen Susannah Moat & Christian Alis & Steven Bishop & Tobias Preis, 2015. "Quantifying International Travel Flows Using Flickr," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-8, July.
    9. Breithaupt, Patrick & Kesler, Reinhold & Niebel, Thomas & Rammer, Christian, 2020. "Intangible capital indicators based on web scraping of social media," ZEW Discussion Papers 20-046, ZEW - Leibniz Centre for European Economic Research.
    10. JooSeok Oh & Timothy Paul Connerton & Hyun-Jung Kim, 2019. "The Rediscovery of Brand Experience Dimensions with Big Data Analysis: Building for a Sustainable Brand," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    11. 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.
    12. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    13. 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.
    14. 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.
    15. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    16. Jingwen Liu & Peng Zou & Yu Ma, 2022. "The Effect of Air Pollution on Food Preferences," Journal of the Academy of Marketing Science, Springer, vol. 50(2), pages 410-423, March.
    17. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
    18. Qian Chen & Xiang Gao & Jianming Mo & Zhouling Xu, 2022. "Market Reaction to Local Attention around Earnings Announcements in China: Evidence from Internet Search Activity," IJFS, MDPI, vol. 10(4), pages 1-26, October.
    19. Corey Lang & John David Ryder, 2016. "The effect of tropical cyclones on climate change engagement," Climatic Change, Springer, vol. 135(3), pages 625-638, April.
    20. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).

    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:gam:jsusta:v:14:y:2022:i:5:p:2803-:d:760594. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.