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Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020

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  • Jingwei Li

    (School of Management, Xi’an Jiaotong University, Xi’an 710049, China
    Department of Information Systems, City University of Hong Kong, Hong Kong 999077, China)

  • Choon-Ling Sia

    (Department of Information Systems, City University of Hong Kong, Hong Kong 999077, China)

  • Zhuo Chen

    (College of Public Health, University of Georgia, Athens, GA 30602, USA
    School of Economics, University of Nottingham Ningbo China, Ningbo 315000, China)

  • Wei Huang

    (College of Business, Southern University of Science and Technology, Shenzhen 518000, China)

Abstract

Real-time online data sources have contributed to timely and accurate forecasting of influenza activities while also suffered from instability and linguistic noise. Few previous studies have focused on unofficial online news articles, which are abundant in their numbers, rich in information, and relatively low in noise. This study examined whether monitoring both official and unofficial online news articles can improve influenza activity forecasting accuracy during influenza outbreaks. Data were retrieved from a Chinese commercial online platform and the website of the Chinese National Influenza Center. We modeled weekly fractions of influenza-related online news articles and compared them against weekly influenza-like illness (ILI) rates using autoregression analyses. We retrieved 153,958,695 and 149,822,871 online news articles focusing on the south and north of mainland China separately from 6 October 2019 to 17 May 2020. Our model based on online news articles could significantly improve the forecasting accuracy, compared to other influenza surveillance models based on historical ILI rates ( p = 0.002 in the south; p = 0.000 in the north) or adding microblog data as an exogenous input ( p = 0.029 in the south; p = 0.000 in the north). Our finding also showed that influenza forecasting based on online news articles could be 1–2 weeks ahead of official ILI surveillance reports. The results revealed that monitoring online news articles could supplement traditional influenza surveillance systems, improve resource allocation, and offer models for surveillance of other emerging diseases.

Suggested Citation

  • Jingwei Li & Choon-Ling Sia & Zhuo Chen & Wei Huang, 2021. "Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020," IJERPH, MDPI, vol. 18(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6591-:d:577793
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    References listed on IDEAS

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    1. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Jan Kreft, 2019. "Instant Articles (Facebook): The Impact of Trust and Relations Among the Partners Pursuing the Strategy of Coopetition," Eurasian Studies in Business and Economics, in: Mehmet Huseyin Bilgin & Hakan Danis & Ender Demir & Ugur Can (ed.), Eurasian Business Perspectives, pages 243-253, Springer.
    4. repec:aph:ajpbhl:10.2105/ajph.2017.303767_1 is not listed on IDEAS
    5. Smolinski, M.S. & Crawley, A.W. & Baltrusaitis, K. & Chunara, R. & Olsen, J.M. & Wójcik, O. & Santillana, M. & Nguyen, A. & Brownstein, J.S., 2015. "Flu near you: Crowdsourced symptom reporting spanning 2 influenza seasons," American Journal of Public Health, American Public Health Association, vol. 105(10), pages 2124-2130.
    6. Hswen, Y. & Brownstein, J.S. & Liu, J. & Hawkins, J.B., 2017. "Use of a digital health application for influenza surveillance in China," American Journal of Public Health, American Public Health Association, vol. 107(7), pages 1130-1136.
    7. 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.
    8. Yufang Wang & Kuai Xu & Yun Kang & Haiyan Wang & Feng Wang & Adrian Avram, 2020. "Regional Influenza Prediction with Sampling Twitter Data and PDE Model," IJERPH, MDPI, vol. 17(3), pages 1-12, January.
    9. 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.
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