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Regional Influenza Prediction with Sampling Twitter Data and PDE Model

Author

Listed:
  • Yufang Wang

    (School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China)

  • Kuai Xu

    (School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069, USA)

  • Yun Kang

    (Science and Mathematics Faculty, Arizona State University, Mesa, AZ 85212, USA)

  • Haiyan Wang

    (School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069, USA)

  • Feng Wang

    (School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069, USA)

  • Adrian Avram

    (School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069, USA)

Abstract

The large volume of geotagged Twitter streaming data on flu epidemics provides chances for researchers to explore, model, and predict the trends of flu cases in a timely manner. However, the explosive growth of data from social media makes data sampling a natural choice. In this paper, we develop a method for influenza prediction based on the real-time tweet data from social media, and this method ensures real-time prediction and is applicable to sampling data. Specifically, we first simulate the sampling process of flu tweets, and then develop a specific partial differential equation (PDE) model to characterize and predict the aggregated flu tweet volumes. Our PDE model incorporates the effects of flu spreading, flu recovery, and active human interventions for reducing flu. Our extensive simulation results show that this PDE model can almost eliminate the data reduction effects from the sampling process: It requires lesser historical data but achieves stronger prediction results with a relative accuracy of over 90% on the 1% sampling data. Even for the more aggressive data sampling ratios such as 0.1% and 0.01% sampling, our model is still able to achieve relative accuracies of 85% and 83%, respectively. These promising results highlight the ability of our mechanistic PDE model in predicting temporal–spatial patterns of flu trends even in the scenario of small sampling Twitter data.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:678-:d:311385
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    References listed on IDEAS

    as
    1. Chen, Zhenwu & Xu, Zhiting, 2019. "A delayed diffusive influenza model with two-strain and two vaccinations," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 439-453.
    2. 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.
    3. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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    Cited by:

    1. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    2. 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.
    3. Li, Ruqi & Song, Yurong & Wang, Haiyan & Jiang, Guo-Ping & Xiao, Min, 2023. "Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    4. Tian-Shyug Lee & I-Fei Chen & Ting-Jen Chang & Chi-Jie Lu, 2020. "Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme," IJERPH, MDPI, vol. 17(13), pages 1-15, July.
    5. Umar Albalawi & Mohammed Mustafa, 2022. "Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review," IJERPH, MDPI, vol. 19(10), pages 1-24, May.

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