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Predicting social response to infectious disease outbreaks from internet-based news streams

Author

Listed:
  • Shannon M. Fast

    (The Charles Stark Draper Laboratory)

  • Louis Kim

    (The Charles Stark Draper Laboratory)

  • Emily L. Cohn

    (Boston Children’s Hospital, Harvard Medical School)

  • Sumiko R. Mekaru

    (Boston Children’s Hospital, Harvard Medical School)

  • John S. Brownstein

    (Boston Children’s Hospital, Harvard Medical School)

  • Natasha Markuzon

    (The Charles Stark Draper Laboratory)

Abstract

Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread.

Suggested Citation

  • Shannon M. Fast & Louis Kim & Emily L. Cohn & Sumiko R. Mekaru & John S. Brownstein & Natasha Markuzon, 2018. "Predicting social response to infectious disease outbreaks from internet-based news streams," Annals of Operations Research, Springer, vol. 263(1), pages 551-564, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2480-9
    DOI: 10.1007/s10479-017-2480-9
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    References listed on IDEAS

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    1. Montgomery, Jacob M. & Hollenbach, Florian M. & Ward, Michael D., 2012. "Improving Predictions using Ensemble Bayesian Model Averaging," Political Analysis, Cambridge University Press, vol. 20(3), pages 271-291, July.
    2. Vito D'Orazio & James E Yonamine, 2015. "Kickoff to Conflict: A Sequence Analysis of Intra-State Conflict-Preceding Event Structures," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-21, May.
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    Cited by:

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    2. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography," Annals of Operations Research, Springer, vol. 341(1), pages 173-195, October.
    3. Sameer Kumar & Chong Xu & Nidhi Ghildayal & Charu Chandra & Muer Yang, 2022. "Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 823-851, December.
    4. Yulan Li & Yang Wang & Kun Ma, 2022. "Integrating Transformer and GCN for COVID-19 Forecasting," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
    5. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    6. Serge Nyawa & Dieudonné Tchuente & Samuel Fosso-Wamba, 2024. "COVID-19 vaccine hesitancy: a social media analysis using deep learning," Annals of Operations Research, Springer, vol. 339(1), pages 477-515, August.

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