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Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US

Author

Listed:
  • Teddy Lazebnik

    (Department of Cancer Biology, Cancer Institute, University College London, London WC1E 6DD, UK)

  • Svetlana Bunimovich-Mendrazitsky

    (Department of Mathematics, Faculty of Natural Sciences, Ariel University, Ariel 4070000, Israel)

  • Shai Ashkenazi

    (Adelson School of Medicine, Ariel University, Ariel 4077625, Israel)

  • Eugene Levner

    (Department of Applied Mathematics, Faculty of Sciences, Holon Institute of Technology, Holon 5810201, Israel)

  • Arriel Benis

    (Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Holon 5810201, Israel
    Department of Digital Medical Technologies, Holon Institute of Technology, Holon 5810201, Israel)

Abstract

Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909, F 1 = 0.899).

Suggested Citation

  • Teddy Lazebnik & Svetlana Bunimovich-Mendrazitsky & Shai Ashkenazi & Eugene Levner & Arriel Benis, 2022. "Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US," IJERPH, MDPI, vol. 19(23), pages 1-17, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16023-:d:989350
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    References listed on IDEAS

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