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

Teddy Lazebnik; Svetlana Bunimovich-Mendrazitsky; Shai Ashkenazi; Eugene Levner; Arriel Benis

doi:10.3390/ijerph192316023

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

Teddy Lazebnik
, Svetlana Bunimovich-Mendrazitsky
, Shai Ashkenazi
, Eugene Levner
, Arriel Benis

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

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, F₁ = 0.899).

Original language	English
Article number	16023
Journal	International Journal of Environmental Research and Public Health
Volume	19
Issue number	23
DOIs	https://doi.org/10.3390/ijerph192316023
State	Published - 30 Nov 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Funding

This research was funded by a grant from Ariel University and Holon Institute of Technology, Israel: Implementation of artificial intelligence methods to improve early detection of disease outbreaks, public responses, prevention, and management.

Funders
Athlone Institute of Technology
Ariel University

Keywords

Sars-Cov-2
computer simulation
coronavirus
epidemics
epidemiologic methods
health belief model
health communication
health policy
influenza
machine learning
online social networking
pandemics
social factors
social media
time series

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/ijerph192316023

Cite this

Lazebnik, T., Bunimovich-Mendrazitsky, S., Ashkenazi, S., Levner, E., & Benis, A. (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. International Journal of Environmental Research and Public Health, 19(23), Article 16023. https://doi.org/10.3390/ijerph192316023

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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, F1 = 0.899).",

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author = "Teddy Lazebnik and Svetlana Bunimovich-Mendrazitsky and Shai Ashkenazi and Eugene Levner and Arriel Benis",

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Lazebnik, T, Bunimovich-Mendrazitsky, S, Ashkenazi, S, Levner, E & Benis, A 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', International Journal of Environmental Research and Public Health, vol. 19, no. 23, 16023. https://doi.org/10.3390/ijerph192316023

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. / Lazebnik, Teddy; Bunimovich-Mendrazitsky, Svetlana; Ashkenazi, Shai et al.
In: International Journal of Environmental Research and Public Health, Vol. 19, No. 23, 16023, 30.11.2022.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

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AU - Lazebnik, Teddy

AU - Bunimovich-Mendrazitsky, Svetlana

AU - Ashkenazi, Shai

AU - Levner, Eugene

AU - Benis, Arriel

PY - 2022/11/30

Y1 - 2022/11/30

N2 - 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, F1 = 0.899).

AB - 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, F1 = 0.899).

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KW - computer simulation

KW - coronavirus

KW - epidemics

KW - epidemiologic methods

KW - health belief model

KW - health communication

KW - health policy

KW - influenza

KW - machine learning

KW - online social networking

KW - pandemics

KW - social factors

KW - social media

KW - time series

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JO - International Journal of Environmental Research and Public Health

JF - International Journal of Environmental Research and Public Health

IS - 23

M1 - 16023

ER -

Lazebnik T, Bunimovich-Mendrazitsky S, Ashkenazi S, Levner E, Benis A. 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. International Journal of Environmental Research and Public Health. 2022 Nov 30;19(23):16023. doi: 10.3390/ijerph192316023

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

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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