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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

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...

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Autores principales: Lazebnik, Teddy, Bunimovich-Mendrazitsky, Svetlana, Ashkenazi, Shai, Levner, Eugene, Benis, Arriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740968/
https://www.ncbi.nlm.nih.gov/pubmed/36498096
http://dx.doi.org/10.3390/ijerph192316023
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author Lazebnik, Teddy
Bunimovich-Mendrazitsky, Svetlana
Ashkenazi, Shai
Levner, Eugene
Benis, Arriel
author_facet Lazebnik, Teddy
Bunimovich-Mendrazitsky, Svetlana
Ashkenazi, Shai
Levner, Eugene
Benis, Arriel
author_sort Lazebnik, Teddy
collection PubMed
description 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).
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spelling pubmed-97409682022-12-11 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 Levner, Eugene Benis, Arriel Int J Environ Res Public Health Article 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). MDPI 2022-11-30 /pmc/articles/PMC9740968/ /pubmed/36498096 http://dx.doi.org/10.3390/ijerph192316023 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lazebnik, Teddy
Bunimovich-Mendrazitsky, Svetlana
Ashkenazi, Shai
Levner, Eugene
Benis, Arriel
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740968/
https://www.ncbi.nlm.nih.gov/pubmed/36498096
http://dx.doi.org/10.3390/ijerph192316023
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