Monitoring COVID-19 Disease Using Big Data and Artificial Intelligence-Driven Tools

Elghamrawy, Sally M.; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1007/978-3-030-63307-3_10

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…This mechanism leads to an inherent delay in apprehension. Since early detection of a new wave is crucial to initiate appropriate measures to prevent its spread and to organize the health system accordingly [ 5 , 9 ], we suggest utilizing health communications and machine learning for earlier detection of a new pandemic wave and assessing the potential effects of certain interventions.This approach follows a wide spread usage of AI-based tools for controlling COVID-19 spread [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

Bunimovich‐Mendrazitsky

Ashkenazi

et al. 2022

IJERPH

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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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