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.
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2022-11
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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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Lazebnik, Teddy, Svetlana Bunimovich-Mendrazitsky, Shai Ashkenazi, Eugene Levner and 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. International journal of environmental research and public health, 19(23). p. 16023. 10.3390/ijerph192316023 Retrieved from https://hdl.handle.net/10161/33092.
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Arriel Benis
Dr. Arriel Benis is a researcher and educator working at the intersection of medical informatics, digital health, and artificial intelligence, advancing health systems and biomedical engineering innovation. His work leverages AI, data science, and knowledge management to improve health-related decision-making at the individual, population, and public health levels.
His research focuses on developing data-driven healthcare solutions that enhance patient care, optimize clinical processes, and promote sustainable systems. Dr. Benis has engineered (a) clinical decision support systems with direct patient and healthcare partitioners impact such as ADHD, PTSD, and diabetes patient management and health communication, (b) MIMO -the Medical Informatics and Digital Health Multilingual Ontology- integrating more than 3500 terms and concepts across 30+ languages, actively deployed in healthcare organizations for AI-powered training and international projects support, (c) smart home and smart city health monitoring approach from a One Health viewpoint. Dr. Benis is a pioneer of the One Digital Health framework, which strategically links digital health innovation with environmental monitoring.
His past academic positions include serving as a department head and track director in biomedical and health informatics. He holds various leadership roles in the international medical informatics community, is a fellow of the International Academy for Health Sciences Informatics, and is the Editor-in-Chief of JMIR Medical Informatics. Dr. Benis is committed to training the next generation of innovators in digital health and medical informatics.
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