Rony, Md Mahababul Alam and Soumik, Md Shadman and Akter, Farzana (2023) Applying Artificial Intelligence to Improve Early Detection and Containment of Infectious Disease Outbreaks, Supporting National Public Health Preparedness. Journal of Medical and Health Studies, 4 (3). ISSN 2710-1452

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Abstract

The swift transmission of infectious diseases constitutes a serious global public health problem and therefore there is a need for sophisticated tools to improve early detection and containment measures. Artificial Intelligence (AI) has become a powerful tool in the transformation of public health preparedness as a means of providing real-time intelligence, predictive modeling and data-driven interventions. This article explores the application of AI in the improvement of early detection of infectious diseases outbreaks and improved containment efforts. We go over some use cases that are important in recent studies, including AI-driven surveillance systems, predictive modeling in disease spreading, and big data analytics that works to optimize response strategies. Notably, AI has been instrumental in the face of the current pandemic, known as the Coronavirus or Covid-19, with its role in diagnostics, tracking and developing preventative measures. However, challenges, such as data privacy, cross-sector data integration and algorithmic biases, have to be addressed in order to leverage the full potential of AI. The article also speaks of incorporating AI with other technologies, like telehealth and digital health systems, in creating resilient public health infrastructures. Overall, AI is set to make a significant impact on the preparedness of a country by improving its early warning systems, decision-making, and response coordination efforts which will ultimately reduce the impact of future outbreaks.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, Infectious Disease Outbreaks, Early Detection, Public Health Preparedness, Machine Learning, Predictive Analytics, Disease Surveillance, Health System Optimization, Deep Learning, Generative AI
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	13 Oct 2026 01:20
Last Modified:	13 Oct 2026 01:20
URI:	http://eprints.umsida.ac.id/id/eprint/17197

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