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Data-Driven Public Health Interventions: Analyzing the Impact of Statistical Tools on Disease Control and Prevention Strategies

Henry Ahumaraezemma Ogu 1, *, Francis Ssenabulya Ssemujju 2 and Merrera Kebeba 3

1 Department of Mathematics and Statistics, North Carolina Agricultural and Technical State University, USA.
2 Washington University in St. Louis, USA.
3 Santa Clara County Department of Public Health, USA.
 
Review
International Journal of Frontline Research in Engineering and Technology, 2026, 05(01), 009-014.
Article DOI: 10.56355/ijfret.2026.5.1.0012
Publication history: 
Received on 09 January 2026; revised on 15 February 2026; accepted on 18 February 2026
 
Abstract: 
This paper demonstrates the impact of data-driven interventions on public health in the United States. It assesses how statistical methods, including machine learning and artificial intelligence, are changing disease prevention and control. This review establishes the development and significance of integrating various data sources in public health and introduces foundational mathematical models, such as SEIR, for tracking infectious diseases. It also analyzes the role of predictive analytics in forecasting epidemics and guiding prompt responses. With real-world examples, this review shows how data analytics has augmented the management of infectious and chronic diseases, focusing on resource allocation, risk modeling, and providing support for vulnerable groups. Additionally, it explores frameworks for assessing intervention outcomes, emphasizing the importance of accountability and health integrity.  Crucial challenges such as data quality, privacy, security, and ethical considerations, including compliance with HIPAA and GDPR, are discussed alongside best practices in safeguarding health information. This review concludes by showcasing emerging technologies and stating recommendations for policymakers and public health professionals to enhance data systems, build workforce skills, and encourage collaboration, ultimately bolstering the effectiveness and equity of data-driven health strategies.
 
Keywords: 
Data; Public health; Disease control; Machine learning; AI
 
Full text article in PDF: 
 
Copyright information: 
Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0