Data-driven decision-making in public health: The role of advanced statistical models in epidemiology
1 Tritek Business Consulting, London United Kingdom.
2 College of Nursing, Xavier University, Ohio, USA.
Review
World Journal of Biology Pharmacy and Health Sciences, 2024, 19(03), 259–270.
Article DOI: 10.30574/wjbphs.2024.19.3.0629
Publication history:
Received on 01 August 2024; revised on 08 September 2024; accepted on 11 September 2024
Abstract:
This paper critically examines the transformative role of data-driven decision-making in public health, focusing on the integration of advanced statistical models in epidemiology. As the volume and complexity of health data increase, leveraging predictive analytics, machine learning, and real-time data integration has become essential for improving public health outcomes. The study explores how these technologies have shifted public health strategies from reactive to proactive approaches, particularly in areas such as disease surveillance, chronic disease management, and health equity. Through comprehensive analysis, the paper identifies key advancements, such as hybrid models that combine traditional epidemiological frameworks with AI, and the integration of multi-modal data sources that enhance predictive accuracy. The findings emphasize the potential of these models to optimize resource allocation, address health disparities, and provide timely interventions. However, challenges such as data quality, algorithmic bias, and the ethical implications of model transparency are highlighted as critical issues requiring ongoing research. The study concludes that for these models to be effectively adopted, there must be a balance between technological innovation and ethical considerations. Recommendations include the need for interdisciplinary collaboration, improved data governance frameworks, and the development of more inclusive models that are generalizable across diverse populations. This research underscores the necessity of combining robust analytical tools with ethical frameworks to enhance the reliability and equity of public health interventions.
Keywords:
Data-Driven Decision-Making; Predictive Analytics; Epidemiology; Public Health; Machine Learning; Health Equity
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0