Abstract

Precision public health (PPH) represents an emerging paradigm that integrates data science, epidemiology, 
genomics, and social determinants of health to deliver more targeted, equitable, and effective population-level 
interventions. Unlike traditional public health, which relies heavily on aggregated data and generalized 
interventions, PPH employs stratified, data-driven strategies to identify high-risk subpopulations and tailor 
responses accordingly. This narrative review synthesizes the conceptual foundations, data systems, analytical 
methods, governance frameworks, and implementation experiences that define precision public health. It examines 
the interplay between diverse data sources, administrative, clinical, environmental, and social, and advanced 
analytics, including predictive modeling, causal inference, clustering, and risk profiling, to enhance the precision of 
decision-making. Key focus areas include performance and equity metrics, ethical and privacy considerations, 
stakeholder engagement, and workforce capacity building. Evidence from case studies demonstrates how PPH 
enhances disease surveillance, early warning systems, targeted interventions, and precision communication. 
However, significant challenges remain, including data quality limitations, disparities in resource allocation, 
ethical dilemmas, and unequal access to emerging technologies. The review concludes that realizing the full 
potential of precision public health requires integrated governance, robust regulatory frameworks, 
multidisciplinary capacity building, and the prioritization of equity to ensure that technological advancements 
translate into tangible health gains for all population groups.