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Artificial Intelligence and Statistical Modeling in Public Health Informatics: Surveillance Systems, Predictive Analytics, and Equity- Centered Health Data Governance

Olamiji Temilade Onafowokan

Abstract

Public health informatics—the discipline integrating information science, epidemiology, and health systems management to improve population health—is undergoing a profound transformation driven by artificial intelligence (AI) and advanced statistical modeling. This paper provides a comprehensive, theoretically grounded, and empirically anchored review of AI and statistical applications across three core domains of public health informatics: (1) disease surveillance and outbreak detection, (2) predictive analytics for population health management, and (3) equity-centered health data governance. Drawing on theories of sociotechnical systems, social determinants of health, and information asymmetry, we synthesize evidence from over 30 peer-reviewed studies and institutional reports published between 2017 and 2026. Key statistical methods reviewed include Bayesian hierarchical models, time-series autoregressive integrated moving average frameworks, spatial scan statistics, machine learning classifiers, and causal inference techniques including propensity score matching and doubly robust estimation. Empirical findings are presented with relevant effect sizes, confidence intervals, and model performance metrics throughout. The paper introduces the Public Health AI Readiness Index , a composite scoring tool for assessing health department capacity for AI-enabled surveillance and analytics. We conclude with policy recommendations, an equity-focused research agenda, and governance principles for responsible AI deployment in public health contexts.

Keywords

public health informaticsartificial intelligencedisease surveillancepredictive analyticsBayesian methodsspatial statisticshealth equitydata governanceepidemiologymachine learning

References

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