ULTRA-HIGH-DIMENSIONAL STATISTICAL LEARNING IN INTEGRATED GENOMIC AND EPIDEMIOLOGICAL DATA

We examine how integrated genomic and epidemiological data systems improve predictive intelligence in modern disease surveillance. Using the Genomic Epidemiology Learning Model and the Integrated Global Genomic Epidemiology Dataset covering 2020 to 2025, we analyze how genomic data integration, epidemiological data linkage, and high dimensional feature extraction shape predictive epidemiological intelligence while computational infrastructure capacity conditions their analytical strength . The empirical analysis draws on integrated genomic sequencing records and epidemiological surveillance data supported by expert validation from professionals engaged in genomic analytics and public health data science in Ghana. Results show that integrated genomic datasets significantly strengthen disease risk prediction, accelerate outbreak detection, improve transmission pattern identification, and increase decision accuracy in public health systems. High dimensional feature extraction produces the strongest analytical contribution, while computational infrastructure amplifies the predictive impact of integrated data environments. We contribute a structured statistical learning framework that explains how integrated health data ecosystems generate predictive epidemiological intelligence. The findings offer global implications for strengthening genomic surveillance systems, scaling digital health infrastructure, and guiding data driven public health policy and epidemic preparedness.