AI-Based Detection of Emerging Biological Threats: Integrating Genomic Surveillance and Predictive Intelligence

Jemma L. Geoghegan

AI-Based Detection of Emerging Biological Threats: Integrating Genomic Surveillance and Predictive Intelligence

Author(s)	Jemma L. Geoghegan
Country	United States
Abstract	Emerging biological threats—including novel pathogens, zoonotic spillovers, antimicrobial-resistant organisms, engineered biological agents, and rapidly evolving viral variants—create substantial challenges for conventional public-health surveillance. Traditional systems often depend on confirmed clinical cases, laboratory reporting, and retrospective epidemiological investigation. Consequently, weak signals may remain undetected until transmission has become geographically dispersed. The integration of genomic surveillance with artificial intelligence offers a potential transition from reactive outbreak investigation to anticipatory biological-threat intelligence. This study develops a simulation-based framework for evaluating AI-supported biological-threat detection architectures. The proposed system combines metagenomic sequencing, whole-genome sequencing, epidemiological metadata, environmental observations, mobility indicators, anomaly detection, phylogenetic analysis, and expert validation. Four surveillance configurations are compared: sequence-only monitoring, genomics with contextual metadata, AI-integrated early warning, and AI-supported detection with expert validation. Performance is evaluated through simulated indices for early-threat sensitivity, alert specificity, operational actionability, interpretability, and response readiness. The results indicate that AI integration can substantially improve early-threat sensitivity by detecting unusual genomic and epidemiological patterns before predefined outbreak thresholds are crossed. However, fully automated warning systems may produce excessive false alerts when confronted with sequencing errors, sampling bias, laboratory contamination, incomplete metadata, or changes in surveillance intensity. The strongest overall performance emerges from a hybrid model in which machine intelligence conducts continuous signal detection while multidisciplinary experts validate biological plausibility and determine appropriate interventions. The study concludes that effective AI-enabled genomic surveillance requires not only powerful algorithms but also representative sampling, interoperable data systems, laboratory quality assurance, explainable risk scores, secure data governance, and clearly defined human accountability.
Keywords	artificial intelligence, biological threats, genomic surveillance, pathogen detection, predictive intelligence, biosecurity, machine learning, outbreak forecasting.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-22

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Based Detection of Emerging Biological Threats: Integrating Genomic Surveillance and Predictive Intelligence

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