AI-Based Genomic Surveillance: Strengthening Preparedness for Emerging Infectious Diseases

Carolyn Stein

AI-Based Genomic Surveillance: Strengthening Preparedness for Emerging Infectious Diseases

Author(s)	Carolyn Stein
Country	United States
Abstract	The emergence and rapid spread of infectious diseases demonstrate the need for surveillance systems capable of detecting biological threats before they develop into large-scale public health emergencies. Conventional surveillance approaches often depend on clinical reporting, laboratory confirmation and retrospective epidemiological analysis, which can delay recognition of novel pathogens and emerging variants. AI-based genomic surveillance combines pathogen sequencing, bioinformatics, machine learning, epidemiological intelligence and real-time data integration to create more responsive approaches to infectious disease monitoring. This paper examines the role of artificial intelligence in strengthening genomic surveillance for emerging infectious diseases, with particular attention to pathogen detection, genomic classification, variant identification, mutation analysis, transmission monitoring, outbreak forecasting and genomic epidemiology. It proposes an integrated AI-enabled surveillance framework connecting clinical laboratories, sequencing platforms, genomic databases, epidemiological systems and public health authorities. Machine learning and deep learning can assist with sequence classification, anomaly detection and prediction of evolutionary patterns, while natural language processing can integrate information from scientific publications, clinical reports and other epidemiological sources. The paper also examines challenges involving genomic data quality, sampling bias, model interpretability, privacy, cybersecurity, infrastructure, interoperability and equitable access to sequencing technologies. The study argues that AI should complement rather than replace laboratory science and epidemiological expertise. A future-ready genomic surveillance ecosystem will require continuous sequencing, responsible AI, interoperable data infrastructure, transparent governance and rapid translation of genomic signals into public health action.
Keywords	Genomic Surveillance, Artificial Intelligence, Infectious Diseases, Pathogen Genomics, Machine Learning, Epidemiology, Variant Detection, Outbreak Prediction, Public Health Intelligence, Bioinformatics.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-16

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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 Genomic Surveillance: Strengthening Preparedness for Emerging Infectious Diseases

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