Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning

Abstract

Infectious disease surveillance has long been vital in public health, but traditional methods often fall short in detecting emerging threats and understanding pathogen evolution. Recent advances in Next-Generation Sequencing (NGS) have revolutionized genomic surveillance, enabling near real-time monitoring of pathogens at the genetic level. This study explores the integration of real-time genomic surveillance with epidemiological models to enhance disease intervention planning. We examine how combining genomic data with models like Susceptible-Infectious-Recovered (SIR) and Susceptible-Exposed-Infectious-Recovered (SEIR) improves outbreak forecasting, facilitates early detection of new variants, and provides actionable insights for targeted interventions. The integration of NGS data allows for more precise transmission network mapping, better-informed resource allocation, and dynamic policy adjustments. However, challenges persist, including technical limitations, data privacy concerns, and equity in global surveillance capacities. The findings suggest that genomic integration enhances epidemic prediction and response but requires robust policy frameworks, equitable data-sharing practices, and continuous capacity-building efforts in low- and middle-income regions. The future of infectious disease control hinges on advancing technologies like artificial intelligence (AI), cloud computing, and machine learning to improve predictive accuracy and support real-time decision-making. This review underscores the potential of genomic surveillance to transform public health strategies and outlines key steps for effective global collaboration.