Forecasting the Threat: Computational and Predictive Approaches to Emerging Outbreak Strain Detection and Clinical Preparedness

The accelerating emergence of novel infectious disease strains presents an urgent challenge for clinical microbiology and public health systems worldwide. Driven by pathogen evolution, zoonotic spillover, ecological disruption, and immune selection pressures, new and re-emerging threats increasingly outpace traditional reactive diagnostic and surveillance frameworks. Advances in computational biology, artificial intelligence, genomic epidemiology, and evolutionary modeling are now offering a transformative opportunity: the ability to anticipate, characterize, and clinically prepare for outbreak strains before widespread transmission occurs. Bridging the gap between predictive science and clinical practice is critical to ensuring that diagnostic tools, surveillance strategies, and therapeutic pipelines can respond with the speed and precision that modern infectious disease demands.This Research Topic invites contributions that apply and advance computational frameworks for predicting emerging and future outbreak strains, with a strong emphasis on translational relevance to clinical microbiology, diagnostics, and public health response. We welcome studies spanning phylogenetics, phylodynamics, machine learning, epidemiological forecasting, immune escape modeling, and systems-level analyses of pathogen evolution — particularly where findings have direct implications for diagnostic development, clinical decision-making, antimicrobial resistance management, vaccine strain selection, or outbreak preparedness planning. By uniting expertise from infectious disease biology, genomics, data science, and clinical microbiology, this collection aims to accelerate the development of predictive frameworks that strengthen surveillance infrastructure and inform frontline clinical and public health practice.We welcome articles addressing, but not limited to, the following themes:-Computational and AI-driven approaches for predicting the emergence and spread of novel infectious disease strains-Phylogenetic and phylodynamic analyses informing the evolutionary trajectory of clinically relevant pathogens-Machine learning models applied to genomic surveillance data for early outbreak detection and strain characterization-Epidemiological forecasting tools and their integration into clinical and public health decision-making frameworks-Predictive modeling of immune escape variants and implications for diagnostic assay performance and vaccine efficacy-Genomic and bioinformatic approaches to antimicrobial resistance surveillance and resistance evolution forecasting-Zoonotic spillover risk modeling and its application to early warning systems and clinical preparedness-Vaccine strain selection methodologies leveraging evolutionary modeling and antigenic cartography-Systems-level modeling of pathogen transmission dynamics to support outbreak response and containment strategies-Strain tracing methodologies — including both computational (dry) and laboratory-based (wet) approaches — for tracking outbreak origins, transmission chains, and pathogen dissemination-Real-world case studies demonstrating the clinical and diagnostic utility of predictive frameworks during active or historical outbreak events Topic editors: Emmanuel James San, Sikhulile Moyo Source: https://www.frontiersin.org/research-topics/81260/forecasting-the-threat-computational-and-predictive-approaches-to-emerging-outbreak-strain-detection-and-clinical-preparedness