Bioinformatics-driven genomic surveillance and evolutionary tracking of emerging viral pathogens

Emerging viral pathogens pose a persistent threat to global health, making genomic surveillance and evolutionary tracking essential public health tools. This review evaluates bioinformatics tools, pipelines, and analytical approaches for genomic surveillance, pathogen tracking, and real-time evolutionary biology, assessing the strengths, limitations, and pathogen-specific applicability of each. Using a cross-pathogen, comparative framework, we examine five case studies: SARS-CoV-2, Ebola virus, monkeypox virus (MPXV), avian influenza virus, and honeybee viruses, each requiring a tailored combination of whole-genome sequencing, multiple-sequence alignment, phylogenetic tree building, and molecular clock reconstruction to reconstruct transmission chains and evolutionary dynamics. Key tools discussed include Nextstrain, GISAID, BEAST, IQ-TREE, MEGA, and Pangolin. These approaches have driven tangible public health outcomes, from mRNA vaccine development for SARS-CoV-2 to universal vaccine design efforts for avian influenza, but their impact remains constrained by uneven access to sequencing infrastructure, expertise, and computational resources. Across all five case studies, no single pipeline performs best universally: tool and platform choice must instead be matched to each pathogen’s biology and the surveillance infrastructure available in a given setting. Strengthening this capacity globally, particularly in under-resourced regions, is critical to ensuring emerging pathogens are detected and characterized in time for an effective public health response.