Phylodynamics for One Health surveillance: wastewater, metagenomics, and scalable Bayesian methods for multi-pathogen monitoring

Phylodynamics, the discipline uniting evolutionary, epidemiological, and ecological inference from genomic data, is increasingly fundamental to One Health practice. The COVID-19 pandemic illustrated its transformative capacity: near real-time reconstruction of pathogen evolution now supports outbreak response, variant tracking, and intervention design. Yet most analytical frameworks remain narrowly tailored to single-pathogen, clinic-derived datasets, while modern surveillance demands the simultaneous tracking of diverse pathogens across environmental reservoirs, hosts, and regions. Achieving this integration at the required scale confronts scientists with heterogeneous sequencing data and computational limitations that inhibit unified inference.The One Health paradigm positions human, animal, and environmental health as dynamically interconnected. Correspondingly, wastewater and environmental metagenomics have emerged as powerful signals that can anticipate outbreaks by capturing community-level, multi-pathogen genomic information. These data, while rich, challenge traditional phylodynamic assumptions due to their complexity, mixed composition, and partial sampling. Advances in scalable Bayesian methods, such as variational and online inference, GPU-accelerated likelihood computation, and machine learning priors, are transforming this landscape. The interplay between structured coalescent and birth-death models allows integration of ecological covariates, mobility data, and host dynamics, setting the stage for actionable, cross-sector surveillance. Unified, reproducible, and ethically governed computational infrastructures remain the next critical step for transforming these conceptual advances into global health intelligence tools.This Research Topic aims to consolidate methodological and applied innovations that make phylodynamic inference feasible and informative across the full One Health continuum. It seeks work integrating wastewater, metagenomic, clinical, and ecological data through scalable Bayesian frameworks to advance reproducibility, interpretability, and public health utility.To gather further insights at the intersection of evolutionary bioinformatics and genomic analysis, we welcome articles addressing, but not limited to, the following themes:- Wastewater phylodynamics: lineage deconvolution, variant quantification, and dynamic modeling from environmental samples.- Metagenomic phylodynamics: resolving co-infections, reconstructing strain-level diversity, and integrating long-read or hybrid assemblies.- Scalable Bayesian inference: GPU/variational methods, amortized posteriors, and large-scale coalescent or birth–death frameworks.- Multi-pathogen and antimicrobial resistance surveillance: evolutionary modeling of viral, bacterial, and resistance gene interactions.- Integrative One Health modeling: structured ecological and phylogeographic frameworks linking human, animal, and environmental domains.- Reproducible and ethical infrastructures: standardized data formats, federated analysis pipelines, benchmarking, and privacy-preserving phylogenetic computation.We encourage submissions connecting bioinformatics, epidemiology, ecology, and public health implementation. Topic editors: Ana Duggan, Benson R. Kidenya, Nalia Ismael, Gerald Mboowa Source: https://www.frontiersin.org/research-topics/81781/phylodynamics-for-one-health-surveillance-wastewater-metagenomics-and-scalable-bayesian-methods-for-multi-pathogen-monitoring