A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
This work addresses the challenge of limited genomic data integration in pathogen modeling for public health and epidemiology, though it appears incremental as it builds on existing evolutionary distance methods.
The authors tackled the problem of incomplete pathogen genome sequencing by proposing a probabilistic framework to impute genetic distances between unsequenced and known cases, enabling uncertainty-aware augmentation of genomic datasets for spatiotemporal modeling, as demonstrated with avian influenza A/H5 data in wild birds.
Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced cases and known sequences within defined transmission chains, using time-aware evolutionary distance modeling. The method estimates pairwise divergence from collection dates and observed genetic distances, enabling biologically plausible imputation grounded in observed divergence patterns, without requiring sequence alignment or known transmission chains. Applied to highly pathogenic avian influenza A/H5 cases in wild birds in the United States, this approach supports scalable, uncertainty-aware augmentation of genomic datasets and enhances the integration of evolutionary information into spatiotemporal modeling workflows.