A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912577371504640 |
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| author | Stone, Haley Du, Jing Xue, Hao Scotch, Matthew Heslop, David Züfle, Andreas MacIntyre, Chandini Raina Salim, Flora |
| author_facet | Stone, Haley Du, Jing Xue, Hao Scotch, Matthew Heslop, David Züfle, Andreas MacIntyre, Chandini Raina Salim, Flora |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models Stone, Haley Du, Jing Xue, Hao Scotch, Matthew Heslop, David Züfle, Andreas MacIntyre, Chandini Raina Salim, Flora Genomics Machine Learning Populations and Evolution 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. |
| title | A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models |
| topic | Genomics Machine Learning Populations and Evolution |
| url | https://arxiv.org/abs/2506.09076 |