Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, <i>Coccidioides</i>

, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

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Publication Details

Journal
Ecology and Evolution
Published
2026-04-01
DOI
https://doi.org/10.1002/ece3.73452
Primary Topic
Fungal Infections and Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

Kimberly Kaufeld, Morgan E. Gorris, Cari D. Lewis, Andrew W. Bartlow
Ecology and Evolution
Fungal Infections and Studies
article

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

Kimberly Kaufeld, Morgan E. Gorris, Cari D. Lewis, Andrew W. Bartlow
article en

Abstract

, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

Ecology and EvolutionVol. 16(4)
Los Alamos National Laboratory (US)
U.S. Department of Energy, National Nuclear Security Administration, Los Alamos National Laboratory, Triad National Security
Openalex Percentile: Top 29%
Fungal Infections and Studies
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