How fast are viruses spreading in the wild?
Simon Dellicour1,2,3, Paul Bastide4, Pauline Rocu5
1Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium.
Plos Biology
|December 3, 2024
Summary
Genomic data helps reconstruct viral spread. The diffusion coefficient and isolation-by-distance (IBD) metrics accurately capture viral dispersal patterns, even with varying sample sizes.
Area of Science:
- * Phylogeography and evolutionary biology
- * Computational biology and bioinformatics
- * Epidemiology and public health
Background:
- * Genomic data from viral outbreaks enables reconstruction of viral lineage dispersal in 2D space using continuous phylogeographic inference.
- * Spatially explicit reconstructions estimate dispersal metrics, providing insights into viral spread dynamics and host-to-host transmission.
- * Heterogeneous genomic sequence sampling can affect the accuracy of phylogeographic dispersal metrics, with sampling intensity being a key factor.
Purpose of the Study:
- * To evaluate the robustness of three dispersal metrics (lineage dispersal velocity, diffusion coefficient, isolation-by-distance signal) to varying sampling intensities in continuous phylogeographic reconstructions.
- * To identify which dispersal metrics are most reliable for characterizing viral spread patterns under different sampling scenarios.
- * To compare the dispersal patterns and capacities of various viruses in animal populations using robust phylogeographic metrics.
Main Methods:
- * Utilized simulation studies to systematically assess the impact of sampling intensity (number of samples) on the accuracy of selected dispersal metrics.
- * Performed continuous phylogeographic inference on simulated genomic data under varying sampling schemes.
- * Calculated and compared lineage dispersal velocity, diffusion coefficient, and isolation-by-distance (IBD) signal metrics across different sampling intensities.
Main Results:
- * The diffusion coefficient and isolation-by-distance (IBD) signal metrics demonstrated the highest robustness to changes in sampling intensity.
- * Lineage dispersal velocity was found to be more sensitive to the number of samples included in the analysis.
- * Comparative analysis of real viral data revealed diverse IBD patterns and diffusion coefficients, reflecting host dispersal capacities and human-mediated trade impacts.
Conclusions:
- * Diffusion coefficient and IBD signal metrics are recommended for robust phylogeographic analysis of viral dispersal, particularly when sampling is uneven.
- * These metrics can effectively compare viral spread dynamics across different viruses and hosts.
- * Findings highlight the influence of host mobility and human activities on viral dispersal patterns, offering valuable insights for future epidemiological studies.
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