How fast are viruses spreading in the wild?
Year: 2024
Type: article
Abstract: Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics that can be informative of the dispersal dynamics and the capacity to spread among hosts. Heterogeneous sampling efforts of genomic sequences can however impact the accuracy of phylogeographic dispersal metrics. While the impact of spatial sampling bias on the outcomes of continuous phylogeographic inference has previously been explored, the impact of sampling intensity (i.e., sampling size) when aiming to characterise dispersal patterns through continuous phylogeographic reconstructions has not yet been thoroughly evaluated. In our study, we use simulations to evaluate the robustness of 3 dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance (IBD) signal metric - to the sampling intensity. Our results reveal that both the diffusion coefficient and IBD signal metrics appear to be the most robust to the number of samples considered for the phylogeographic reconstruction. We then use these 2 dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of IBD patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the use of lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.
Source: PLoS Biology
Institutions: Université Libre de Bruxelles, Vrije Universiteit Brussel, Rega Institute for Medical Research, KU Leuven, Centre National de la Recherche Scientifique, Université de Montpellier, Institut Montpelliérain Alexander Grothendieck, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Institut Agro Montpellier, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut de Recherche pour le Développement, Plant Health Institute de Montpellier, University of California, Los Angeles
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,500
Awards: Integrating spatial heterogeneity with temporal and demographic inferences, with an application to combating human rabies, MUSE, G1270632966, G1317281429, G1642488393, Real-time approach to the molecular epidemiology and evolutionary investigation and surveillance of acute viral epidemics., G2325959999, G2674448505, G2826300118, G3239578364, G339059223, Integrating Multi-Disciplinary Expertise in a Learning and Adaptive European Pandemic Preparedness System, Hidden collections: uncovering the diversity and evolutionary histories of viruses from archived specimens, G434337789, Putting genomic surveillance at the heart of viral epidemic response., G5900189728, G6020151247, G6022412921, G6154638825, MOnitoring Outbreak events for Disease surveillance in a data science context, G6823955004, G6943096680, Putting genomic surveillance at the heart of viral epidemic response., G7472199847, G7873168750, G8207891944, G8246962394, G8563367456, G9000023057, G9000079671, G9000096548, G9000125204, G9000144868, G9000153721, G9000222881, G9000317040, Center for Viral Systems Biology (CViSB)
OpenAlex ID: W4404955851