How Fast are Viruses Spreading in the Wild?
Simon Dellicour, Paul Bastide, Pauline Rocu, Denis Fargette, Olivier J. Hardy, Marc A. Suchard, Stéphane Guindon, Philippe Lemey.
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.
Introduction
Unravelling the spatiotemporal dynamics of pathogenic spread constitutes a long-standing challenge in epidemiology. When designing and implementing intervention strategies to mitigate outbreaks or endemic circulation of pathogens, evaluating the speed at which they spread and circulate within host populations can be crucial. While geo-localised infectious cases can be used to model and quantify the wavefront progression of an outbreak during its expansion phase their analysis usually provides little information about the routes taken by the underlying transmission chains.
Materials and Methods:
We first implemented a forward-in-time birth-death approach to simulate the dispersal history of viral lineages over 20 years while considering a time step of one day. In between the probability to give birth and the probability to be sampled, ongoing lineages also had the opportunity, at each time step, to move on the grid. This displacement was defined by a two-steps procedure. In both cases, the resulting displacement on the map was then randomly rotated around the previous location point.
Discussion:
We here assess 3 distinct dispersal metrics: the weighted lineage dispersal velocity (WLDV), the weighted diffusion coefficient (WDC), and an IBD signal metric that we here propose to estimate as the Pearson correlation coefficient between the patristic and log-transformed great-circle geographic distances computed for each pair of tip nodes (which is similar to the metric estimated by Pekar and colleagues.
Acknowledgments:
We are grateful to two anonymous reviewers for their constructive comments, as well as to Richard Neher for his useful feedback on a previous version of this study.
Citation: Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, et al. (2024) How fast are viruses spreading in the wild? PLoS Biol 22(12): e3002914. https://doi.org/10.1371/journal.pbio.3002914
Editor: Jonathan Dushoff, McMaster University, CANADA
Received: April 29, 2024; Accepted: October 27, 2024; Published: December 3, 2024.
Copyright: © 2024 Dellicour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: R scripts related to the analyses based on simulated and real datasets are all available, along with the associated input/output files, at https://github.com/sdellicour/dispersal_capacities. Continuous phylogeographic simulations and dispersal statistics were respectively conducted and computed using the R package “seraphim” available at https://github.com/sdellicour/seraphim (see also the updated “seraphim” tutorial on the estimation of dispersal statistics available at: https://github.com/sdellicour/seraphim/blob/master/tutorials/).
Funding: SD and PL acknowledge support from the European Union Horizon 2020 project MOOD (grant agreement n°874850). SD also acknowledges support from the Fonds National de la Recherche Scientifique (F.R.S.-FNRS, Belgium; grant n°F.4515.22), from the Research Foundation — Flanders (Fonds voor Wetenschappelijk Onderzoek — Vlaanderen, FWO, Belgium; grant n°G098321N), and from the European Union Horizon 2020 project LEAPS (grant agreement n°101094685). PR’s internship at the University of Montpellier was founded by the I-SITE MUSE through the Key Initiative “Data and Life Sciences”. MAS and PL acknowledge support from the European Union's Horizon 2020 research and innovation programme (grant agreement no. 725422-ReservoirDOCS), from the Wellcome Trust through project 206298/Z/17/Z, and from the National Institutes of Health grants R01 AI153044, R01 AI162611 and U19 AI135995. PL also acknowledges support from the Research Foundation — Flanders (Fonds voor Wetenschappelijk Onderzoek — Vlaanderen, FWO, Belgium; grants n°G0D5117N and G051322N). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
