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. 2022 Sep 26;8(2):veac088.
doi: 10.1093/ve/veac088. eCollection 2022.

SPREAD 4: online visualisation of pathogen phylogeographic reconstructions

Kanika D Nahata  1 , Filip Bielejec  2 , Juan Monetta  3 , Simon Dellicour  1   4 , Andrew Rambaut  5 , Marc A Suchard  6   7   8 , Guy Baele  1 , Philippe Lemey  1
Affiliations

SPREAD 4: online visualisation of pathogen phylogeographic reconstructions

Kanika D Nahata et al. Virus Evol. .

Abstract

Phylogeographic analyses aim to extract information about pathogen spread from genomic data, and visualising spatio-temporal reconstructions is a key aspect of this process. Here we present SPREAD 4, a feature-rich web-based application that visualises estimates of pathogen dispersal resulting from Bayesian phylogeographic inference using BEAST on a geographic map, offering zoom-and-filter functionality and smooth animation over time. SPREAD 4 takes as input phylogenies with both discrete and continuous location annotation and offers customised visualisation as well as generation of publication-ready figures. SPREAD 4 now features account-based storage and easy sharing of visualisations by means of unique web addresses. SPREAD 4 is intuitive to use and is available online at https://spreadviz.org, with an accompanying web page containing answers to frequently asked questions at https://beast.community/spread4.

Keywords: BEAST; Bayesian inference; phylogeography; viral spread; visualisation.

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Figures

Figure 1.
Figure 1.
Discrete phylogeographic transition history of porcine epidemic diarrhoea virus (He et al., 2022) in China. A color gradient reflects the time of the estimated transition events.
Figure 2.
Figure 2.
Discrete transition rates between countries with a posterior probability support >0.5 for SARS-CoV-2 lineage B.1.620 (Dudas et al., 2021). A color gradient reflects the Bayes factor support for the transition rates.
Figure 3.
Figure 3.
Continuous phylogeographic transition history of yellow fever virus across western Brazil (Faria et al., 2018) over four different time points. A color gradient reflects the time of the estimated dispersal events.
See this image and copyright information in PMC

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