Supplementary video 1. Reconstructed history of the West African Ebola virus epidemic. Map of the three most affected countries – Guinea, Liberia and Sierra Leone – is shown on the left. Colours indicate country – Guinea is green, Liberia is red and Sierra Leone is blue. Weekly incidence of EVD cases is indicated by shading of administrative divisions (darker shades correspond to more cases, on a logarithmic scale) within each country. Cases are linearly interpolated between successive reporting weeks. Inferred movements of Ebola virus are indicated with tapered projectiles, coloured by its origin country (Guinea in green, Sierra Leone in blue, Liberia in red) if lineage is crossing an international border and black otherwise. Red circles at population centroids of each administrative division indicate the number of lineages estimated to be present within the location. Phylogenetic tree in the upper right shows the relationships between sampled Ebola lineages, with branches coloured by location (lighter shades indicate locations further west within each country). Migrations inferred between any two locations in the tree are animated on the map on the left. Plot on the lower right shows the sum of weekly cases reported for each administrative division, for each individual country (Guinea in green, Sierra Leone in blue, Liberia in red). Weekly cases for individual administrative divisions are animated as changes in administrative division’s colour on the map on the left.

Abstract: The 2013–2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic ‘gravity’ model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We show why the epidemic did not spread into neighbouring countries, and show that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.

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