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 address why the epidemic did not spread into neighbouring countries, showing 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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Extended data figures and tables
Extended Data Figures
- Extended Data Figure 1: Distribution and correlation of EVD cases and EBOV sequences. (484 KB)
a, Administrative regions within Guinea (green), Sierra Leone (blue) and Liberia (red); shading is proportional to the cumulative number of known and suspected EVD cases in each region. Darkest shades represent 784 cases for Guinea (Macenta prefecture); 3,219 cases for Sierra Leone (Western Area urban district); and 2,925 cases for Liberia (Montserrado county); hatching indicate regions without reported EVD cases. Circle diameters are proportional to the number of EBOV genomes available from that region over the entire EVD epidemic with the largest circle representing 152 sequences. Crosses mark regions for which no sequences are available. Circles and crosses are positioned at population centroids within each region. b, A plot of number of EBOV genomes sampled against the known and suspected cumulative EVD case numbers. Regions in Guinea are denoted in green, Sierra Leone in blue and Liberia in red. Spearman correlation coefficient: 0.93.
- Extended Data Figure 2: Dispersal of virus lineages over time. (548 KB)
Virus dispersal between administrative regions estimated using the GLM phylogeography model (see Methods). The arcs are between population centroids of each region, show directionality from the thin end to the thick end and are coloured in a scale denoting time from December 2013 in blue to October 2015 in yellow. Countries are coloured with Liberia in red, Guinea in green and Sierra Leone in blue.
- Extended Data Figure 3: Inference of GLM predictors in a ‘real-time’ context. (166 KB)
For the dataset constructed from EBOV genome sequences derived from samples taken up until October 2014 (blue), the same 5 spatial EBOV movement predictors were given categorical support (inclusion probabilities = 1.0) as for the full dataset (red). Likewise, the coefficients for these predictors are consistent in their sign and magnitude.
- Extended Data Figure 4: The effect of borders on EBOV migration rates between regions. (129 KB)
Posterior densities for the migration rates between locations that share a geographical border and those that do not share borders for international migrations and national migrations. Where two regions share a border (right y axis), national migrations are only marginally more frequent than international migrations showing that both types of borders are porous to short local movement. Where the two regions are not adjacent (left y axis), international migrations are much rarer than national migrations.
- Extended Data Figure 5: Summarized international migration history of the epidemic. (718 KB)
a, b, All viral movement events between countries (Guinea, green; Sierra Leone, blue; Liberia, red) are shown split by whether they are between regions that are geographically distant (a) or regions that share the international border (b). Curved lines indicate median (intermediate colour intensity), and 95% highest posterior density intervals (lightest and darkest colour intensities) for the number of migrations that are inferred to have taken place between countries.
- Extended Data Figure 6: Comparison of predicted and observed numbers of introductions and case numbers. (474 KB)
a, b, Left, scatter plots show inferred introduction numbers (a) or observed case numbers (b), coloured by region as in Extended Data Fig. 1. Administrative regions that did not report any cases are indicated with empty circles on the scatter plot. Right, administrative regions on the map are coloured by the residuals (as observed/predicted) of the scatter plot. Regions are coloured grey where 0.5 < observed/predicted < 2.0 and transition into red or blue colours for overestimation or underestimation, respectively.
- Extended Data Figure 7: Region-specific introductions, cluster sizes and persistence. (632 KB)
Each row summarizes independent introductions and the sizes (as numbers of sequences) of resulting outbreak clusters. Clusters are coloured by their inferred region of origin (colours are the same as in Extended Data Fig. 1). The horizontal lines represent the persistence of each cluster from the time of introduction to the last sampled case (individual tips have persistence 0). The areas of the circles in the middle of the lines are proportional to the number of sequenced cases in the cluster. The areas of the circles next to the labels on the left represent the population sizes of each administrative region. Vertical lines within each cell indicate the dates of declared border closures by each of the three countries: 11 June 2014 in Sierra Leone (blue), 27 July 2014 in Liberia (red) and 09 August 2014 in Guinea (green).
- Extended Data Figure 8: Kernel density estimates for inferred epidemiological statistics. (259 KB)
From top to bottom, distance travelled (distance between population centroids, in kilometres); number of introductions that each location experienced; cluster size (number of sequences collected in a location as a result of a single introduction); cluster persistence (days from the common ancestor of a cluster to its last descendent, single tips have persistence of 0. Left, analysis for Sierra Leone (blue), Liberia (red) and Guinea (green). Right, analysis for before October 2014 (grey) and after October 2014 (orange). Points with vertical lines connected to the x axis indicate the 50% and 95% quantiles of the parameter density estimates. Within Sierra Leone, Liberia and Guinea, 50% of all migrations occurred over distances of around 100 km and persisted for around 25 days. Exceptions were for Sierra Leone, which experienced more introductions per location (around 12) than Guinea and Liberia (around 4); and Guinea, where migrations tended to occur over larger distances owing to the size of the country and whose cluster sizes following introductions tended to be lower (3 sequences versus Liberia and Sierra Leone, which had 5 sequences each). Between the first (grey) and second (orange) years of the epidemic there were considerable reductions in cluster persistence, cluster sizes and distances travelled by viruses, whereas dispersal intensity remained largely the same.
- Extended Data Figure 9: Relationship between cluster size, introductions or persistence and population size. (502 KB)
a, The mean number of introductions into each location against (log) population sizes. The Western Area (in Sierra Leone) received the most introductions, whereas Conakry and Montserrado were closer to the average. The association between population size and the number of introductions was not very strong (R2 = 0.28, Pearson correlation = 0.54, Spearman correlation = 0.57). b, The mean cluster size for each location plotted against (log) population sizes. The association is weaker than for a (R2 = 0.11, Pearson correlation = 0.35, Spearman correlation = 0.57). c, The mean persistence times (per cluster, in days) against population sizes. A similarly weak association is observed as in b (R2 = 0.12, Pearson correlation = 0.37, Spearman correlation = 0.36). All computations were based on a sample of 10,000 trees from the posterior distribution.
Extended Data Tables
- Video 1: Video 1: Reconstructed history of the West African Ebola virus epidemic (10.94 MB, Download)
- 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.
- Supplementary Table (40 KB)
This file contains Supplementary Table 1.