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Temporal and spatial analysis of the 2014-2015 Ebola virus outbreak in West Africa - PubMed

  • ️Thu Jan 01 2015

. 2015 Aug 6;524(7563):97-101.

doi: 10.1038/nature14594. Epub 2015 Jun 17.

David A Matthews  2 , Julian A Hiscox  3 , Michael J Elmore  4 , Georgios Pollakis  3 , Andrew Rambaut  5 , Roger Hewson  6 , Isabel García-Dorival  3 , Joseph Akoi Bore  7 , Raymond Koundouno  7 , Saïd Abdellati  8 , Babak Afrough  9 , John Aiyepada  10 , Patience Akhilomen  10 , Danny Asogun  10 , Barry Atkinson  9 , Marlis Badusche  11 , Amadou Bah  12 , Simon Bate  9 , Jan Baumann  13 , Dirk Becker  14 , Beate Becker-Ziaja  11 , Anne Bocquin  15 , Benny Borremans  16 , Andrew Bosworth  17 , Jan Peter Boettcher  18 , Angela Cannas  19 , Fabrizio Carletti  19 , Concetta Castilletti  19 , Simon Clark  9 , Francesca Colavita  19 , Sandra Diederich  20 , Adomeh Donatus  10 , Sophie Duraffour  21 , Deborah Ehichioya  22 , Heinz Ellerbrok  18 , Maria Dolores Fernandez-Garcia  23 , Alexandra Fizet  24 , Erna Fleischmann  25 , Sophie Gryseels  16 , Antje Hermelink  18 , Julia Hinzmann  18 , Ute Hopf-Guevara  18 , Yemisi Ighodalo  10 , Lisa Jameson  9 , Anne Kelterbaum  14 , Zoltan Kis  26 , Stefan Kloth  18 , Claudia Kohl  18 , Miša Korva  27 , Annette Kraus  28 , Eeva Kuisma  9 , Andreas Kurth  18 , Britta Liedigk  11 , Christopher H Logue  9 , Anja Lüdtke  29 , Piet Maes  30 , James McCowen  9 , Stéphane Mély  15 , Marc Mertens  20 , Silvia Meschi  19 , Benjamin Meyer  31 , Janine Michel  18 , Peter Molkenthin  25 , César Muñoz-Fontela  29 , Doreen Muth  31 , Edmund N C Newman  9 , Didier Ngabo  9 , Lisa Oestereich  11 , Jennifer Okosun  10 , Thomas Olokor  10 , Racheal Omiunu  10 , Emmanuel Omomoh  10 , Elisa Pallasch  11 , Bernadett Pályi  26 , Jasmine Portmann  32 , Thomas Pottage  9 , Catherine Pratt  9 , Simone Priesnitz  33 , Serena Quartu  19 , Julie Rappe  34 , Johanna Repits  35 , Martin Richter  18 , Martin Rudolf  11 , Andreas Sachse  18 , Kristina Maria Schmidt  18 , Gordian Schudt  14 , Thomas Strecker  14 , Ruth Thom  9 , Stephen Thomas  9 , Ekaete Tobin  10 , Howard Tolley  9 , Jochen Trautner  36 , Tine Vermoesen  8 , Inês Vitoriano  9 , Matthias Wagner  25 , Svenja Wolff  14 , Constanze Yue  18 , Maria Rosaria Capobianchi  19 , Birte Kretschmer  37 , Yper Hall  4 , John G Kenny  38 , Natasha Y Rickett  3 , Gytis Dudas  39 , Cordelia E M Coltart  40 , Romy Kerber  11 , Damien Steer  41 , Callum Wright  42 , Francis Senyah  4 , Sakoba Keita  43 , Patrick Drury  44 , Boubacar Diallo  45 , Hilde de Clerck  46 , Michel Van Herp  46 , Armand Sprecher  46 , Alexis Traore  47 , Mandiou Diakite  48 , Mandy Kader Konde  49 , Lamine Koivogui  50 , N'Faly Magassouba  51 , Tatjana Avšič-Županc  27 , Andreas Nitsche  18 , Marc Strasser  32 , Giuseppe Ippolito  19 , Stephan Becker  14 , Kilian Stoecker  25 , Martin Gabriel  11 , Hervé Raoul  52 , Antonino Di Caro  19 , Roman Wölfel  25 , Pierre Formenty  44 , Stephan Günther  11

Affiliations

Temporal and spatial analysis of the 2014-2015 Ebola virus outbreak in West Africa

Miles W Carroll et al. Nature. 2015.

Abstract

West Africa is currently witnessing the most extensive Ebola virus (EBOV) outbreak so far recorded. Until now, there have been 27,013 reported cases and 11,134 deaths. The origin of the virus is thought to have been a zoonotic transmission from a bat to a two-year-old boy in December 2013 (ref. 2). From this index case the virus was spread by human-to-human contact throughout Guinea, Sierra Leone and Liberia. However, the origin of the particular virus in each country and time of transmission is not known and currently relies on epidemiological analysis, which may be unreliable owing to the difficulties of obtaining patient information. Here we trace the genetic evolution of EBOV in the current outbreak that has resulted in multiple lineages. Deep sequencing of 179 patient samples processed by the European Mobile Laboratory, the first diagnostics unit to be deployed to the epicentre of the outbreak in Guinea, reveals an epidemiological and evolutionary history of the epidemic from March 2014 to January 2015. Analysis of EBOV genome evolution has also benefited from a similar sequencing effort of patient samples from Sierra Leone. Our results confirm that the EBOV from Guinea moved into Sierra Leone, most likely in April or early May. The viruses of the Guinea/Sierra Leone lineage mixed around June/July 2014. Viral sequences covering August, September and October 2014 indicate that this lineage evolved independently within Guinea. These data can be used in conjunction with epidemiological information to test retrospectively the effectiveness of control measures, and provides an unprecedented window into the evolution of an ongoing viral haemorrhagic fever outbreak.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Geographical location, sequence read depth, and read depth vs Ct value of patient samples.

a, Geographical location of patient samples. The origin of the sequenced samples (one sample per patient) from Guinea, Sierra Leone, and Liberia processed by EMLab Guéckédou are plotted as numbers of cases by district. EMLab data are overlaid on an Ebola outbreak distribution map where cumulative cases are plotted as a heat map (low (yellow) to high (brown)) of confirmed cases from March 2014 to January 2015. Case data sourced from World Health Organization (WHO) Ebola response situation reports (

http://apps.who.int/ebola/en/ebola-situation-reports

); Geographic Information Systems (GIS) data sourced from Environmental Systems Research Institute (ESRI) and Database of Global Administrative Areas (GADM;

http://www.gadm.org/

). b, Sequence depth per nucleotide position. The number of reads for each nucleotide position was plotted across the full length of the virus genome for each of the 179 virus isolates we analysed. In red is shown the uniformity of the depth across individual genomes, although the median number of reads per nucleotide position had a variation spanning over four log10 units. c, Linear regression of the log10 median sequence depth of each virus isolate versus the Ct value of the viral load as determined by qRT–PCR. Red dots indicate samples taken from patients who went on to survive EBOV infection and grey shaded dots are from patients who records suggest died from EBOV infection. PowerPoint slide

Figure 2
Figure 2. Phylogenetic relatedness and nucleotide sequence divergence of EBOV isolates from the 2013–2015 outbreak.

a, Phylogenetic relatedness of EBOV isolates. Phylogenetic tree inferred using MrBayes for full-length EBOV genomes sequenced from 179 patient samples obtained between March 2014 and January 2015. Displayed is the majority consensus of 10,000 trees sampled from the posterior distribution with mean branch lengths. Posterior support is shown for selected key nodes. Twenty-two samples originated in Liberia and were collected between March and August 2014 and six samples from Sierra Leone were obtained in June and July 2014. In our analysis we also included published sequences, including the three early Guinean sequences and 78 sequences described by Gire et al.. A number of lineages predominantly circulating in Guinea are denoted as GN1–4 along with a uniquely Sierra Leone lineage (SL3) recognised in Gire et al.. b, EBOV nucleotide sequence divergence from root of the phylogeny in Fig. 2a plotted against time of collection of each virus. The date of the first documented case near Meliandou in eastern Guinea is indicated by the red triangle. PowerPoint slide

Figure 3
Figure 3. A time-scaled phylogenetic tree of 262 EBOV genomes from Guinea, Sierra Leone, Liberia and Mali.

Shown is a maximum clade credibility tree constructed from 10,000 trees sampled from the posterior distribution with mean node ages. Clades described in Gire et al. are identified here (SL1, SL2 and SL3) as well as a number of lineages predominantly circulating in Guinea and posterior probability support is given for these. For certain key node ages, 95% credible intervals are shown by horizontal bars. PowerPoint slide

Figure 4
Figure 4. Position of non-synonymous amino acid variations in the 179 genomes analysed in this study compared to a reference sequence taken from March 2014 (KJ660346.2).

Shown is the frequency of all amino acid positions that had variability and the substitution that occurred with the first single letter position indicating the reference sequence and the second position showing the variation. The percentage frequency in the 179 genomes is shown on the y axis. GP, glycoprotein; NP, nucleoprotein; L, RNA polymerase; VP, viral protein. PowerPoint slide

Extended Data Figure 1
Extended Data Figure 1. Spatial and temporal location of patient samples. Geographical locations of sequenced samples are plotted by district as panels for each month of collection (March 2014–January 2015).

In brief, the number of samples obtained for each month was as follows: March 2014, 11; April 2014, 14; May 2014, 14; June 2014, 22; July 2014, 16; August 2014, 19; September 2014, 18; October 2014, 21; November 2014, 11; December 2014, 22; January 2015, 11. Total number of samples sequenced, 179.

Extended Data Figure 2
Extended Data Figure 2. Enlarged view of phylogenetic tree presented in Fig. 3.

Posterior support shown where >0.5.

Extended Data Figure 3
Extended Data Figure 3. Temporal spread of EBOV based on phylogenetic analyses in Figs 2a and 3.

Colour scheme is as follows: Guinea is red/blue (1st half/2nd half of 2014, respectively), Sierra Leone is grey-black, Liberia is green, Mali is brown. Lineage A (A) is associated with the initial focus of the outbreak (Guéckédou, Macenta and Kissidougou) in March 2014, expanded around this area and then declined around July 2014. From lineage A a second lineage (B) emerged in May/June 2014 and expanded into Sierra Leone (end of May 2014) and Liberia (small arrow). Lineage B continued to spread into Sierra Leone, Liberia, and further into Guinea (beyond the original focus into most districts of Guinea). EBOV disease entered Mali from Guinea via two separate routes (from the Beyla district (possibly originally from Kissidougou) in October 2014 and from the Siguiri district in November 2014).

Extended Data Figure 4
Extended Data Figure 4. Survival rate amongst individuals with known EBOV sequences.

The total survival rate for the 179 sequenced virus isolates included in this study is presented, as is the survival rate for two sub-lineages, GN1 and GN2, as defined by phylogenetic inference in Figs 2a and 3. The sequences available for GN1 were collected during the period of March–July 2014 and the sequences available for GN2 were collected during the period of August 2014–January 2015. Red dots indicate survivors.

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