Virus genomes reveal factors that spread and sustained the Ebola epidemic (original) (raw)

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Acknowledgements

The authors acknowledge support from: European Union Seventh Framework 278433-PREDEMICS (P.L., A.R.) and ERC 260864 (P.L., A.R., M.A.S.) European Union Horizon 2020 643476-COMPARE (M.P.G.K., A.R.), 634650-VIROGENESIS (P.L., M.P.G.K.), 666100-EVIDENT and European Commission IFS/2011/272-372, EMLab (S.G.), National Institutes of Health R01 AI107034, R01 AI117011 and R01 HG006139 and National Science Foundation IIS 1251151 and DMS 1264153 (M.A.S.), NIH AI081982, AI082119, AI082805 AI088843, AI104216, AI104621, AI115754, HSN272200900049C, HHSN272201400048C (R.F.G.), NIH R35 GM119774-01 (T.B.) National Health & Medical Research Council (Australia) (E.C.H.). The Research Foundation - Flanders G0D5117N (G.B., P.L.), Work in Liberia was funded by the Defense Threat Reduction Agency, the Global Emerging Infections System and the Targeted Acquisition of Reference Materials Augmenting Capabilities (TARMAC) Initiative agencies from the US Department of Defense (G.Pa.), Bill and Melinda Gates Foundation OPP1106427, 1032350, OPP1134076, Wellcome Trust 106866/Z/15/Z, Clinton Health Access Initiative (A.J.T.), National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections (J.A.H.), Key Research and Development Program from the Ministry of Science and Technology of China 2016YFC1200800 (D.L.), National Natural Science Foundation of China 81590760 and 81321063 (G.F.G.), Mahan Post-doctoral fellowship Fred Hutchinson Cancer Research Center (G.D.), National Institute of Allergy and Infectious Disease U19AI110818, 5R01AI114855-03, United States Agency for International Development OAA-G-15-00001 and the Bill and Melinda Gates Foundation OPP1123407 (P.C.S.), NIH 1U01HG007480-01 and the World Bank ACE019 (C.T.H.), PEW Biomedical Scholarship, NIH UL1TR001114, and NIAID contract HHSN272201400048C (K.G.A.). J.H.K., an employee of Tunnell Government Services, Inc., is a subcontractor under Battelle Memorial Institute’s prime contract with the NIAID (contract HHSN272200700016I). Colour-blind-friendly colour palettes were designed by C. Brewer, Pennsylvania State University (http://colorbrewer2.org). Matplotlib (http://matplotlib.org) was used extensively throughout this article for data visualisation. We acknowledge support from NVIDIA Corporation with the donation of parallel computing resources used for this research. Finally, we recognize the contributions made by our colleagues who died from Ebola virus disease whilst fighting the epidemic.

Author information

Authors and Affiliations

  1. Institute of Evolutionary Biology, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
    Gytis Dudas, Luiz Max Carvalho & Andrew Rambaut
  2. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, 98109, Washington, USA
    Gytis Dudas & Trevor Bedford
  3. Department of Geography and Environment, WorldPop, University of Southampton, Highfield, SO17 1BJ, Southampton, UK
    Andrew J. Tatem
  4. Flowminder Foundation, Stockholm, Sweden
    Andrew J. Tatem
  5. Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Leuven, 3000, Belgium
    Guy Baele, Filip Bielejec, Simon Dellicour & Philippe Lemey
  6. Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK
    Nuno R. Faria & Oliver G. Pybus
  7. Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA
    Daniel J. Park, Stephen Gire, Adrianne Gladden-Young, Andreas Gnirke, Christine M. Malboeuf, Christian B. Matranga, James Qu, Stephen F. Schaffner, Rachel S. Sealfon, Kendra West, Sarah M. Winnicki, Shirlee Wohl, Nathan L. Yozwiak & Pardis C. Sabeti
  8. Center for Genome Sciences, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, 21702, Maryland, USA
    Jason T. Ladner, Jonathan D’Ambrozio, Merle L. Gilbert, Jeffrey R. Kugelman, Suzanne Mate, Mariano Sanchez-Lockhart, Michael R. Wiley & Gustavo Palacios
  9. Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
    Armando Arias, Sarah L. Caddy, Jia Lu, Luke W. Meredith, Lucy Thorne & Ian Goodfellow
  10. National Veterinary Institute, Technical University of Denmark, Bülowsvej, 27, 1870, Frederiksberg C, Denmark
    Armando Arias
  11. Institute of Lassa Fever Research and Control, Irrua Specialist Teaching Hospital, Irrua, Nigeria
    Danny Asogun & Ekaete Alice Tobin
  12. The European Mobile Laboratory Consortium, Hamburg, 20359, Germany
    Danny Asogun, Antonino Di Caro, Sophie Duraffour, Kilian Stoecker, Ekaete Alice Tobin, Roman Wölfel, Miles W. Carroll & Stephan Günther
  13. Virus Genomics, Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, Cambridge, UK
    Matthew Cotten, My V. T. Phan, Simon J. Watson & Paul Kellam
  14. Department of Viroscience, Erasmus University Medical Centre, PO Box 2040, 300, CA Rotterdam, the Netherlands
    Matthew Cotten, Bart L. Haagmans, Suzan D. Pas, My V. T. Phan, Chantal B. Reusken, Saskia L. Smits & Marion P. G. Koopmans
  15. National Institute for Infectious Diseases ‘L. Spallanzani’—IRCCS, Via Portuense 292, Rome, 00149, Italy
    Antonino Di Caro
  16. Naval Medical Research Unit 3, 3A Imtidad Ramses Street, Cairo, 11517, Egypt
    Joseph W. Diclaro
  17. Bernhard Nocht Institute for Tropical Medicine, Hamburg, 20359, Germany
    Sophie Duraffour & Stephan Günther
  18. National Infections Service, Public Health England, Porton Down, Salisbury, SP4 0JG, Wilts, UK
    Michael J. Elmore & Miles W. Carroll
  19. Liberian Institute for Biomedical Research, Charlesville, Liberia
    Lawrence S. Fakoli & Fatorma Bolay
  20. Institut Pasteur de Dakar, Arbovirus and Viral Hemorrhagic Fever Unit, 36 Avenue Pasteur, BP, 220, Dakar, Sénégal
    Ousmane Faye & Amadou Sall
  21. University of Sierra Leone, Freetown, Sierra Leone
    Sahr M. Gevao & Isatta Wurie
  22. Department of Organismic and Evolutionary Biology, Center for Systems Biology, Harvard University, Cambridge, 02138, Massachusetts, USA
    Stephen Gire, Shirlee Wohl, Nathan L. Yozwiak & Pardis C. Sabeti
  23. Viral Hemorrhagic Fever Program, Kenema Government Hospital, 1 Combema Road, Kenema, Sierra Leone
    Augustine Goba, Donald S. Grant & Mohamed A. Vandi
  24. Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
    Augustine Goba, Donald S. Grant, Mohamed A. Vandi & Brima Kargbo
  25. Institute of Infection and Global Health, University of Liverpool, Liverpool, L69 2BE, UK
    Julian A. Hiscox & Georgios Pollakis
  26. NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, L69 3GL, UK
    Julian A. Hiscox & Miles W. Carroll
  27. University of Makeni, Makeni, Sierra Leone
    Umaru Jah, Luke W. Meredith & Ian Goodfellow
  28. Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
    Di Liu & George F. Gao
  29. University of Bristol, Bristol, BS8 1TD, UK
    David A. Matthews
  30. Institute of Microbiology and Infection, University of Birmingham, Birmingham, B15 2TT, UK
    Joshua Quick & Nicholas J. Loman
  31. University of Nebraska Medical Center, Omaha, 68198, Nebraska, USA
    Mariano Sanchez-Lockhart & Michael R. Wiley
  32. Department of Pediatrics, Section of Infectious Diseases, New Orleans, 70112, Louisiana, USA
    John S. Schieffelin & Sarah M. Winnicki
  33. Center for Computational Biology, Flatiron Institute, New York, 10010, New York, USA
    Rachel S. Sealfon
  34. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08544, New Jersey, USA
    Rachel S. Sealfon
  35. Institut Pasteur, Functional Genetics of Infectious Diseases Unit, 28 rue du Docteur Roux, Paris, 75724, Cedex 15, France
    Etienne Simon-Loriere
  36. Génétique Fonctionelle des Maladies Infectieuses, CNRS URA3012, Paris, 75015, France
    Etienne Simon-Loriere
  37. Bundeswehr Institute of Microbiology, Neuherbergstrasse 11, Munich, 80937, Germany
    Kilian Stoecker & Roman Wölfel
  38. Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, 30333, Georgia, USA
    Shannon Whitmer, Stuart T. Nichol & Ute Ströher
  39. Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, 92037, California, USA
    Kristian G. Andersen
  40. Scripps Translational Science Institute, La Jolla, 92037, California, USA
    Kristian G. Andersen
  41. Ministry of Social Welfare, Gender and Children’s Affairs, New Englandville, Freetown, Sierra Leone
    Sylvia O. Blyden
  42. University of Southampton, South General Hospital, Southampton, SO16 6YD, UK
    Miles W. Carroll
  43. Minstry of Health Liberia, Monrovia, Liberia
    Bernice Dahn & Tolbert Nyenswah
  44. World Health Organization, Conakry, Guinea
    Boubacar Diallo
  45. World Health Organization, Geneva, Switzerland
    Pierre Formenty & Dhamari Naidoo
  46. Nuffield Department of Medicine, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7FZ, UK
    Christophe Fraser
  47. Chinese Center for Disease Control and Prevention (China CDC), Beijing, 102206, China
    George F. Gao
  48. Department of Microbiology and Immunology, New Orleans, 70112, Louisiana, USA
    Robert F. Garry
  49. Department of Biological Sciences, Redeemer’s University, Ede, Osun State, Nigeria
    Christian T. Happi
  50. African Center of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Ede, Osun State, Nigeria
    Christian T. Happi
  51. Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, the University of Sydney, Sydney, 2006, New South Wales, Australia
    Edward C. Holmes
  52. Ministry of Health Guinea, Conakry, Guinea
    Sakoba Keïta
  53. Division of Infectious Diseases, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
    Paul Kellam
  54. Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, B-8200 Research Plaza, Fort Detrick, Frederick, 21702, Maryland, USA
    Jens H. Kuhn
  55. Université Gamal Abdel Nasser de Conakry, Laboratoire des Fièvres Hémorragiques en Guinée, Conakry, Guinea
    N’Faly Magassouba
  56. Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, 90095, California, USA
    Marc A. Suchard
  57. Department of Biomathematics David Geffen School of Medicine at UCLA, University of California, Los Angeles, 90095, California, USA
    Marc A. Suchard
  58. Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, 90095, California, USA
    Marc A. Suchard
  59. Centre for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
    Andrew Rambaut
  60. Fogarty International Center, National Institutes of Health, Bethesda, 20892, Maryland, USA
    Andrew Rambaut

Authors

  1. Gytis Dudas
  2. Luiz Max Carvalho
  3. Trevor Bedford
  4. Andrew J. Tatem
  5. Guy Baele
  6. Nuno R. Faria
  7. Daniel J. Park
  8. Jason T. Ladner
  9. Armando Arias
  10. Danny Asogun
  11. Filip Bielejec
  12. Sarah L. Caddy
  13. Matthew Cotten
  14. Jonathan D’Ambrozio
  15. Simon Dellicour
  16. Antonino Di Caro
  17. Joseph W. Diclaro
  18. Sophie Duraffour
  19. Michael J. Elmore
  20. Lawrence S. Fakoli
  21. Ousmane Faye
  22. Merle L. Gilbert
  23. Sahr M. Gevao
  24. Stephen Gire
  25. Adrianne Gladden-Young
  26. Andreas Gnirke
  27. Augustine Goba
  28. Donald S. Grant
  29. Bart L. Haagmans
  30. Julian A. Hiscox
  31. Umaru Jah
  32. Jeffrey R. Kugelman
  33. Di Liu
  34. Jia Lu
  35. Christine M. Malboeuf
  36. Suzanne Mate
  37. David A. Matthews
  38. Christian B. Matranga
  39. Luke W. Meredith
  40. James Qu
  41. Joshua Quick
  42. Suzan D. Pas
  43. My V. T. Phan
  44. Georgios Pollakis
  45. Chantal B. Reusken
  46. Mariano Sanchez-Lockhart
  47. Stephen F. Schaffner
  48. John S. Schieffelin
  49. Rachel S. Sealfon
  50. Etienne Simon-Loriere
  51. Saskia L. Smits
  52. Kilian Stoecker
  53. Lucy Thorne
  54. Ekaete Alice Tobin
  55. Mohamed A. Vandi
  56. Simon J. Watson
  57. Kendra West
  58. Shannon Whitmer
  59. Michael R. Wiley
  60. Sarah M. Winnicki
  61. Shirlee Wohl
  62. Roman Wölfel
  63. Nathan L. Yozwiak
  64. Kristian G. Andersen
  65. Sylvia O. Blyden
  66. Fatorma Bolay
  67. Miles W. Carroll
  68. Bernice Dahn
  69. Boubacar Diallo
  70. Pierre Formenty
  71. Christophe Fraser
  72. George F. Gao
  73. Robert F. Garry
  74. Ian Goodfellow
  75. Stephan Günther
  76. Christian T. Happi
  77. Edward C. Holmes
  78. Brima Kargbo
  79. Sakoba Keïta
  80. Paul Kellam
  81. Marion P. G. Koopmans
  82. Jens H. Kuhn
  83. Nicholas J. Loman
  84. N’Faly Magassouba
  85. Dhamari Naidoo
  86. Stuart T. Nichol
  87. Tolbert Nyenswah
  88. Gustavo Palacios
  89. Oliver G. Pybus
  90. Pardis C. Sabeti
  91. Amadou Sall
  92. Ute Ströher
  93. Isatta Wurie
  94. Marc A. Suchard
  95. Philippe Lemey
  96. Andrew Rambaut

Contributions

G.D., L.M.C., T.B., C.F., M.A.S., P.L. and A.R. designed the study. G.D., L.M.C., T.B., A.J.T., G.B., P.L. and A.R. performed the analysis. G.D., T.B., M.A.S, P.L. and A.R. wrote the manuscript. L.M.C., A.J.T., G.B., N.R.F., J.T.L., M.C., S.F.S., K.G.A., M.W.C., R.F.G., I.G., E.C.H., P.K., M.P.G.K., J.H.K., S.T.N., G.Pa., O.G.P., P.C.S. and U.S. edited the manuscript. The other authors were critical for the coordination, collection, processing of virus samples or the sequencing and bioinformatics of virus genomes. All authors read and approved the contents of the manuscript.

Corresponding authors

Correspondence toGytis Dudas, Philippe Lemey or Andrew Rambaut.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks R. Biek, C. Viboud, M. Worobey and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Distribution and correlation of EVD cases and EBOV sequences.

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.

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.

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.

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.

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.

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.

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.

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.

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 (_R_2 = 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 (_R_2 = 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 (_R_2 = 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 Table 1 Predictors included in the time-homogenous GLM

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Supplementary information

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Video 1: Reconstructed history of the West African Ebola virus epidemic (download MP4 )

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. (MP4 11204 kb)

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Dudas, G., Carvalho, L., Bedford, T. et al. Virus genomes reveal factors that spread and sustained the Ebola epidemic.Nature 544, 309–315 (2017). https://doi.org/10.1038/nature22040

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