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Virus genomes reveal factors that spread and sustained the Ebola epidemic

Gytis Dudas, +110 more
- 20 Apr 2017 - 
- Vol. 544, Iss: 7650, pp 309-315
TLDR
It is revealed that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity, which will help to inform interventions in future epidemics.
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 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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University of Birmingham
Virus genomes reveal factors that spread and
sustained the Ebola epidemic
Dudas, Gytis; Carvalho, Luiz Max; Bedford, Trevor; Tatem, Andrew J; Baele, Guy; Faria,
Nuno R; Park, Daniel J; Ladner, Jason T; Arias, Armando; Asogun, Danny; Bielejec, Filip;
Caddy, Sarah L; Cotten, Matthew; D'Ambrozio, Jonathan; Dellicour, Simon; Di Caro,
Antonino; Diclaro, Joseph W; Duraffour, Sophie; Elmore, Michael J; Fakoli, Lawrence S
DOI:
10.1038/nature22040
License:
None: All rights reserved
Document Version
Peer reviewed version
Citation for published version (Harvard):
Dudas, G, Carvalho, LM, Bedford, T, Tatem, AJ, Baele, G, Faria, NR, Park, DJ, Ladner, JT, Arias, A, Asogun, D,
Bielejec, F, Caddy, SL, Cotten, M, D'Ambrozio, J, Dellicour, S, Di Caro, A, Diclaro, JW, Duraffour, S, Elmore,
MJ, Fakoli, LS, Faye, O, Gilbert, ML, Gevao, SM, Gire, S, Gladden-Young, A, Gnirke, A, Goba, A, Grant, DS,
Haagmans, BL, Hiscox, JA, Jah, U, Kugelman, JR, Liu, D, Lu, J, Malboeuf, CM, Mate, S, Matthews, DA,
Matranga, CB, Meredith, LW, Qu, J, Quick, J, Pas, SD, Phan, MVT, Pollakis, G, Reusken, CB, Sanchez-
Lockhart, M, Schaffner, SF, Schieffelin, JS, Sealfon, RS, Simon-Loriere, E, Smits, SL, Stoecker, K, Thorne, L,
Tobin, EA, Vandi, MA, Watson, SJ, West, K, Whitmer, S, Wiley, MR, Winnicki, SM, Wohl, S, Wölfel, R, Yozwiak,
NL, Andersen, KG, Blyden, SO, Bolay, F, Carroll, MW, Dahn, B, Diallo, B, Formenty, P, Fraser, C, Gao, GF,
Garry, RF, Goodfellow, I, Günther, S, Happi, CT, Holmes, EC, Kargbo, B, Keïta, S, Kellam, P, Koopmans, MPG,
Kuhn, JH, Loman, NJ, Magassouba, NF, Naidoo, D, Nichol, ST, Nyenswah, T, Palacios, G, Pybus, OG, Sabeti,
PC, Sall, A, Ströher, U, Wurie, I, Suchard, MA, Lemey, P & Rambaut, A 2017, 'Virus genomes reveal factors that
spread and sustained the Ebola epidemic', Nature, vol. 544, no. 7650, pp. 309-315.
https://doi.org/10.1038/nature22040
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Virus genomes reveal factors that spread and sus-
tained the Ebola epidemic
Gytis Dudas
1,2,
, Luiz Max Carvalho
1
, Trevor Bedford
2
, Andrew J. Tatem
3,4
, Guy
Baele
5
, Nuno R. Faria
6
, Daniel J. Park
7
, Jason T. Ladner
8
, Armando Arias
9,10
, Danny
Asogun
11,12
, Filip Bielejec
5
, Sarah L. Caddy
9
, Matthew Cotten
13,14
, Jonathan D’Ambrozio
8
,
Simon Dellicour
5
, Antonino Di Caro
15,12
, Joseph W. Diclaro II
16
, Sophie Duraffour
17,12
,
Michael J. Elmore
18
, Lawrence S. Fakoli III
19
, Ousmane Faye
20
, Merle L. Gilbert
8
,
Sahr M. Gevao
21
, Stephen Gire
7,22
, Adrianne Gladden-Young
7
, Andreas Gnirke
7
,
Augustine Goba
23,24
, Donald S. Grant
23,24
, Bart L. Haagmans
14
, Julian A. Hiscox
25,26
,
Umaru Jah
27
, Brima Kargbo
24
, Jeffrey R. Kugelman
8
, Di Liu
28
, Jia Lu
9
, Christine M.
Malboeuf
7
, Suzanne Mate
8
, David A. Matthews
29
, Christian B. Matranga
7
, Luke W.
Meredith
9,27
, James Qu
7
, Joshua Quick
30
, Suzan D. Pas
14
, My VT Phan
13,14
, Geor-
gios Pollakis
25
, Chantal B. Reusken
14
, Mariano Sanchez-Lockhart
8,31
, Stephen F.
Schaffner
7
, John S. Schieffelin
32
, Rachel S. Sealfon
33,7,34
, Etienne Simon-Loriere
35,36
,
Saskia L. Smits
14
, Kilian Stoecker
37,12
, Lucy Thorne
9
, Ekaete Alice Tobin
11,12
, Mo-
hamed A. Vandi
23,24
, Simon J. Watson
13
, Kendra West
7
, Shannon Whitmer
38,
, Michael
R. Wiley
8,31
, Sarah M. Winnicki
7,22
, Shirlee Wohl
7,22
, Roman W
¨
olfel
37,12
, Nathan L.
Yozwiak
7,22
, Kristian G. Andersen
39,40
, Sylvia O. Blyden
41
, Fatorma Bolay
19
, Miles W.
Carroll
18,12,42,26
, Bernice Dahn
43
, Boubacar Diallo
44
, Pierre Formenty
45
, Christophe
Fraser
46
, George F. Gao
28,47
, Robert F. Garry
48
, Ian Goodfellow
9,27
, Stephan G
¨
unther
17,12
,
Christian T. Happi
49,50
, Edward C. Holmes
51
, Brima Kargbo
24
, Sakoba Ke
¨
ıta
52
, Paul
Kellam
13,53
, Marion P. G. Koopmans
14
, Jens H. Kuhn
54
, Nicholas J. Loman
30
, N’Faly
Magassouba
55
, Dhamari Naidoo
45
, Stuart T. Nichol
38,
, Tolbert Nyenswah
43
, Gus-
tavo Palacios
8
, Oliver G. Pybus
6
, Pardis C. Sabeti
7,22
, Amadou Sall
20
, Ute Str
¨
oher
38,
,
Isatta Wurie
21
, Marc A. Suchard
56,57,58
, Philippe Lemey
5,
& Andrew Rambaut
1,59,60,
1
Institute of Evolutionary Biology, University of Edinburgh, King’s Buildings, Edinburgh, EH9
3FL, UK,
2
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Cen-
ter, Seattle, WA, USA,
3
WorldPop, Department of Geography and Environment, University of
Southampton, Highfield, Southampton SO17 1BJ, UK,
4
Flowminder Foundation, Stockholm, Swe-
den,
5
Department of Microbiology and Immunology, Rega Institute, KU Leuven University of
Leuven, Leuven, Belgium,
6
Department of Zoology, University of Oxford, South Parks Road,
Oxford, OX1 3PS, UK,
7
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA,
8
Center
for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick,
Frederick, MD 21702, USA,
9
Department of Pathology, University of Cambridge, Addenbrooke’s
Hospital, Cambridge, CB2 2QQ, UK,
10
National Veterinary Institute, Technical University of
Denmark, B
¨
ulowsvej 27, 1870, Frederiksberg C, Denmark,
11
Institute of Lassa Fever Research and
Control, Irrua Specialist Teaching Hospital, Irrua, Nigeria,
12
The European Mobile Laboratory Con-
sortium, 20359 Hamburg, Germany,
13
Virus Genomics, Wellcome Trust Sanger Institute, Hinxton,
UK,
14
Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA
Rotterdam, the Netherlands,
15
National Institute for Infectious Diseases ”L. Spallanzani” IRCCS,
Via Portuense 292, 00149 Rome, Italy,
16
Naval Medical Research Unit 3, 3A Imtidad Ramses
1

Street, Cairo, 11517, Egypt,
17
Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg,
Germany,
18
National Infections Service, Public Health England, Porton Down, Salisbury, Wilts SP4
0JG, UK,
19
Liberian Institute for Biomedical Research, Charlesville, Liberia,
20
Institut Pasteur de
Dakar, Arbovirus and Viral Hemorrhagic Fever Unit, 36 Avenue Pasteur, BP 220, Dakar, S
´
en
´
egal,
21
University of Sierra Leone, Freetown, Sierra Leone,
22
Center for Systems Biology, Department of
Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA,
23
Viral
Hemorrhagic Fever Program, Kenema Government Hospital, 1 Combema Road, Kenema, Sierra
Leone,
24
Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
,
25
Institute of Infection and Global Health, University of Liverpool, Liverpool L69 2BE, UK,
26
NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of
Liverpool, UK,
27
University of Makeni, Makeni, Sierra Leone,
28
Institute of Microbiology, Chinese
Academy of Sciences, Beijing 100101, China,
29
University of Bristol, BS8 1TD, UK,
30
Institute of
Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK,
31
University
of Nebraska Medical Center, Omaha, NE, USA,
32
Department of Pediatrics, Section of Infectious
Diseases, New Orleans, LA 70112, USA,
33
Center for Computational Biology, Flatiron Institute,
New York, NY 10010, USA,
34
Lewis-Sigler Institute for Integrative Genomics, Princeton Univer-
sity, Princeton, NJ 08544, USA,
35
Institut Pasteur, Functional Genetics of Infectious Diseases Unit,
28 rue du Docteur Roux, 75724 Paris Cedex 15, France,
36
G
´
en
´
etique Fonctionelle des Maladies
Infectieuses, CNRS URA3012, Paris 75015, France,
37
Bundeswehr Institute of Microbiology,
Neuherbergstrasse 11, 80937 Munich, Germany,
38
Viral Special Pathogens Branch, Centers for
Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, Georgia, USA,
39
The Scripps
Research Institute, Department of Immunology and Microbial Science, La Jolla, CA 92037, USA,
40
Scripps Translational Science Institute, La Jolla, CA 92037, USA,
41
Ministry of Social Wel-
fare, Gender and Children’s Affairs, New Englandville, Freetown, Sierra Leone,
42
University of
Southampton, South General Hospital, Southampton SO16 6YD, UK,
43
Minstry of Health Liberia,
Monrovia, Liberia,
44
World Health Organization, Conakry, Guinea,
45
World Health Organization,
Geneva, Switzerland,
46
Oxford Big Data Institute, Li Ka Shing Centre for Health Information
and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK,
47
Chinese
Center for Disease Control and Prevention (China CDC), Beijing 102206, China,
48
Department
of Microbiology and Immunology, New Orleans, LA 70112, USA,
49
Department of Biological
Sciences, Redeemer’s University, Ede, Osun State, Nigeria,
50
African Center of Excellence for
Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Ede, Osun State, Nigeria,
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,
NSW 2006, Australia,
52
Ministry of Health Guinea, Conakry, Guinea,
53
Division of Infectious
Diseases, Imperial College Faculty of Medicine, London W2 1PG, UK,
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, MD 21702, USA,
55
Universit
´
e Gamal
Abdel Nasser de Conakry, Laboratoire des Fi
`
evres H
´
emorragiques en Guin
´
ee, Conakry, Guinea,
56
Department of Biostatistics, UCLA Fielding School of Public Health, University of California,
Los Angeles, CA, USA,
57
Department of Biomathematics David Geffen School of Medicine at
UCLA, University of California, Los Angeles, CA, USA,
58
Department of Human Genetics, David
Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA,
59
Centre
2

for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh,
EH9 3FL, UK,
60
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
Corresponding authors (a.rambaut@ed.ac.uk, gdudas@fredhutch.org, philippe.lemey@kuleuven.be)
The findings and conclusions in this report are those of the authors and do not necessarily
represent the official position of the Centers for Disease Control and Prevention.
3

The 2013–2016 epidemic of Ebola virus disease was of unprecedented magnitude,
duration and impact. Analysing 1610 Ebola virus genomes, representing over 5% of
known cases, we reconstruct the dispersal, proliferation and decline of Ebola virus
throughout the region. We test the association of geography, climate and demogra-
phy with viral movement among administrative regions, inferring a classic ‘gravity’
model, with intense dispersal between larger and closer populations. Despite attenu-
ation of international dispersal after border closures, cross-border transmission had
already set the seeds for an international epidemic, rendering these measures inef-
fective in curbing the epidemic. We address why the epidemic did not spread into
neighbouring countries, showing they were susceptible to significant outbreaks but
at lower risk of introductions. Finally, we reveal this large epidemic to be a hetero-
geneous and spatially dissociated collection of transmission clusters of varying size,
duration and connectivity. These insights will help inform interventions in future
epidemics.
At least 28,646 cases and 11,323 deaths
1
have been attributed to the Makona variant of
Ebola virus (EBOV)
2
in the two and a half years it circulated in West Africa. The epidemic
is thought to have begun in December 2013 in Guinea, but was not detected and reported
until March 2014
3
. Initial efforts to control the outbreak in Guinea were considered to be
succeeding
4
, but in early 2014 the virus crossed international borders into neighbouring
Liberia (first cases diagnosed in late March) and Sierra Leone (first documented case in
late February
5, 6
, first diagnosed cases in May
7
). EBOV genomes sequenced from three
patients in Guinea early in the epidemic
3
demonstrated that the progenitor of the Makona
variant originated in Middle Africa and arrived in West Africa within the last 15 years
7, 8
.
Rapid sequencing from the first reported cases in Sierra Leone confirmed that EBOV
had crossed the border from Guinea and were not the result of an independent zoonotic
introduction
7
. Subsequent studies analysed the genetic makeup of the Makona variant,
focusing on Guinea
9–11
, Sierra Leone
12, 13
or Liberia
14, 15
, identifying local viral lineages
and transmission patterns within each country.
Although virus sequencing has covered considerable fractions of the epidemic in each
affected country, individual studies focused on either limited geographical areas or time
periods, so that the regional level patterns and drivers of the epidemic across its entire
duration have remained uncertain. Using 1610 genome sequences collected throughout the
epidemic, representing over 5% of recorded Ebola virus disease (EVD) cases (Figure 1),
we reconstruct a detailed phylogenetic history of the movement of EBOV within and
between the three most affected countries. Using a recently developed phylogeographic
approach that integrates covariates of spatial spread
16
, we test which features of each region
(administrative, economic, climatic, infrastructural and demographic) were important in
shaping the spatial dynamics of EVD. We also examine the effectiveness of international
border closures on controlling virus dissemination. Finally, we investigate why regions
that immediately border the most affected countries did not develop protracted outbreaks
similar to those that ravaged Sierra Leone, Guinea and Liberia.
4

Figures
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MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform

TL;DR: A simplified scoring system is proposed that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length.
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Q1. What are the contributions mentioned in the paper "University of birmingham virus genomes reveal factors that spread and sustained the ebola epidemic" ?

Dudas et al. this paper, 2017, 'Virus genomes reveal factors that spread and sustained the Ebola epidemic ', Nature, vol. 544, no. 7650, pp. 309-315. 

Their analysis reveals that there were at least 21 (95% CI: 16 - 25) re-introductions into Guinea from April 2014 to February 2015. 

To obtain realisations of the phylogenetic CTMC process, including both transitions (Markov jumps) between states and waiting times (Markov rewards) within states, the authors employ posterior inference of the complete Markov jump history through time16, 56. 

Putative tracts of T-to-C hypermutation almost exclusively occur within non-coding intergenic regions, where their effects on viral fitness are presumably minimal. 

Their work demonstrates the value of pathogen genome sequencing in a public healthcare emergency and the value of timely pre-publication data sharing to identify the origins of imported disease case clusters, to track pathogen transmission as the epidemic progresses, and to follow up on individual cases as the epidemic subsides. 

In keeping with the genetic GLM analyses, the authors also set the prior inclusion probabilities such that there was a 50% probability of no predictors being included. 

it is likely that some of these regions were at risk of becoming part of the EVD epidemic, but that their geographical distance from areas of active transmission and the attenuating effect of international borders prevented this from occurring. 

To consider time-inhomogeneity in the spatial diffusion process, the authors start by borrowing epoch modelling concepts from Bielejec et al. (2014)57. 

using the distribution function of a binomial random variable q = 1− w1/P , where P is the number of predictors, as before. 

These random effects account for unexplained variability in the diffusion process that may otherwise lead to spurious inclusion of predictors. 

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