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1. On the importance of assessing topological convergence in Bayesian phylogenetic inference

Author

Brusselmans, Marius, Carvalho, Luiz Max, Hong, Samuel L., Gao, Jiansi, Matsen IV, Frederick A., Rambaut, Andrew, Lemey, Philippe, Suchard, Marc A., Dudas, Gytis, and Baele, Guy

Subjects
Quantitative Biology - Populations and Evolution, Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods
Abstract

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size (ESS) and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies - one on Ebola virus and one on HIV - illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especially now that additional tools are becoming available to researchers.

Published
2024

9. Fitting stochastic epidemic models to gene genealogies using linear noise approximation

Author

Tang, Mingwei, Dudas, Gytis, Bedford, Trevor, and Minin, Vladimir N

Subjects
Mathematical Sciences, Statistics, Bioengineering, Infectious Diseases, Emerging Infectious Diseases, Aetiology, 2.5 Research design and methodologies (aetiology), Infection, Good Health and Well Being, Coalescent, susceptible -infectious -recovered model, state -space model, phylodynamics, Ebola virus, Susceptible-Infectious-Recovered model, state-space model, Econometrics, Statistics & Probability
Abstract

Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches (e.g., Bayesian curve-fitting based on change-point models or Gaussian process priors), parametric approaches (e.g., based on differential equations), and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard to interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) - a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). In a simulation study, we show that our method can successfully recover parameters of stochastic epidemic models. We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.

Published
2023

10. Lower respiratory tract infections in children requiring mechanical ventilation: a multicentre prospective surveillance study incorporating airway metagenomics

Author

Tsitsiklis, Alexandra, Osborne, Christina M, Kamm, Jack, Williamson, Kayla, Kalantar, Katrina, Dudas, Gytis, Caldera, Saharai, Lyden, Amy, Tan, Michelle, Neff, Norma, Soesanto, Victoria, Harris, J Kirk, Ambroggio, Lilliam, Maddux, Aline B, Carpenter, Todd C, Reeder, Ron W, Locandro, Chris, Simões, Eric AF, Leroue, Matthew K, Hall, Mark W, Zuppa, Athena F, Carcillo, Joseph, Meert, Kathleen L, Sapru, Anil, Pollack, Murray M, McQuillen, Patrick S, Notterman, Daniel A, Dean, J Michael, Zinter, Matt S, Wagner, Brandie D, DeRisi, Joseph L, Mourani, Peter M, and Langelier, Charles R

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Pediatric, Immunization, Clinical Research, Lung, Infectious Diseases, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, Bacteria, Child, Cohort Studies, Critical Illness, Haemophilus influenzae, Humans, Metagenomics, Moraxella catarrhalis, Prospective Studies, Respiration, Artificial, Respiratory Syncytial Virus, Human, Respiratory Tract Infections, United States, Microbiology, Immunology, Medical microbiology
Abstract

BackgroundLower respiratory tract infections (LRTI) are a leading cause of critical illness and mortality in mechanically ventilated children; however, the pathogenic microbes frequently remain unknown. We combined traditional diagnostics with metagenomic next generation sequencing (mNGS) to evaluate the cause of LRTI in critically ill children.MethodsWe conducted a prospective, multicentre cohort study of critically ill children aged 31 days to 17 years with respiratory failure requiring mechanical ventilation (>72 h) in the USA. By combining bacterial culture and upper respiratory viral PCR testing with mNGS of tracheal aspirate collected from all patients within 24 h of intubation, we determined the prevalence, age distribution, and seasonal variation of viral and bacterial respiratory pathogens detected by either method in children with or without LRTI.FindingsBetween Feb 26, 2015, and Dec 31, 2017, of the 514 enrolled patients, 397 were eligible and included in the study (276 children with LRTI and 121 with no evidence of LRTI). A presumptive microbiological cause was identified in 255 (92%) children with LRTI, with respiratory syncytial virus (127 [46%]), Haemophilus influenzae (70 [25%]), and Moraxella catarrhalis (65 [24%]) being most prevalent. mNGS identified uncommon pathogens including Ureaplasma parvum and Bocavirus. Co-detection of viral and bacterial pathogens occurred in 144 (52%) patients. Incidental carriage of potentially pathogenic microbes occurred in 82 (68%) children without LRTI, with rhinovirus (30 [25%]) being most prevalent. Respiratory syncytial virus (p

Published
2022

11. Global disparities in SARS-CoV-2 genomic surveillance

Author

Brito, Anderson F, Semenova, Elizaveta, Dudas, Gytis, Hassler, Gabriel W, Kalinich, Chaney C, Kraemer, Moritz UG, Ho, Joses, Tegally, Houriiyah, Githinji, George, Agoti, Charles N, Matkin, Lucy E, Whittaker, Charles, Howden, Benjamin P, Sintchenko, Vitali, Zuckerman, Neta S, Mor, Orna, Blankenship, Heather M, de Oliveira, Tulio, Lin, Raymond TP, Siqueira, Marilda Mendonça, Resende, Paola Cristina, Vasconcelos, Ana Tereza R, Spilki, Fernando R, Aguiar, Renato Santana, Alexiev, Ivailo, Ivanov, Ivan N, Philipova, Ivva, Carrington, Christine VF, Sahadeo, Nikita SD, Branda, Ben, Gurry, Céline, Maurer-Stroh, Sebastian, Naidoo, Dhamari, von Eije, Karin J, Perkins, Mark D, van Kerkhove, Maria, Hill, Sarah C, Sabino, Ester C, Pybus, Oliver G, Dye, Christopher, Bhatt, Samir, Flaxman, Seth, Suchard, Marc A, Grubaugh, Nathan D, Baele, Guy, and Faria, Nuno R

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Health Sciences, Prevention, Human Genome, Biodefense, Vaccine Related, Immunization, Emerging Infectious Diseases, Genetics, Infection, Good Health and Well Being, Humans, SARS-CoV-2, Genome, Viral, COVID-19, Pandemics, Genomics, Bulgarian SARS-CoV-2 sequencing group, Communicable Diseases Genomics Network, COVID-19 Impact Project, Danish Covid-19 Genome Consortium, Fiocruz COVID-19 Genomic Surveillance Network, GISAID core curation team, Network for Genomic Surveillance in South Africa, Swiss SARS-CoV-2 Sequencing Consortium
Abstract

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time

Published
2022

12. Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades

Author

Klitting, Raphaëlle, Kafetzopoulou, Liana E, Thiery, Wim, Dudas, Gytis, Gryseels, Sophie, Kotamarthi, Anjali, Vrancken, Bram, Gangavarapu, Karthik, Momoh, Mambu, Sandi, John Demby, Goba, Augustine, Alhasan, Foday, Grant, Donald S, Okogbenin, Sylvanus, Ogbaini-Emovo, Ephraim, Garry, Robert F, Smither, Allison R, Zeller, Mark, Pauthner, Matthias G, McGraw, Michelle, Hughes, Laura D, Duraffour, Sophie, Günther, Stephan, Suchard, Marc A, Lemey, Philippe, Andersen, Kristian G, and Dellicour, Simon

Subjects
Infectious Diseases, Emerging Infectious Diseases, Rare Diseases, Aetiology, 2.2 Factors relating to the physical environment, Infection, Life on Land, Animals, Humans, Lassa Fever, Lassa virus, Phylogeography, Risk Factors, Rodentia
Abstract

Lassa fever is a severe viral hemorrhagic fever caused by a zoonotic virus that repeatedly spills over to humans from its rodent reservoirs. It is currently not known how climate and land use changes could affect the endemic area of this virus, currently limited to parts of West Africa. By exploring the environmental data associated with virus occurrence using ecological niche modelling, we show how temperature, precipitation and the presence of pastures determine ecological suitability for virus circulation. Based on projections of climate, land use, and population changes, we find that regions in Central and East Africa will likely become suitable for Lassa virus over the next decades and estimate that the total population living in ecological conditions that are suitable for Lassa virus circulation may drastically increase by 2070. By analysing geotagged viral genomes using spatially-explicit phylogeography and simulating virus dispersal, we find that in the event of Lassa virus being introduced into a new suitable region, its spread might remain spatially limited over the first decades.

Published
2022

13. Emergence of an early SARS-CoV-2 epidemic in the United States.

Author

Zeller, Mark, Gangavarapu, Karthik, Anderson, Catelyn, Smither, Allison R, Vanchiere, John A, Rose, Rebecca, Snyder, Daniel J, Dudas, Gytis, Watts, Alexander, Matteson, Nathaniel L, Robles-Sikisaka, Refugio, Marshall, Maximilian, Feehan, Amy K, Sabino-Santos, Gilberto, Bell-Kareem, Antoinette R, Hughes, Laura D, Alkuzweny, Manar, Snarski, Patricia, Garcia-Diaz, Julia, Scott, Rona S, Melnik, Lilia I, Klitting, Raphaëlle, McGraw, Michelle, Belda-Ferre, Pedro, DeHoff, Peter, Sathe, Shashank, Marotz, Clarisse, Grubaugh, Nathan D, Nolan, David J, Drouin, Arnaud C, Genemaras, Kaylynn J, Chao, Karissa, Topol, Sarah, Spencer, Emily, Nicholson, Laura, Aigner, Stefan, Yeo, Gene W, Farnaes, Lauge, Hobbs, Charlotte A, Laurent, Louise C, Knight, Rob, Hodcroft, Emma B, Khan, Kamran, Fusco, Dahlene N, Cooper, Vaughn S, Lemey, Phillipe, Gardner, Lauren, Lamers, Susanna L, Kamil, Jeremy P, Garry, Robert F, Suchard, Marc A, and Andersen, Kristian G

Subjects
Humans, Risk Factors, Disease Outbreaks, Phylogeny, Travel, United States, Louisiana, Texas, Databases as Topic, Epidemics, COVID-19, SARS-CoV-2, genomic epidemiology, mobility, phylogenetics, viral emergence, viral sequencing, Emerging Infectious Diseases, Biodefense, Prevention, Vaccine Related, Lung, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.

Published
2021

15. Estimating effective population size changes from preferentially sampled genetic sequences.

Author

Karcher, Michael D, Carvalho, Luiz Max, Suchard, Marc A, Dudas, Gytis, and Minin, Vladimir N

Subjects
q-bio.PE, stat.ME, Bioinformatics, Mathematical Sciences, Biological Sciences, Information and Computing Sciences
Abstract

Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa.

Published
2020

16. Estimating effective population size changes from preferentially sampled genetic sequences

Author

Karcher, Michael D., Suchard, Marc A., Dudas, Gytis, and Minin, Vladimir N.

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Methodology
Abstract

Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa., Comment: 47 pages

Published
2019
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17. Fitting stochastic epidemic models to gene genealogies using linear noise approximation

Author

Tang, Mingwei, Dudas, Gytis, Bedford, Trevor, and Minin, Vladimir N.

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Methodology
Abstract

Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches, parametric approaches, and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard-to-interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia., Comment: 43 pages, 6 figures in the main text

Published
2019

18. Antibody escape and global spread of SARS-CoV-2 lineage A.27

Author

Kaleta, Tamara, Kern, Lisa, Hong, Samuel Leandro, Hölzer, Martin, Kochs, Georg, Beer, Julius, Schnepf, Daniel, Schwemmle, Martin, Bollen, Nena, Kolb, Philipp, Huber, Magdalena, Ulferts, Svenja, Weigang, Sebastian, Dudas, Gytis, Wittig, Alice, Jaki, Lena, Padane, Abdou, Lagare, Adamou, Salou, Mounerou, Ozer, Egon Anderson, Nnaemeka, Ndodo, Odoom, John Kofi, Rutayisire, Robert, Benkahla, Alia, Akoua-Koffi, Chantal, Ouedraogo, Abdoul-Salam, Simon-Lorière, Etienne, Enouf, Vincent, Kröger, Stefan, Calvignac-Spencer, Sébastien, Baele, Guy, Panning, Marcus, and Fuchs, Jonas

Published
2022
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19. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak.

Author

Dellicour, Simon, Baele, Guy, Dudas, Gytis, Faria, Nuno R, Pybus, Oliver G, Suchard, Marc A, Rambaut, Andrew, and Lemey, Philippe

Subjects
Humans, Hemorrhagic Fever, Ebola, Disease Outbreaks, Phylogeny, Genome, Viral, Geography, Time Factors, Africa, Western, Ebolavirus, Africa, Western, Genome, Viral, Hemorrhagic Fever, Ebola
Abstract

Genetic analyses have provided important insights into Ebola virus spread during the recent West African outbreak, but their implications for specific intervention scenarios remain unclear. Here, we address this issue using a collection of phylodynamic approaches. We show that long-distance dispersal events were not crucial for epidemic expansion and that preventing viral lineage movement to any given administrative area would, in most cases, have had little impact. However, major urban areas were critical in attracting and disseminating the virus: preventing viral lineage movement to all three capitals simultaneously would have contained epidemic size to one-third. We also show that announcements of border closures were followed by a significant but transient effect on international virus dispersal. By quantifying the hypothetical impact of different intervention strategies, as well as the impact of barriers on dispersal frequency, our study illustrates how phylodynamic analyses can help to address specific epidemiological and outbreak control questions.

Published
2018

21. Virus genomes reveal factors that spread and sustained the Ebola epidemic

Author

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, Faye, Ousmane, Gilbert, Merle L, Gevao, Sahr M, Gire, Stephen, Gladden-Young, Adrianne, Gnirke, Andreas, Goba, Augustine, Grant, Donald S, Haagmans, Bart L, Hiscox, Julian A, Jah, Umaru, Kugelman, Jeffrey R, Liu, Di, Lu, Jia, Malboeuf, Christine M, Mate, Suzanne, Matthews, David A, Matranga, Christian B, Meredith, Luke W, Qu, James, Quick, Joshua, Pas, Suzan D, Phan, My VT, Pollakis, Georgios, Reusken, Chantal B, Sanchez-Lockhart, Mariano, Schaffner, Stephen F, Schieffelin, John S, Sealfon, Rachel S, Simon-Loriere, Etienne, Smits, Saskia L, Stoecker, Kilian, Thorne, Lucy, Tobin, Ekaete Alice, Vandi, Mohamed A, Watson, Simon J, West, Kendra, Whitmer, Shannon, Wiley, Michael R, Winnicki, Sarah M, Wohl, Shirlee, Wölfel, Roman, Yozwiak, Nathan L, Andersen, Kristian G, Blyden, Sylvia O, Bolay, Fatorma, Carroll, Miles W, Dahn, Bernice, Diallo, Boubacar, Formenty, Pierre, Fraser, Christophe, Gao, George F, Garry, Robert F, Goodfellow, Ian, Günther, Stephan, Happi, Christian T, Holmes, Edward C, Kargbo, Brima, Keïta, Sakoba, Kellam, Paul, Koopmans, Marion PG, Kuhn, Jens H, Loman, Nicholas J, Magassouba, N’Faly, Naidoo, Dhamari, Nichol, Stuart T, Nyenswah, Tolbert, Palacios, Gustavo, Pybus, Oliver G, Sabeti, Pardis C, Sall, Amadou, Ströher, Ute, Wurie, Isatta, Suchard, Marc A, Lemey, Philippe, and Rambaut, Andrew

Subjects
Infectious Diseases, Emerging Infectious Diseases, Biodefense, Vaccine Related, Prevention, Infection, Good Health and Well Being, Climate, Disease Outbreaks, Ebolavirus, Genome, Viral, Geography, Hemorrhagic Fever, Ebola, Humans, Internationality, Linear Models, Molecular Epidemiology, Phylogeny, Travel, General Science & Technology
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.

Published
2017

22. Inference of evolutionary and ecological processes from reticulate evolution in RNA viruses

Author

Dudas, Gytis, Rambaut, Andrew, and Leigh-Brown, Andrew

Subjects
579.2, RNA viruses, reassortment, recombination, influenza, Ebola virus, MERS-CoV, phylogenetics
Abstract

RNA viruses have the fastest evolutionary rates amongst protein-coding organisms on the planet. Ease of sequencing, advanced techniques of analysis and global health and economic concerns have all contributed to the recognition of RNA viruses as a robust research platform. Phylogenetic methods have been at the forefront of analytical techniques used to understand the dynamics of RNA viruses - during natural circulation in populations and in individual hosts, within epidemics, across species barriers and over billions of years that viruses have been around. Most of the work presented in this thesis employs phylogenetic incongruity arising from reassortment and recombination to gain insights into the genomes and populations of RNA viruses. Chapter 2 explores the selection regimes Ebola virus has experienced following a year of circulation in humans inWest Africa, as well as its recent history. Chapter 3 investigates the extent of recombination in MERS-CoV, a novel human pathogen with an obscure epidemiology, which is suggestive of frequent co-infection of some hosts. Chapter 4, on the other hand, documents a pattern of non-intuitive linkage between some segments of the human-endemic influenza B virus genome and explores its potential to speciate. Chapter 5 builds upon chapter 4 and attempts to describe small-scale reassortment between two segments of influenza B virus and the overall migration patterns of influenza B virus in Scotland. Chapter 6 exploits the independence of segments of influenza D virus, a recently described cattle pathogen, and coalescent theory to disentangle the origins of this virus. This thesis exemplifies the success of modern sequencing methods, which, together with the use of sophisticated analytical techniques, have uncovered a wealth of information hidden away in molecular sequences of RNA viruses. The work presented herein demonstrates how reticulate evolution can be exploited as a reliable, and sometimes indispensable, marker to improve inference of evolutionary forces in RNA viruses.

Published
2016

24. Reassortment between influenza B lineages and the emergence of a co-adapted PB1-PB2-HA gene complex

Author

Dudas, Gytis, Bedford, Trevor, Lycett, Samantha, and Rambaut, Andrew

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Influenza B viruses make a considerable contribution to morbidity attributed to seasonal influenza. Currently circulating influenza B isolates are known to belong to two antigenically distinct lineages referred to as B/Victoria and B/Yamagata. Frequent exchange of genomic segments of these two lineages has been noted in the past, but the observed patterns of reassortment have not been formalized in detail. We investigate inter-lineage reassortments by comparing phylogenetic trees across genomic segments. Our analyses indicate that of the 8 segments of influenza B viruses only PB1, PB2 and HA segments maintained separate Victoria and Yamagata lineages and that currently circulating strains possess PB1, PB2 and HA segments derived entirely from one or the other lineage; other segments have repeatedly reassorted between lineages thereby reducing genetic diversity. We argue that this difference between segments is due to selection against reassortant viruses with mixed lineage PB1, PB2 and HA segments. Given sufficient time and continued recruitment to the reassortment-isolated PB1-PB2-HA gene complex, we expect influenza B viruses to eventually undergo sympatric speciation., Comment: 16 pages, 8 figures in main text, 18 pages, 17 figures in supplementary text

Published
2014

25. Emergence and spread of SARS-CoV-2 lineage B.1.620 with variant of concern-like mutations and deletions

Author

Dudas, Gytis, Hong, Samuel L., Potter, Barney I., Calvignac-Spencer, Sébastien, Niatou-Singa, Frédéric S., Tombolomako, Thais B., Fuh-Neba, Terence, Vickos, Ulrich, Ulrich, Markus, Leendertz, Fabian H., Khan, Kamran, Huber, Carmen, Watts, Alexander, Olendraitė, Ingrida, Snijder, Joost, Wijnant, Kim N., Bonvin, Alexandre M.J.J., Martres, Pascale, Behillil, Sylvie, Ayouba, Ahidjo, Maidadi, Martin Foudi, Djomsi, Dowbiss Meta, Godwe, Celestin, Butel, Christelle, Šimaitis, Aistis, Gabrielaitė, Miglė, Katėnaitė, Monika, Norvilas, Rimvydas, Raugaitė, Ligita, Koyaweda, Giscard Wilfried, Kandou, Jephté Kaleb, Jonikas, Rimvydas, Nasvytienė, Inga, Žemeckienė, Živilė, Gečys, Dovydas, Tamušauskaitė, Kamilė, Norkienė, Milda, Vasiliūnaitė, Emilija, Žiogienė, Danguolė, Timinskas, Albertas, Šukys, Marius, Šarauskas, Mantas, Alzbutas, Gediminas, Aziza, Adrienne Amuri, Lusamaki, Eddy Kinganda, Cigolo, Jean-Claude Makangara, Mawete, Francisca Muyembe, Lofiko, Emmanuel Lokilo, Kingebeni, Placide Mbala, Tamfum, Jean-Jacques Muyembe, Belizaire, Marie Roseline Darnycka, Essomba, René Ghislain, Assoumou, Marie Claire Okomo, Mboringong, Akenji Blaise, Dieng, Alle Baba, Juozapaitė, Dovilė, Hosch, Salome, Obama, Justino, Ayekaba, Mitoha Ondo’o, Naumovas, Daniel, Pautienius, Arnoldas, Rafaï, Clotaire Donatien, Vitkauskienė, Astra, Ugenskienė, Rasa, Gedvilaitė, Alma, Čereškevičius, Darius, Lesauskaitė, Vaiva, Žemaitis, Lukas, Griškevičius, Laimonas, and Baele, Guy

Published
2021
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26. Conserved ancestral tropical niche but different continental histories explain the latitudinal diversity gradient in brush-footed butterflies

Author

Chazot, Nicolas, Condamine, Fabien L., Dudas, Gytis, Peña, Carlos, Kodandaramaiah, Ullasa, Matos-Maraví, Pável, Aduse-Poku, Kwaku, Elias, Marianne, Warren, Andrew D., Lohman, David J., Penz, Carla M., DeVries, Phil, Fric, Zdenek F., Nylin, Soren, Müller, Chris, Kawahara, Akito Y., Silva-Brandão, Karina L., Lamas, Gerardo, Kleckova, Irena, Zubek, Anna, Ortiz-Acevedo, Elena, Vila, Roger, Vane-Wright, Richard I., Mullen, Sean P., Jiggins, Chris D., Wheat, Christopher W., Freitas, Andre V. L., and Wahlberg, Niklas

Published
2021
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27. Integrating influenza antigenic dynamics with molecular evolution

Author

Bedford, Trevor, Suchard, Marc A., Lemey, Philippe, Dudas, Gytis, Gregory, Victoria, Hay, Alan J., McCauley, John W., Russell, Colin A., Smith, Derek J., and Rambaut, Andrew

Subjects
Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution., Comment: 32 pages, 13 figures, 2 tables

Published
2013

28. Integrating influenza antigenic dynamics with molecular evolution

Author

Bedford, Trevor, Suchard, Marc A, Lemey, Philippe, Dudas, Gytis, Gregory, Victoria, Hay, Alan J, McCauley, John W, Russell, Colin A, Smith, Derek J, and Rambaut, Andrew

Subjects
Infectious Diseases, Vaccine Related, Influenza, Pneumonia & Influenza, Prevention, Biodefense, Emerging Infectious Diseases, Genetics, Aetiology, 2.2 Factors relating to the physical environment, Infection, Antigens, Viral, Evolution, Molecular, Genetic Drift, Genotype, Humans, Influenza A Virus, H1N1 Subtype, Influenza A Virus, H3N2 Subtype, Influenza B virus, Influenza Vaccines, Influenza, Human, Models, Genetic, Phenotype, Phylogeny, Seasons, Time Factors, Virulence, Bayesian inference, antigenic cartography, evolution, influenza, multidimensional scaling, phylogenetics, q-bio.PE, q-bio.QM, stat.AP, Biochemistry and Cell Biology
Abstract

Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution. DOI: http://dx.doi.org/10.7554/eLife.01914.001.

Published
2014

31. MERS coronaviruses from camels in Africa exhibit region-dependent genetic diversity

Author

Chu, Daniel K. W., Hui, Kenrie P. Y., Perera, Ranawaka A. P. M., Miguel, Eve, Niemeyer, Daniela, Zhao, Jincun, Channappanavar, Rudragouda, Dudas, Gytis, Oladipo, Jamiu O., Traoré, Amadou, Fassi-Fihri, Ouafaa, Ali, Abraham, Demissié, Getnet F., Muth, Doreen, Chan, Michael C. W., Nicholls, John M., Meyerholz, David K., Kuranga, Sulyman A., Mamo, Gezahegne, Zhou, Ziqi, So, Ray T. Y., Hemida, Maged G., Webby, Richard J., Roger, Francois, Rambaut, Andrew, Poon, Leo L. M., Perlman, Stanley, Drosten, Christian, Chevalier, Veronique, and Peiris, Malik

Published
2018

35. The evolution of Ebola virus: Insights from the 20132016 epidemic

Author

Holmes, Edward C., Dudas, Gytis, Rambaut, Andrew, and Andersen, Kristian G.

Subjects
Ebola hemorrhagic fever -- Distribution -- Forecasts and trends -- Causes of, Epidemics -- Causes of -- Forecasts and trends, Ebola virus -- Natural history -- Genetic aspects -- Health aspects, Market trend/market analysis, Company distribution practices, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

The 20132016 epidemic of Ebola virus disease in West Africa was of unprecedented magnitude and changed our perspective on this lethal but sporadically emerging virus. This outbreak also marked the beginning of large-scale real-time molecular epidemiology. Here, we show how evolutionary analyses of Ebola virus genome sequences provided key insights into virus origins, evolution and spread during the epidemic. We provide basic scientists, epidemiologists, medical practitioners and other outbreak responders with an enhanced understanding of the utility and limitations of pathogen genomic sequencing. This will be crucially important in our attempts to track and control future infectious disease outbreaks., Author(s): Edward C. Holmes (corresponding author) [1]; Gytis Dudas [2, 3]; Andrew Rambaut [3, 4, 5]; Kristian G. Andersen (corresponding author) [6, 7, 8] The 20132016 Ebola virus disease (EVD) [...]

Published
2016
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37. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak

Author

Gire, Stephen K., Goba, Augustine, Andersen, Kristian G., Sealfon, Rachel S. G., Park, Daniel J., Kanneh, Lansana, Jalloh, Simbirie, Momoh, Mambu, Fullah, Mohamed, Dudas, Gytis, Wohl, Shirlee, Moses, Lina M., Yozwiak, Nathan L., Winnicki, Sarah, Matranga, Christian B., Malboeuf, Christine M., Qu, James, Gladden, Adrianne D., Schaffner, Stephen F., Yang, Xiao, Jiang, Pan-Pan, Nekoui, Mahan, Colubri, Andres, Coomber, Moinya Ruth, Fonnie, Mbalu, Moigboi, Alex, Gbakie, Michael, Kamara, Fatima K., Tucker, Veronica, Konuwa, Edwin, Saffa, Sidiki, Sellu, Josephine, Jalloh, Abdul Azziz, Kovoma, Alice, Koninga, James, Mustapha, Ibrahim, Kargbo, Kandeh, Foday, Momoh, Yillah, Mohamed, Kanneh, Franklyn, Robert, Willie, Massally, James L. B., Chapman, Sinéad B., Bochicchio, James, Murphy, Cheryl, Nusbaum, Chad, Young, Sarah, Birren, Bruce W., Grant, Donald S., Scheiffelin, John S., Lander, Eric S., Happi, Christian, Gevao, Sahr M., Gnirke, Andreas, Rambaut, Andrew, Garry, Robert F., Khan, S. Humarr, and Sabeti, Pardis C.

Published
2014

38. Temporal and spatial analysis of the 2014–2015 Ebola virus outbreak in West Africa

Author

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

Published
2015
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39. Global disparities in SARS-CoV-2 genomic surveillance

Author

Brito, Anderson F, Semenova, Elizaveta, Dudas, Gytis, Hassler, Gabriel W, Kalinich, Chaney C, Kraemer, Moritz U G, Ho, Joses, Tegally, Houriiyah, Githinji, George, Agoti, Charles N, Matkin, Lucy E, Whittaker, Charles, Howden, Benjamin P, Sintchenko, Vitali, Zuckerman, Neta S, Mor, Orna, Blankenship, Heather M, de Oliveira, Tulio, Lin, Raymond T P, Siqueira, Marilda Mendonça, Resende, Paola Cristina, Vasconcelos, Ana Tereza R, Spilki, Fernando R, Aguiar, Renato Santana, Alexiev, Ivailo, Ivanov, Ivan N, Philipova, Ivva, Carrington, Christine V F, Sahadeo, Nikita S D, Branda, Ben, Gurry, Céline, Maurer-Stroh, Sebastian, Naidoo, Dhamari, von Eije, Karin J, Perkins, Mark D, van Kerkhove, Maria, Hill, Sarah C, Sabino, Ester C, Pybus, Oliver G, Dye, Christopher, Bhatt, Samir, Flaxman, Seth, Suchard, Marc A, Grubaugh, Nathan D, Baele, Guy, and Faria, Nuno R

Subjects
medicine.medical_specialty, Multidisciplinary, Coronavirus disease 2019 (COVID-19), SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Public health, viruses, COVID-19, General Physics and Astronomy, Genome, Viral, Genomics, General Chemistry, Biology, sequencing, Genome, DNA sequencing, Virus, General Biochemistry, Genetics and Molecular Biology, Temporal heterogeneity, Environmental health, Pandemic, medicine, Humans, Pandemics
Abstract

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time

Published
2021

40. Real-time, portable genome sequencing for Ebola surveillance

Author

Quick, Joshua, Loman, Nicholas J., Duraffour, Sophie, Simpson, Jared T., Severi, Ettore, Cowley, Lauren, Bore, Joseph Akoi, Koundouno, Raymond, Dudas, Gytis, Mikhail, Amy, Ouédraogo, Nobila, Afrough, Babak, Bah, Amadou, Baum, Jonathan H. J., Becker-Ziaja, Beate, Boettcher, Jan Peter, Cabeza-Cabrerizo, Mar, Camino-Sánchez, Álvaro, Carter, Lisa L., Doerrbecker, Juliane, Enkirch, Theresa, Dorival, Isabel García-, Hetzelt, Nicole, Hinzmann, Julia, Holm, Tobias, Kafetzopoulou, Liana Eleni, Koropogui, Michel, Kosgey, Abigael, Kuisma, Eeva, Logue, Christopher H., Mazzarelli, Antonio, Meisel, Sarah, Mertens, Marc, Michel, Janine, Ngabo, Didier, Nitzsche, Katja, Pallasch, Elisa, Patrono, Livia Victoria, Portmann, Jasmine, Repits, Johanna Gabriella, Rickett, Natasha Y., Sachse, Andreas, Singethan, Katrin, Vitoriano, Inês, Yemanaberhan, Rahel L., Zekeng, Elsa G., Racine, Trina, Bello, Alexander, Sall, Amadou Alpha, Faye, Ousmane, Faye, Oumar, Magassouba, NʼFaly, Williams, Cecelia V., Amburgey, Victoria, Winona, Linda, Davis, Emily, Gerlach, Jon, Washington, Frank, Monteil, Vanessa, Jourdain, Marine, Bererd, Marion, Camara, Alimou, Somlare, Hermann, Camara, Abdoulaye, Gerard, Marianne, Bado, Guillaume, Baillet, Bernard, Delaune, Déborah, Nebie, Koumpingnin Yacouba, Diarra, Abdoulaye, Savane, Yacouba, Pallawo, Raymond Bernard, Gutierrez, Giovanna Jaramillo, Milhano, Natacha, Roger, Isabelle, Williams, Christopher J., Yattara, Facinet, Lewandowski, Kuiama, Taylor, James, Rachwal, Phillip, Turner, Daniel J., Pollakis, Georgios, Hiscox, Julian A., Matthews, David A., Shea, Matthew K. Oʼ, Johnston, Andrew McD., Wilson, Duncan, Hutley, Emma, Smit, Erasmus, Di Caro, Antonino, Wölfel, Roman, Stoecker, Kilian, Fleischmann, Erna, Gabriel, Martin, Weller, Simon A., Koivogui, Lamine, Diallo, Boubacar, Keïta, Sakoba, Rambaut, Andrew, Formenty, Pierre, Günther, Stephan, and Carroll, Miles W.

Published
2016
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41. Predicting the evolution of Lassa Virus endemic area and population at risk over the next decades

Author

Klitting, Raphaëlle, primary, Kafetzopoulou, Liana E., additional, Thiery, Wim, additional, Dudas, Gytis, additional, Gryseels, Sophie, additional, Kotamarthi, Anjali, additional, Vrancken, Bram, additional, Gangavarapu, Karthik, additional, Momoh, Mambu, additional, Sandi, John Demby, additional, Goba, Augustine, additional, Alhasan, Foday, additional, Grant, Donald S., additional, Garry, Robert F., additional, Smither, Allison R., additional, Zeller, Mark, additional, Pauthner, Matthias G., additional, McGraw, Michelle, additional, Hughes, Laura D., additional, Duraffour, Sophie, additional, Günther, Stephan, additional, Suchard, Marc A., additional, Lemey, Philippe, additional, Andersen, Kristian G., additional, and Dellicour, Simon, additional

Published
2021
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43. Global genome analysis reveals a vast and dynamic anellovirus landscape within the human virome

Author

Arze, Cesar A., primary, Springer, Simeon, additional, Dudas, Gytis, additional, Patel, Sneha, additional, Bhattacharyya, Agamoni, additional, Swaminathan, Harish, additional, Brugnara, Carlo, additional, Delagrave, Simon, additional, Ong, Tuyen, additional, Kahvejian, Avak, additional, Echelard, Yann, additional, Weinstein, Erica G., additional, Hajjar, Roger J., additional, Andersen, Kristian G., additional, and Yozwiak, Nathan L., additional

Published
2021
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44. Variant Analysis of SARS-CoV-2 Genomes from Belgian Military Personnel Engaged in Overseas Missions and Operations

Author

Pirnay, Jean-Paul, primary, Selhorst, Philippe, additional, Hong, Samuel L., additional, Cochez, Christel, additional, Potter, Barney, additional, Maes, Piet, additional, Petrillo, Mauro, additional, Dudas, Gytis, additional, Claes, Vincent, additional, Van der Beken, Yolien, additional, Verbeken, Gilbert, additional, Degueldre, Julie, additional, Dellicour, Simon, additional, Cuypers, Lize, additional, T’Sas, France, additional, Van den Eede, Guy, additional, Verhasselt, Bruno, additional, Weuts, Wouter, additional, Smets, Cedric, additional, Mertens, Jan, additional, Geeraerts, Philippe, additional, Ariën, Kevin K., additional, André, Emmanuel, additional, Neirinckx, Pierre, additional, Soentjens, Patrick, additional, and Baele, Guy, additional

Published
2021
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46. Travel-driven emergence and spread of SARS-CoV-2 lineage B.1.620 with multiple VOC-like mutations and deletions in Europe

Author

Dudas, Gytis, primary, Hong, Samuel L., additional, Potter, Barney, additional, Calvignac-Spencer, Sébastien, additional, Niatou-Singa, Frédéric S., additional, Tombolomako, Thais B., additional, Fuh-Neba, Terence, additional, Vickos, Ulrich, additional, Ulrich, Markus, additional, Leendertz, Fabian H., additional, Khan, Kamran, additional, Watts, Alexander, additional, Olendraitė, Ingrida, additional, Snijder, Joost, additional, Wijnant, Kim N., additional, Bonvin, Alexandre M.J.J., additional, Martres, Pascale, additional, Behillil, Sylvie, additional, Ayouba, Ahidjo, additional, Maidadi, Martin Foudi, additional, Djomsi, Dowbiss Meta, additional, Godwe, Celestin, additional, Butel, Christelle, additional, Šimaitis, Aistis, additional, Gabrielaitė, Miglė, additional, Katėnaitė, Monika, additional, Norvilas, Rimvydas, additional, Raugaitė, Ligita, additional, Jonikas, Rimvydas, additional, Nasvytienė, Inga, additional, Žemeckienė, Živilė, additional, Gečys, Dovydas, additional, Tamušauskaitė, Kamilė, additional, Norkienė, Milda, additional, Vasiliūnaitė, Emilija, additional, Žiogienė, Danguolė, additional, Timinskas, Albertas, additional, Šukys, Marius, additional, Šarauskas, Mantas, additional, Alzbutas, Gediminas, additional, Juozapaitė, Dovilė, additional, Naumovas, Daniel, additional, Pautienius, Arnoldas, additional, Vitkauskienė, Astra, additional, Ugenskienė, Rasa, additional, Gedvilaitė, Alma, additional, Čereškevičius, Darius, additional, Lesauskaitė, Vaiva, additional, Žemaitis, Lukas, additional, Griškevičius, Laimonas, additional, and Baele, Guy, additional

Published
2021
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49. VIRAL EVOLUTION: Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak

Author

Gire, Stephen K., Goba, Augustine, Andersen, Kristian G., Sealfon, Rachel S. G., Park, Daniel J., Kanneh, Lansana, Jalloh, Simbirie, Momoh, Mambu, Fullah, Mohamed, Dudas, Gytis, Wohl, Shirlee, Moses, Lina M., Yozwiak, Nathan L., Winnicki, Sarah, Matranga, Christian B., Malboeuf, Christine M., Qu, James, Gladden, Adrianne D., Schaffner, Stephen F., Yang, Xiao, Jiang, Pan-Pan, Nekoui, Mahan, Colubri, Andres, Coomber, Moinya Ruth, Fonnie, Mbalu, Moigboi, Alex, Gbakie, Michael, Kamara, Fatima K., Tucker, Veronica, Konuwa, Edwin, Saffa, Sidiki, Sellu, Josephine, Jalloh, Abdul Azziz, Kovoma, Alice, Koninga, James, Mustapha, Ibrahim, Kargbo, Kandeh, Foday, Momoh, Yillah, Mohamed, Kanneh, Franklyn, Robert, Willie, Massally, James L. B., Chapman, Sinéad B., Bochicchio, James, Murphy, Cheryl, Nusbaum, Chad, Young, Sarah, Birren, Bruce W., Grant, Donald S., Scheiffelin, John S., Lander, Eric S., Happi, Christian, Gevao, Sahr M., Gnirke, Andreas, Rambaut, Andrew, Garry, Robert F., Khan, S. Humarr, and Sabeti, Pardis C.

Published
2014
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