Virus genomes reveal factors that spread and sustained the Ebola epidemic
By G. Dudas, L. Carvalho, Trevor Bedford, Andrew J. Tatem, Guy Baele, N. Faria, Daniel J. Park, J. Ladner, A. Arias, D. Asogun and 86 more. Nature, 2017
389 citations recorded by the source corpus.
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.
Subfields: Infectious Diseases, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/3a914c9e88f50a396d67f03c968cc1b6a72a6882
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In Infectious Diseases
Citations in the corpus
- Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 (2018, positive): “The estimates are based on a large data set of 1,610 genomes that represent over 5 per cent of the known cases (Dudas et al. 2017).”
- Fast hierarchical Bayesian analysis of population structure (2018, methodology): “The two viral datasets comprised a subset of sequences from the recent Ebola outbreak as well as a global dataset of over 110 000 HIV partial pol genes (positions 1–1497) obtained from the Los Alamos HIV Database (44,45), with associated meta-data on subtype and country of sampling.”
- Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations (2023, neutral): “Traditionally, phylodynamic approaches have been employed to retrospectively characterize lineage dynamics during outbreaks of viruses such as Zika [13–15], West Nile [16] and Ebola viruses [17,18].”
- GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens (2017, positive): “Toillustratethisflexibility, Figure3showsaGrapeTreerepresentationofaphylogenetictreeof 1610 Ebola genomes from the 2013 – 2016 Ebola epidemic in West Africa (Dudas et al. 2017) which was downloaded together with associated metadata from Microreact (Argimon et al. 2016).”
- TreeTime: Maximum-likelihood phylodynamic analysis (2017, background): “The genomic epidemiology has been studied intensively by multiple groups (Dudas et al. 2017).”
- Towards a genomics-informed, real-time, global pathogen surveillance system (2017, background): “By the time it was reported to be Ebola, the virus had spread to three countries 1 and was likely past the point at which case-level control measures, such as isolation and infection control, could have contained the nascent outbreak.”