Epidemiological dynamics of Ebola outbreaks

House, Thomas

doi:10.7554/elife.03908

Cited by 27 publications

(34 citation statements)

References 11 publications

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“…Recent studies using those methods found the Sierra Leone outbreak had an R 0 of between 2.02 (95% CI; 1.79-2.26) 14 and 2.53 (2.41-2.67) 15 and the lower bound of previous outbreaks ranged between 1.34 and 3.65. 16 However, sequences collected from patients receiving care will account for public health control measures and so these methods should therefore be considered as measuring the R e . Indeed, our estimate is closer to the R e found using conventional count-based methods (ranging between 1.38 and 1.5 for the 2014 Ebola outbreak.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the epidemic spread of Ebola virus (EBOV) in Sierra Leone using phylodynamics

et al. 2014

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Section: Discussionmentioning

confidence: 99%

Quantifying the epidemic spread of Ebola virus (EBOV) in Sierra Leone using phylodynamics

et al. 2014

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“…The growing concern over the repeated emergence of EVD (7,8) has stimulated many studies on these outbreaks, in particular on the most recent one in West Africa, trying to understand the transmission mechanisms and the effectiveness of containment strategies (9)(10)(11)(12)(13)(14)(15)(16)(17)(18). These studies, however, either used limited data at an early stage of the epidemic or assessed a single influential factor.…”

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confidence: 99%

Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone

Fang

Yang

Jiang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

101

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Sierra Leone is the most severely affected country by an unprecedented outbreak of Ebola virus disease (EVD) in West Africa. Although successfully contained, the transmission dynamics of EVD and the impact of interventions in the country remain unclear. We established a database of confirmed and suspected EVD cases from May 2014 to September 2015 in Sierra Leone and mapped the spatiotemporal distribution of cases at the chiefdom level. A Poisson transmission model revealed that the transmissibility at the chiefdom level, estimated as the average number of secondary infections caused by a patient per week, was reduced by 43% [95% confidence interval (CI): 30%, 52%] after October 2014, when the strategic plan of the United Nations Mission for Emergency Ebola Response was initiated, and by 65% (95% CI: 57%, 71%) after the end of December 2014, when 100% case isolation and safe burials were essentially achieved, both compared with before October 2014. Population density, proximity to Ebola treatment centers, cropland coverage, and atmospheric temperature were associated with EVD transmission. The household secondary attack rate (SAR) was estimated to be 0.059 (95% CI: 0.050, 0.070) for the overall outbreak. The household SAR was reduced by 82%, from 0.093 to 0.017, after the nationwide campaign to achieve 100% case isolation and safe burials had been conducted. This study provides a complete overview of the transmission dynamics of the 2014−2015 EVD outbreak in Sierra Leone at both chiefdom and household levels. The interventions implemented in Sierra Leone seem effective in containing the epidemic, particularly in interrupting household transmission.Ebola virus disease | spatiotemporal modeling | household transmission | secondary attack rate | intervention effectiveness

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“…Historically, outbreaks have been confined to isolated areas within Central Africa; however, the 2014 outbreak reached an unprecedented level, making this the largest outbreak since the discovery of the virus in 1976 (2).…”

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confidence: 99%

Both Epistasis and Diversifying Selection Drive the Structural Evolution of the Ebola Virus Glycoprotein Mucin-Like Domain

Ibeh

Nshogozabahizi

Aris‐Brosou

2016

J Virol

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Throughout the last 3 decades, Ebola virus (EBOV) outbreaks have been confined to isolated areas within Central Africa; however, the 2014 variant reached unprecedented transmission and mortality rates. While the outbreak was still under way, it was reported that the variant leading up to this outbreak evolved faster than previous EBOV variants, but evidence for diversifying selection was undetermined. Here, we test this selection hypothesis and show that while previous EBOV outbreaks were preceded by bursts of diversification, evidence for site-specific diversifying selection during the emergence of the 2014 EBOV clade is weak. However, we show strong evidence supporting an interplay between selection and correlated evolution (epistasis), particularly in the mucin-like domain (MLD) of the EBOV glycoprotein. By reconstructing ancestral structures of the MLD, we further propose a structural mechanism explaining how the substitutions that accumulated between 1918 and 1969 distorted the MLD, while more recent epistatic substitutions restored part of the structure, with the most recent substitution being adaptive. We suggest that it is this complex interplay between weak selection, epistasis, and structural constraints that has shaped the evolution of the 2014 EBOV variant. IMPORTANCEThe role that selection plays in the emergence of viral epidemics remains debated, particularly in the context of the 2014 EBOV outbreak. Most critically, should such evidence exist, it is generally unclear how this relates to function and increased virulence. Here, we show that the viral lineage leading up to the 2014 outbreak underwent a complex interplay between selection and correlated evolution (epistasis) in a protein region that is critical for immune evasion. We then reconstructed the three-dimensional structure of this domain and showed that the initial mutations in this lineage deformed the structure, while subsequent mutations restored part of the structure. Along this mutational path, the first and last mutations were adaptive, while the intervening ones were epistatic. Altogether, we provide a mechanistic model that explains how selection and epistasis acted on the structural constraints that materialized during the 2014 EBOV outbreak. The five viruses that constitute the genus Ebolavirus (Zaire ebolavirus [EBOV], Sudan ebolavirus, Bundibugyo ebolavirus, Reston ebolavirus, and Tai Forest ebolavirus) have been the cause of a major public health concern in sub-Saharan Africa for over 3 decades (1). Historically, outbreaks have been confined to isolated areas within Central Africa; however, the 2014 outbreak reached an unprecedented level, making this the largest outbreak since the discovery of the virus in 1976 (2).The primary analysis of EBOV isolates conducted by Gire and colleagues found a large number of nonsynonymous mutations between the 2014 EBOV sequences and all previously published EBOV sequences (3). In particular, 50 fixed nonsynonymous changes were observed, all of which were distinct to the 2014 EBOV vari...

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Epidemiological dynamics of Ebola outbreaks

Cited by 27 publications

References 11 publications

Quantifying the epidemic spread of Ebola virus (EBOV) in Sierra Leone using phylodynamics

Quantifying the epidemic spread of Ebola virus (EBOV) in Sierra Leone using phylodynamics

Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone

Both Epistasis and Diversifying Selection Drive the Structural Evolution of the Ebola Virus Glycoprotein Mucin-Like Domain

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