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

Alizon, Samuel; Lion, Sébastien; Murall, Carmen Lía; Abbate, Jessica Lee

doi:10.4161/21505594.2014.976514

Cited by 26 publications

(21 citation statements)

References 19 publications

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“…In the 2014 Ebola epidemic, we have identified a genetic variant that has a substantially higher growth rate than its progenitor lineage [4,21,26]. We conclude that a viral epidemic can develop strong growth heterogeneity even on the limited temporal and spatial scales of its initial outbreak.…”

Section: Discussionmentioning

confidence: 80%

A Genomic Landscape of Haplotype Diversity and Signatures of Phylogeographic Distribution in Zaire Ebolavirus during the 2014 EVD Epidemic

Teng¹,

Yu²,

Yuan³

et al. 2016

Ebola

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The Ebola virus (EBOV) disease epidemic from 2013 to 2015 is the largest in history, affecting multiple countries in West Africa. Genome sequencing of EBOV has revealed extensive genetic variation and mutation rate. The evolution and the variations among genotypes of EBOV observed remain low, which suggests that the viral haplotypes may be common in this transmission. To address this hypothesis, we investigated the genomic portrait of haplotype diversity in EBOV from 1976 to the 2014 outbreaks. We obtained 176 haplotypes in 305 gene-coding sequences of EBOV and found that the Hap8 in multiple viral haplotypes is the major epidemic lineage in the 2014 Sierra Leone outbreak. The phylogeographic analysis of EBOV transmission in Sierra Leone during 2014 outbreaks indicated that the genetic flow in EBOV was no more likely to occur within or without populations and the correlation between genetic and geographical distance is not significant. Our study first detected the diversity of viral haplotypes with systematic calculation of phylogeographic distribution in EBOV. This observation highlighted how Ebola virus is substantially different in virulence or transmissibility in comparison to the virus lineages associated with 2014 outbreaks in Sierra Leone, which provides a clue to understand the 2014 EBOV spreading.

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Section: Discussionmentioning

confidence: 80%

A Genomic Landscape of Haplotype Diversity and Signatures of Phylogeographic Distribution in Zaire Ebolavirus during the 2014 EVD Epidemic

Teng¹,

Yu²,

Yuan³

et al. 2016

Ebola

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“…By assuming a range of durations of the infectivity periods and different proportions of transmitters in a population, we were able to estimate the generation time of HCV in different populations in Greece by combining genetic and surveillance data (Magiorkinis et al, 2013). During the 2009 Influenza outbreak and the 2014 Ebola outbreak researchers have shown that epidemiological parameters inferred through molecular data are similar to those coming from the count-based epidemiological studies (Fraser et al, 2009, Alizon et al, 2014), suggesting that molecular data are reliable for epidemiological parameters estimations.…”

Section: Epidemics On a Macro Scale: Inferring The Statistics Of Epidmentioning

confidence: 82%

Integrating molecular epidemiology and social network analysis to study infectious diseases: Towards a socio-molecular era for public health

Vasylyeva

Friedman

Paraskevis³

et al. 2016

Infection, Genetics and Evolution

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The number of public health applications for molecular epidemiology and social network analysis has increased rapidly since the improvement in computational capacities and the development of new sequencing techniques. Currently, molecular epidemiology methods are used in a variety of settings: from infectious disease surveillance systems to the description of disease transmission pathways. The latter are of great epidemiological importance as they let us describe how a virus spreads in a community, make predictions for the further epidemic developments, and plan preventive interventions. Social network methods are used to understand how infections spread through communities and what the risk factors for this are, as well as in improved contact tracing and message-dissemination interventions. Research is needed on how to combine molecular and social network data as both include essential, but not fully sufficient information on infection transmission pathways. The main differences between the two data sources are that, firstly, social network data include uninfected individuals unlike the molecular data sampled only from infected network members. Thus, social network data include more detailed picture of a network and can improve inferences made from molecular data. Secondly, network data refer to the current state and interactions within the social network, while molecular data refer to the time points when transmissions happened, which might have happened years before the sampling date. As of today, there have been attempts to combine and compare the data obtained from the two sources. Even though there is no consensus on whether and how social and genetic data complement each other, this research might significantly improve our understanding of how viruses spread through communities.

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“…Consequently, nonparametric models may not always be able to reconstruct complex dynamics, as for dengue virus in southern Vietnam, where seasonality, vector dynamics, and spatial structure were all found to play important roles [10]. Similarly, simplifying epidemiological dynamics may also lead to biases, as in the 2014 West African Ebola epidemic where models that ignore the incubation period returned slightly different estimates than those accounting for an incubation period [12][13][14]. Phylodynamic inference methods have been used to infer complex dynamics of, for example, HIV, hepatitis C virus, rabies, influenza, dengue virus, and Ebola virus.…”

Section: Science and Societymentioning

confidence: 92%

“…Using phylodynamic methods we estimated the basic reproduction number in Sierra Leone to be 2.18 [95% confidence interval (CI) 1.24-3.55] [13], which is in line with the WHO estimate based on surveillance data of 2.02 (95% CI 1.79-2.26) [1]. However, other estimates based on incidence data alone are highly divergent, possibly due to variations in the reporting rate and sensitivity to model misspecification [13,14]. Moreover, while inferences about population structure are difficult to make from incidence data alone, phylodynamic inferences from genetic data suggest the presence of superspreaders [12,13].…”

Section: Science and Societymentioning

confidence: 95%

“…In 2009, the WHO Rapid Pandemic Assessment Collaboration was able to rapidly produce an assessment of the pandemic potential of the influenza H1N1 outbreak using phylodynamic methods [2]. During the 2014 West African Ebola virus epidemic, key epidemiological parameters were derived directly from genetic data sampled during the initial phase of the outbreak in Sierra Leone [3,[12][13][14]. Using phylodynamic methods we estimated the basic reproduction number in Sierra Leone to be 2.18 [95% confidence interval (CI) 1.24-3.55] [13], which is in line with the WHO estimate based on surveillance data of 2.02 (95% CI 1.79-2.26) [1].…”

Section: Science and Societymentioning

confidence: 99%

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Getting to the root of epidemic spread with phylodynamic analysis of genomic data

Plessis

Stadler

2015

Trends in Microbiology

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

Cited by 26 publications

References 19 publications

A Genomic Landscape of Haplotype Diversity and Signatures of Phylogeographic Distribution in Zaire Ebolavirus during the 2014 EVD Epidemic

A Genomic Landscape of Haplotype Diversity and Signatures of Phylogeographic Distribution in Zaire Ebolavirus during the 2014 EVD Epidemic

Integrating molecular epidemiology and social network analysis to study infectious diseases: Towards a socio-molecular era for public health

Getting to the root of epidemic spread with phylodynamic analysis of genomic data

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