Virus genomes reveal factors that spread and sustained the Ebola epidemic
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Citations
Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10
Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples
TreeTime: Maximum-likelihood phylodynamic analysis
Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool.
Towards a genomics-informed, real-time, global pathogen surveillance system.
References
MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform
Bayesian Phylogenetics with BEAUti and the BEAST 1.7
Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.
Relaxed Phylogenetics and Dating with Confidence
Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods
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Frequently Asked Questions (11)
Q2. How many reintroductions of EBOV into Guinea from April 2014 to February?
Their analysis reveals that there were at least 21 (95% CI: 16 - 25) re-introductions into Guinea from April 2014 to February 2015.
Q3. How did the authors obtain the posterior estimate of the phylogenetic CTMC process?
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.
Q4. Where do putative tracts of T-to-C hypermutation occur?
Putative tracts of T-to-C hypermutation almost exclusively occur within non-coding intergenic regions, where their effects on viral fitness are presumably minimal.
Q5. What is the value of a pre-publication data sharing tool?
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.
Q6. How did the authors set the prior inclusion probabilities?
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.
Q7. What prevented some of these regions from becoming part of the EVD epidemic?
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.
Q8. How do the authors consider time-inhomogeneity in the spatial diffusion process?
To consider time-inhomogeneity in the spatial diffusion process, the authors start by borrowing epoch modelling concepts from Bielejec et al. (2014)57.
Q9. How do the authors determine the probability of no predictors being included?
using the distribution function of a binomial random variable q = 1− w1/P , where P is the number of predictors, as before.
Q10. What are the effects of random effects on the diffusion process?
These random effects account for unexplained variability in the diffusion process that may otherwise lead to spurious inclusion of predictors.
Q11. What is the relative preference of transitioning between location pairs that are in different countries?
The relative preference of transitioning between location pairs that are in the same country and share at least one of 17 vernacular languages Climatic OrTemp Temperature annual mean at origin, log-transformed, standardized Climatic DestTemp Temperature annual mean at destination, log-transformed, standardized Climatic OrTempSS