Comparison of the key characteristics of EpiFusion compared to the tools and literature referred to in this manuscript.

Comparison of the key characteristics of EpiFusion compared to the tools and literature referred to in this manuscript.

<p>Rasmussen’11 denotes Rasmussen et. al (2011), which was referenced in the introduction. However, the model is not distributed for use as a software or program, so we were unable to assess its computational efficiency (*). (BD–birth death).</p>

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Bibliographic Details
Main Author: Ciara Judge (20161514) (author)
Other Authors: Timothy Vaughan (5654278) (author), Timothy Russell (20161517) (author), Sam Abbott (9548946) (author), Louis du Plessis (3264087) (author), Tanja Stadler (118822) (author), Oliver Brady (5331668) (author), Sarah Hill (101064) (author)
Published: 2024
Subjects:
Genetics
Biotechnology
Evolutionary Biology
Cancer
Infectious Diseases
Computational Biology
Mathematical Sciences not elsewhere classified
surveillance rapidly changes
scaled trees estimated
remain largely distinct
poorly sampled epidemics
effective reproduction number
accommodate future extensions
respiratory virus outbreaks
approach scales efficiently
2014 ebola outbreak
phylodynamic observation models
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simulated outbreaks
outbreak dynamics
joint approach
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weighted according
sierra leone
retrospective analysis
phylogenetic uncertainty
pathogen phylodynamics
joint inference
integrating phylodynamic
infectious disease
increases certainty
genetic sequences
fundamental challenge
existing methods
demonstrate advances
dataset size
complementary strengths
circulating pathogen
case incidence
accurately estimating
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Summary:<p>Rasmussen’11 denotes Rasmussen et. al (2011), which was referenced in the introduction. However, the model is not distributed for use as a software or program, so we were unable to assess its computational efficiency (*). (BD–birth death).</p>

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