Unifying the Epidemiological and Evolutionary Dynamics of Pathogens

By Bryan T. Grenfell, Oliver George Pybus, J. Gog, J. Wood, J. Daly, J. Mumford, Edward C. Holmes. Science, 2004

1,389 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Genetics, Virology

View Paper: https://www.semanticscholar.org/paper/00c5244fd459309dc7e179f6adb63371390732c3

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Citations in the corpus

Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 (2018, background): “Since inception, a strong motivation for BEAST development has been the rapid growth of pathogen genome sequencing as part of public health responses to infectious diseases (Grenfell et al. 2004).”
Molecular phylogenetics: principles and practice (2012, methodology): “…are used to describe relationships between paralogues in a gene family 1 , histories of populations 2 , the evolutionary and epidemiological dynamics of pathogens 3,4 , the genealogical relationship of somatic cells during differentiation and cancer development 5 and the evolution of language 6 .”
The early spread and epidemic ignition of HIV-1 in human populations (2014, background): “Epidemiological processes such as geographic spread and population growth leave a measurable imprint on HIV-1 genomes sampled from infected individuals at different places and times (24, 80).”
Viral Phylodynamics (2013, background): “The Relative Lengths of Internal Versus External Branches Will Be Affected by Changes in Viral Population Size over Time (see Figure 1) [1] Rapid expansion of a virus in a population will be reflected by a ‘‘star-like’’ tree, in which external branches are long relative to internal branches.”
The Evolution and Emergence of RNA Viruses (2009, background): “This has motivated the development of phylodynamics, a new area of research that attempts to achieve a synthesis between epidemiology and phylogenetics (Grenfell et al. 2004).”
Fast Dating Using Least-Squares Criteria and Algorithms (2015, background): “Moreover, the ladder-shaped trees (995/1000 death rate) are easier than the more star-like trees (750/1000), an outcome already observed with real data, for example from human seasonal influenza (ladder-shaped) versus HIV (star-like) epidemics (Grenfell et al. 2004).”
The co-evolution of host cationic antimicrobial peptides and microbial resistance (2006, background): “The key issues in microbial infection — adherence to, and invasion of, host cells, and evasion of host defences, on the one hand, and the host strategies to prevent microbial colonization and tissue damage on the other — are a result of ongoing, highly dynamic co-evolutionary processe”
Modeling infectious disease dynamics in the complex landscape of global health (2015, positive): “As pathogen genetic data become increasingly available, modelers are finding ways to synthesize these new data streams with more traditional epidemiological information in phylodynamic tools (27,28).”