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Can Evolutionary Tools Reliably Tell Us about Dengue Virus' Past Outbreaks?
Author(s) -
Joseph Caspermeyer
Publication year - 2013
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/mst233
Subject(s) - dengue fever , dengue virus , biology , viral phylodynamics , outbreak , population , virology , evolutionary dynamics , evolutionary biology , serotype , infectious disease (medical specialty) , disease , ecology , demography , phylogenetics , genetics , medicine , pathology , sociology , gene
The mosquito-borne dengue virus is most prevalent in southeastern Asia, with four common strains or “serotypes” of the virus infecting up to 10% of children in Vietnam annually. Dengue virus is a major challenge for evolutionary biologists because of its complex ecology and rapidly changing disease dynamics. But can evolutionary models become a reliable tool for epidemiologists studying infectious disease? Coming up with a model to relate dengue’s genealogical history, or phylodynamics, with the epidemiology of the disease is challenging because of its complexity: seasonal infection rates, changes in mosquito population sizes, different viral strains, urban versus rural populations densities, and the widespread movement of people—where viruses can usurp geographic boundaries—are all contributing factors. Now, Rasmussen et al. (2013) have looked at dengue virus serotype 1 (DENV-1) in southern Vietnam, the most dominant endemic strain of the virus, for which a large number of sequence samples (237) are available along with reliable data on dengue hospitalizations. They incorporated some of these additional ecological complexities to tweak different phylodynamic models and were able to reconstruct dengue’s past dynamics from genealogies that are consistent with the observed hospitalization data and also lead to new insights into the factors shaping viral family histories. Their best-fit models accounted for population variation in urban versus rural areas or the population dynamics of mosquitos, matching the hospital-reported cases. This gave new insights for the researchers to create new and improved models that are more reliable and accurate for the complex dynamics of infectious disease.

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