Reconciling Phylodynamics with Epidemiology: The Case of Dengue Virus in Southern Vietnam
Author(s) -
David A. Rasmussen,
Maciej F. Boni,
Katia Koelle
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/mst203
Subject(s) - coalescent theory , viral phylodynamics , biology , evolutionary biology , population , dengue fever , demographic history , phylogeography , evolutionary dynamics , vector (molecular biology) , effective population size , ecology , phylogenetics , genetics , virology , demography , gene , genetic variation , recombinant dna , sociology
Coalescent methods are widely used to infer the demographic history of populations from gene genealogies. These approaches-often referred to as phylodynamic methods-have proven especially useful for reconstructing the dynamics of rapidly evolving viral pathogens. Yet, population dynamics inferred from viral genealogies often differ widely from those observed from other sources of epidemiological data, such as hospitalization records. We demonstrate how a modeling framework that allows for the direct fitting of mechanistic epidemiological models to genealogies can be used to test different hypotheses about what ecological factors cause phylodynamic inferences to differ from observed dynamics. We use this framework to test different hypotheses about why dengue serotype 1 (DENV-1) population dynamics in southern Vietnam inferred using existing phylodynamic methods differ from hospitalization data. Specifically, we consider how factors such as seasonality, vector dynamics, and spatial structure can affect inferences drawn from genealogies. The coalescent models we derive to take into account vector dynamics and spatial structure reveal that these ecological complexities can substantially affect coalescent rates among lineages. We show that incorporating these additional ecological complexities into coalescent models can also greatly improve estimates of historical population dynamics and lead to new insights into the factors shaping viral genealogies.
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