Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications

被引:13
|
作者
Featherstone, Leo A. [1 ]
Zhang, Joshua M. [1 ]
Vaughan, Timothy G. [2 ,3 ]
Duchene, Sebastian [1 ]
机构
[1] Univ Melbourne, Peter Doherty Inst Infect & Immun, Melbourne, Vic 3000, Australia
[2] Swiss Fed Inst Technol, Dept Biosyst Sci & Engn, CH-4058 Basel, Switzerland
[3] Swiss Inst Bioinformat, CH-1015 Geneva, Switzerland
基金
澳大利亚研究理事会; 英国医学研究理事会;
关键词
epidemiological models; phylodynamics; birth-death model; coalescent model; BAYESIAN COALESCENT INFERENCE; PAST POPULATION-DYNAMICS; HEPATITIS-C VIRUS; BIRTH; SKYLINE; TIME; TRANSMISSION; PHYLOGENIES; SPECIATION; FRAMEWORK;
D O I
10.1093/ve/veac045
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.
引用
收藏
页数:12
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