Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data

• Published in: PLoS computational biology Vol. 10; no. 1; p. e1003457
• Main Authors: Jombart, Thibaut, Cori, Anne, Didelot, Xavier, Cauchemez, Simon, Fraser, Christophe, Ferguson, Neil
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Bayes Theorem; Bayesian statistical decision theory; Biology; Communicable Disease Control; Computer Simulation; Disease Outbreaks; Disease transmission; Entropy; Epidemics; Freeware; Gene expression; Genetic research; Genome, Viral; Genomes; Genomics; Humans; Infections; Mathematics; Medical research; Medicine; Medicine, Experimental; Mutation; Parameter estimation; Phylogenetics; Probability; Programming Languages; Severe Acute Respiratory Syndrome - epidemiology; Severe Acute Respiratory Syndrome - virology; Severe acute respiratory syndrome-related coronavirus - genetics; Singapore; Software; Statistical methods; Trees; Singapore; Epidemics; Genome, Viral; Singapore; Genomics; Humans; Probability; Entropy; Communicable Disease Control; SARS Virus; Severe Acute Respiratory Syndrome; Algorithms; Disease Outbreaks; Computer Simulation; Bayes Theorem; Programming Languages; Software; Mutation

Recent years have seen progress in the development of statistically rigorous frameworks to infer outbreak transmission trees ("who infected whom") from epidemiological and genetic data. Making use of pathogen genome sequences in such analyses remains a challenge, however, with a variety of...