ArticleMolecular biology and evolution2014
Bayesian inference of infectious disease transmission from whole-genome sequence data.
Article in Molecular biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 2 of them syntheses that pooled it.
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Who cites it
125 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Are Food Animals Responsible for Transfer of Antimicrobial-Resistant Escherichia coli or Their Resistance Determinants to Human Populations? A Systematic Review.Foodborne pathogens and disease · 2018Pooled it
- Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review.BMC medicine · 2016Pooled it
- A statistical framework for comparing epidemic forests.PLoS computational biology · 2026Article
- Clinical utility and limitations of whole-genome sequencing via nanopore technology and Bayesian modeling for outbreak transmission inference.Antimicrobial resistance and infection control · 2026Article
- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- Apollo: a comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell.Nature communications · 2025Article
- ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.PLoS computational biology · 2025Article
- Incorporating Epidemiological Data into the Genomic Analysis of Partially Sampled Infectious Disease Outbreaks.Molecular biology and evolution · 2025Article
- Bayesian estimation of transmission networks for infectious diseases.Journal of mathematical biology · 2025Article
- Whole-genome sequencing-based source tracing and infection control ofFrontiers in microbiology · 2025Article
- Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell.bioRxiv : the preprint server for biology · 2024Article
- Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.Molecular biology and evolution · 2024Article
- Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights.Frontiers in public health · 2024Article
- A Bayesian inference method to estimate transmission trees with multiple introductions; applied to SARS-CoV-2 in Dutch mink farms.PLoS computational biology · 2023Article
- Associations between Nutrigenomic Effects and Incidences of Microbial Resistance against Novel Antibiotics.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Effect of compensatory evolution in the emergence and transmission of rifampicin-resistant Mycobacterium tuberculosis in Cape Town, South Africa: a genomic epidemiology study.The Lancet. Microbe · 2023Article
- ComparingEpidemiology and infection · 2023Article
- Low Viral Diversity Limits the Effectiveness of Sequence-Based Transmission Inference for SARS-CoV-2.mSphere · 2023Article
- Population genomic analysis of clinical ST15Frontiers in microbiology · 2023Article
- Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease.Bioinformatics (Oxford, England) · 2023Article
65 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Genomics is increasingly being used to investigate disease outbreaks, but an important question remains unanswered--how well do genomic data capture known transmission events, particularly for pathogens with long carriage periods or large within-host population sizes? Here we present a novel Bayesian approach to reconstruct densely sampled outbreaks from genomic data while considering within-host diversity. We infer a time-labeled phylogeny using Bayesian evolutionary analysis by sampling trees (BEAST), and then infer a transmission network via a Monte Carlo Markov chain. We find that under a realistic model of within-host evolution, reconstructions of simulated outbreaks contain substantial uncertainty even when genomic data reflect a high substitution rate. Reconstruction of a real-world tuberculosis outbreak displayed similar uncertainty, although the correct source case and several clusters of epidemiologically linked cases were identified. We conclude that genomics cannot wholly replace traditional epidemiology but that Bayesian reconstructions derived from sequence data may form a useful starting point for a genomic epidemiology investigation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.