ArticleMolecular biology and evolution2017
Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks.
Article in Molecular biology and evolution, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 185 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
185 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methicillin-ResistantBioMed research international · 2024Pooled it
- Spatially targeted screening to reduce tuberculosis transmission in high-incidence settings.The Lancet. Infectious diseases · 2019Pooled it
- Article
- cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.NAR genomics and bioinformatics · 2026Article
- Genomic surveillance reveals long-term endemicity and outbreak potential ofMicrobial genomics · 2026Article
- Novel transposon TnMicrobial genomics · 2026Article
- Horizontal Gene Transfer inBiology · 2026Review
- SARS-CoV-2 saltational events are recurrent and trace to persistent human infections.bioRxiv : the preprint server for biology · 2026Article
- Network analysis of pairwise relative tuberculosis transmission probabilities in Lima, Peru.American journal of epidemiology · 2026Article
- A statistical framework for comparing epidemic forests.PLoS computational biology · 2026Article
- Genomic epidemiology of the 2025 mpox epidemic in Sierra Leone.Nature medicine · 2026Article
- Probing Genomic Diversity ofFoods (Basel, Switzerland) · 2026Article
- DiagnoDating: diagnostics for dated phylogenies in microbial population genetics.Molecular biology and evolution · 2026Article
- Novel transposon TnmedRxiv : the preprint server for health sciences · 2026Article
- Unwelcome neighbours: Tracking the transmission of Streptococcus equi in the United Kingdom horse population.Equine veterinary journal · 2026Article
- Rapid dissemination of Staphylococcus aureus in the neonatal intensive care unit is associated with invasive infection.Nature communications · 2026Article
- Unpacking the spread of pathogens.Nature reviews. Microbiology · 2026Article
- Global Comparative Genomics ofLife (Basel, Switzerland) · 2026Article
- Beyond presence and frequency: a determinant-level framework for interpreting AMR dissemination in One Health surveillance.Frontiers in bioinformatics · 2026Article
- Transmission of bedaquiline-resistant Mycobacterium tuberculosis in KwaZulu-Natal, South Africa.American journal of respiratory and critical care medicine · 2026Article
125 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom-a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.