ArticleInternational journal of epidemiology2020
Estimating the relative probability of direct transmission between infectious disease patients.
Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Network analysis of pairwise relative tuberculosis transmission probabilities in Lima, Peru.American journal of epidemiology · 2026Article
- A new method to estimate factors associated with tuberculosis transmission using whole genome sequencing and other additional metadata.Epidemiologic methods · 2026Article
- Network Analysis of Pairwise Relative Tuberculosis Transmission Probabilities in Lima, Peru.medRxiv : the preprint server for health sciences · 2025Article
- Transmissibility and control of tuberculosis in school outbreaks: a modeling study based on four outbreaks in China.BMC infectious diseases · 2024Article
- Estimation of local time-varying reproduction numbers in noisy surveillance data.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Estimation of the generation interval using pairwise relative transmission probabilities.Biostatistics (Oxford, England) · 2022Article
- Estimation of local time-varying reproduction numbers in noisy surveillance data.medRxiv : the preprint server for health sciences · 2022Article
- Observational
- Statistical Estimation of the Reproductive Number From Case Notification Data.American journal of epidemiology · 2021Review
- Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo.Current protocols · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundEstimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.
methodsUsing a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.
resultsIn simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.
conclusionsOur method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.
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.