ReviewMicrobiology spectrum2017
Quantifying Transmission.
Review in Microbiology spectrum, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Host heterogeneity and unpredictability in parasite outbreaks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Humans seropositive for Trypanosoma cruzi co-infected with intestinal helminths have higher infectiousness, parasitaemia and Th2-type response in the Argentine Chaco.Parasites & vectors · 2024Article
- Chronic High-Level Parasitemia in HIV-Infected Individuals With or Without Visceral Leishmaniasis in an Endemic Area in Northwest Ethiopia: Potential Superspreaders?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Evaluation of the Xpert Carba-R assay for quantifying carbapenemase-producing bacterial load in stool samples.PloS one · 2024Article
- Article
- Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review.Pathogens (Basel, Switzerland) · 2022Review
- Over-dispersed Trypanosoma cruzi parasite load in sylvatic and domestic mammals and humans from northeastern Argentina.Parasites & vectors · 2022Article
- Bacterial-viral interactions: a factor that facilitates transmission heterogeneities.FEMS microbes · 2022Article
- Management of Breast Cancer Patients During the Coronavirus Disease 2019 Pandemic: The Experience From the Epicenter of China, Wuhan.Clinical breast cancer · 2022Article
- Hand-to-Environment Contact During Indoor Activities in Senior Welfare Centers Among Korean Older Adults: A Pilot Study.Healthcare (Basel, Switzerland) · 2021Article
- Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model.mBio · 2019Article
- QuantifyingCommunications biology · 2019Article
- The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transmissibility is the defining characteristic of infectious diseases. Quantifying transmission matters for understanding infectious disease epidemiology and designing evidence-based disease control programs. Tracing individual transmission events can be achieved by epidemiological investigation coupled with pathogen typing or genome sequencing. Individual infectiousness can be estimated by measuring pathogen loads, but few studies have directly estimated the ability of infected hosts to transmit to uninfected hosts. Individuals' opportunities to transmit infection are dependent on behavioral and other risk factors relevant given the transmission route of the pathogen concerned. Transmission at the population level can be quantified through knowledge of risk factors in the population or phylogeographic analysis of pathogen sequence data. Mathematical model-based approaches require estimation of the per capita transmission rate and basic reproduction number, obtained by fitting models to case data and/or analysis of pathogen sequence data. Heterogeneities in infectiousness, contact behavior, and susceptibility can have substantial effects on the epidemiology of an infectious disease, so estimates of only mean values may be insufficient. For some pathogens, super-shedders (infected individuals who are highly infectious) and super-spreaders (individuals with more opportunities to transmit infection) may be important. Future work on quantifying transmission should involve integrated analyses of multiple data sources.
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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.