ArticleScientific reports2015
Strong influence of behavioral dynamics on the ability of testing and treating HIV to stop transmission.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Transitions in sexual behaviour among gay, bisexual, and other men who have sex with men in England: Data from a prospective study.PloS one · 2025Article
- Modelling the impact of tailored behavioural interventions on chlamydia transmission.Scientific reports · 2021Article
- Contribution of high risk groups' unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis.Infectious Disease Modelling · 2020Article
- Elimination prospects of the Dutch HIV epidemic among men who have sex with men in the era of preexposure prophylaxis.AIDS (London, England) · 2018Article
- A novel Bayesian approach to predicting reductions in HIV incidence following increased testing interventions among gay, bisexual and other men who have sex with men in Vancouver, Canada.Journal of the Royal Society, Interface · 2018Article
- Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention.PloS one · 2018Article
- Modelling the dynamics of population viral load measures under HIV treatment as prevention.Infectious Disease Modelling · 2018Article
- Risk stratification in compartmental epidemic models: Where to draw the line?Journal of theoretical biology · 2017Article
- A network intervention that locates and intervenes with recently HIV-infected persons: The Transmission Reduction Intervention Project (TRIP).Scientific reports · 2016Article
- Impact of Heterogeneity in Sexual Behavior on Effectiveness in Reducing HIV Transmission with Test-and-Treat Strategy.PLoS computational biology · 2016Article
- Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes.BMC infectious diseases · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Choosing between strategies to control HIV transmission with antivirals requires understanding both the dynamics affecting those strategies' effectiveness and what causes those dynamics. Alternating episodes of high and low contact rates (episodic risk) interact with increased transmission probabilities during early infection to strongly influence HIV transmission dynamics. To elucidate the mechanics of this interaction and how these alter the effectiveness of universal test and treat (UT8T) strategies, we formulated a model of UT8T effects. Analysis of this model shows how and why changing the dynamics of episodic risk changes the fraction of early transmissions (FET) and the basic reproduction number (R0) and consequently causes UT8T to vary from easily eliminating transmission to having little effect. As the length of risk episodes varies from days to lifetimes, FET first increases, then falls. Endemic prevalence varies similarly. R0, in contrast, increases monotonically and is the major determinant of UT8T effects. At some levels of episodic risk, FET can be high, but eradication is easy because R0 is low. At others FET is lower, but a high R0 makes eradication impossible and control ineffective. Thus changes in individual risk over time must be measured and analyzed to plan effective control strategies with antivirals.
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.