ReviewCurrent opinion in HIV and AIDS2010
Modelling sexual transmission of HIV: testing the assumptions, validating the predictions.
Review in Current opinion in HIV and AIDS, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 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
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Infectious Disease Modelling of HIV Prevention Interventions: A Systematic Review and Narrative Synthesis of Compartmental Models.PharmacoEconomics · 2023Pooled it
- How do HIV-negative individuals in sub-Saharan Africa change their sexual risk behaviour upon learning their serostatus? A systematic review.Sexually transmitted infections · 2016Pooled it
- Clinical uncertainties, health service challenges, and ethical complexities of HIV "test-and-treat": a systematic review.American journal of public health · 2013Pooled it
- Pooled it
- Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.Current HIV/AIDS reports · 2024Review
- The impact of condom use on the HIV epidemic.Gates open research · 2021Article
- Reproductive justice & preventable deaths: state funding, family planning, abortion, and infant mortality, US 1980-2010.SSM - population health · 2016Article
- Acute HIV infection transmission among people who inject drugs in a mature epidemic setting.AIDS (London, England) · 2016Article
- Critical considerations for adopting the HIV 'treat all' approach in Zimbabwe: is the nation poised?Public health action · 2016Article
- Conceptual framework and research methods for migration and HIV transmission dynamics.AIDS and behavior · 2014Review
- Reconceptualizing the HIV epidemiology and prevention needs of Female Sex Workers (FSW) in Swaziland.PloS one · 2014Article
- Assessing the cost effectiveness of pre-exposure prophylaxis for HIV prevention in the US.PharmacoEconomics · 2013Review
- Beyond the distinction between biomedical and social dimensions of HIV prevention through the lens of a social public health.American journal of public health · 2012Article
- Male circumcision for HIV prevention: current evidence and implementation in sub-Saharan Africa.Journal of the International AIDS Society · 2011Review
- Universal testing and treatment as an HIV prevention strategy: research questions and methods.Current HIV research · 2011Review
- Antiretroviral therapy: a key component of a comprehensive HIV prevention strategy.Current HIV/AIDS reports · 2011Review
- Interaction of mathematical modeling and social and behavioral HIV/AIDS research.Current opinion in HIV and AIDS · 2011Review
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
Abstract
purpose of reviewTo discuss the role of mathematical models of sexual transmission of HIV: the methods used and their impact. RECENT
findingsWe use mathematical modelling of 'universal test and treat' as a case study to illustrate wider issues relevant to all modelling of sexual HIV transmission. SUMMARY: Mathematical models are used extensively in HIV epidemiology to deduce the logical conclusions arising from one or more sets of assumptions. Simple models lead to broad qualitative understanding, whereas complex models can encode more realistic assumptions and, thus, be used for predictive or operational purposes. An overreliance on model analysis in which assumptions are untested and input parameters cannot be estimated should be avoided. Simple models providing bold assertions have provided compelling arguments in recent public health policy, but may not adequately reflect the uncertainty inherent in the analysis.
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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.