ReviewPloS one2013
Individual-based simulation models of HIV transmission: reporting quality and recommendations.
Review in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Calibration of individual-based models to epidemiological data: A systematic review.PLoS computational biology · 2020Pooled it
- Guidelines on reporting and assessing dynamic mathematical models of infectious diseases: a scoping review.BMC infectious diseases · 2025Article
- HIV response financing challenges in Sub-Saharan Africa: barriers to achieving the 95-95-95 UNAIDS targets.Frontiers in public health · 2025Review
- A hybrid simulation model of HIV program interventions: from transmission behavior to macroeconomic impacts.Therapeutic advances in drug safety · 2025Article
- Article
- PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial.PLoS computational biology · 2021Article
- The opioid crisis: need for systems science research.Health research policy and systems · 2020Article
- Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium.PLoS neglected tropical diseases · 2020Review
- The Challenges of Parameterizing Direct Effects in Individual-Level Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2020Article
- Modelling the epidemiologic impact of achieving UNAIDS fast-track 90-90-90 and 95-95-95 targets in South Africa.Epidemiology and infection · 2019Article
- Using Observational Data to Calibrate Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference.American journal of epidemiology · 2017Article
- Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial.Clinical trials (London, England) · 2017Article
- Management and modeling approaches for controlling raccoon rabies: The road to elimination.PLoS neglected tropical diseases · 2017Review
- Can the Heterosexual HIV Epidemic be Eliminated in South Africa Using Combination Prevention? A Modeling Analysis.American journal of epidemiology · 2016Article
- Understanding the effects of different HIV transmission models in individual-based microsimulation of HIV epidemic dynamics in people who inject drugs.Epidemiology and infection · 2016Article
- Article
- Computational Studies of the Intestinal Host-Microbiota Interactome.Computation (Basel, Switzerland) · 2015Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundIndividual-based modeling is a growing technique in the HIV transmission and prevention literature, but insufficient attention has been paid to formally evaluate the quality of reporting in this field. We present reporting recommendations for individual-based models for HIV treatment and prevention, assess the quality of reporting in the existing literature, and comment on the contribution of this model type to HIV policy and prediction.
methodsWe developed reporting recommendations for individual-based HIV transmission mathematical models, and through a systematic search, used them to evaluate the reporting in the existing literature. We identified papers that employed individual-based simulation models and were published in English prior to December 31, 2012. Articles were included if the models they employed simulated and tracked individuals, simulated HIV transmission between individuals in a particular population, and considered a particular treatment or prevention intervention. The papers were assessed with the reporting recommendations.
findingsOf 214 full text articles examined, 32 were included in the evaluation, representing 20 independent individual-based HIV treatment and prevention mathematical models. Manuscripts universally reported the objectives, context, and modeling conclusions in the context of the modeling assumptions and the model's predictive capabilities, but the reporting of individual-based modeling methods, parameterization and calibration was variable. Six papers discussed the time step used and one discussed efforts to maintain internal validity in coding.
conclusionIndividual-based models represent detailed HIV transmission processes with the potential to contribute to inference and policy making for many different regions and populations. The rigor in reporting of assumptions, methods, and calibration of individual-based models focused on HIV transmission and prevention varies greatly. Higher standards for reporting of statistically rigorous calibration and model assumption testing need to be implemented to increase confidence in existing and future modeling results.
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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.