Individual-Based Simulation Models of HIV Transmission: Reporting Quality and Recommendations

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.

A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference

“…See Web Appendix 4 and Web Table 3 for uncertainty intervals around the ABM estimates (18). Although the uncertainty intervals obtained from these sensitivity analyses are not interpretable as confidence intervals, they can provide some insight into the range of possible outcome distributions consistent with variations of the model (19).…”

Computational Studies of the Intestinal Host-Microbiota Interactome

“…Computational modeling of the immune system has historically concentrated either on specific host-pathogen interactions or on inflammation. Host-pathogen specific models include research on the diseases and pathogens with broad global health implications such as HIV [122][123][124][125], malaria [126][127][128], tuberculosis [129][130][131] and influenza [132,133]. These models primarily focus on the adaptive immune system and cover a wide range of topics related to the epidemiology of the diseases including the molecular biology interactions between host and pathogen pathways and molecules, disease progression and transmission both in the host and associated vectors (e.g., mosquito for malaria), evolutionary and selection forces on pathogen genetics, and drug discovery for vaccines and treatments.…”

Calibration of individual-based models to epidemiological data: A systematic review

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