HIV treatment as prevention: Principles of good HIV epidemiology modelling for public health decision-making in all modes of prevention and evaluation
South African Department of Science and Technology, National Research Foundation Centre for Excellence in Epidemiological Modelling and Analysis, University of Stellenbosch, Stellenbosch, South Africa; Kirby Institute, University of New South Wales, Sydney, NSW, United States; Infectious Disease Epidemiology Group, Weill Cornell Medical College-Qatar, Cornell University, Qatar Foundation-Education City, Doha, Qatar; Department of Public Health, Weill Cornell Medical College, Cornell University, New York, NY, United States; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States; Global HIV/AIDS Program, The World Bank, Washington, DC, United States; Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
Public health responses to HIV epidemics have long relied on epidemiological modelling analyses to help prospectively project and retrospectively estimate the impact, cost-effectiveness, affordability, and investment returns of interventions, and to help plan the design of evaluations. But translating model output into policy decisions and implementation on the ground is challenged by the differences in background and expectations of modellers and decision-makers. As part of the PLoS Medicine Collection "Investigating the Impact of Treatment on New HIV Infections"-which focuses on the contribution of modelling to current issues in HIV prevention-we present here principles of "best practice" for the construction, reporting, and interpretation of HIV epidemiological models for public health decision-making on all aspects of HIV. Aimed at both those who conduct modelling research and those who use modelling results, we hope that the principles described here will become a shared resource that facilitates constructive discussions about the policy implications that emerge from HIV epidemiology modelling results, and that promotes joint understanding between modellers and decision-makers about when modelling is useful as a tool in quantifying HIV epidemiological outcomes and improving prevention programming. © 2012 Delva et al.
anti human immunodeficiency virus agent; article; cost effectiveness analysis; decision making; epidemic; health economics; highly active antiretroviral therapy; human; Human immunodeficiency virus; Human immunodeficiency virus infection; incidence; infection risk; mathematical model; risk factor; virus transmission; biological model; decision making; evaluation; Human immunodeficiency virus; physiology; public health; uncertainty; virology; Decision Making; Evaluation Studies as Topic; HIV; HIV Infections; Humans; Models, Biological; Public Health; Uncertainty