Research Articles
HIV incidence and prevalence projections for Zimbabwe: Findings from five mathematical models
DOI:
10.2989/16085906.2025.2518936
Author(s):
Isaac Taramusi UNAIDS UCO Zimbabwe, Zimbabwe, John Stover , United States of America, Robert Glaubius , United States of America, Tsitsi Apollo Ministry of Health and Child Care, Zimbabwe, Getrude Ncube Ministry of Health and Child Care, Zimbabwe, Owen Mugurungi Ministry of Health and Child Care, Zimbabwe, Ngwarai Sithole Ministry of Health and Child Care, Zimbabwe, Loveleen Bansi-Matharu Institute for Global Health, University College London, United Kingdom, Jenny Smith Institute for Global Health, University College London, United Kingdom, Andrew Phillips Institute for Global Health, University College London, United Kingdom, Valentina Cambiano Institute for Global Health, University College London, United Kingdom, Daniel T Citron New York University Grossman School of Medicine, United States of America, Anna Bershteyn New York University Grossman School of Medicine, United States of America, Debra ten Brink Burnet Institute, Australia, Rowan Martin-Hughes Burnet Institute, Australia, Michael Pickles Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, United Kingdom, Paul Revill University of York, United Kingdom, Amon Mpofu National AIDS Council of Zimbabwe, Zimbabwe, Jeffrey Imai-Eaton Chan School of Public Health, Center for Communicable Diseases Dynamics, Harvard University, United States of America, Richard Makurumidze Faculty of Medicine and Health Sciences, University of Zimbabwe, Zimbabwe, Simbarashe Rusakaniko Faculty of Medicine and Health Sciences, University of Zimbabwe, Zimbabwe,
Abstract
Introduction: Understanding how HIV epidemics are likely to behave in the future is key to informing HIV response strategies in low-income countries. Up-to-date HIV epidemiological estimates are important for policy decision- making, but surveillance data can be out of date. This study compared forecasts from HIV epidemiological models.
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