Research Articles

HIV incidence and prevalence projections for Zimbabwe: Findings from five mathematical models

Published in: African Journal of AIDS Research
Volume 24 , issue 1-2 , 2025 , pages: 44–52

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

Keywords: epidemic projections , HIV , incidence , model comparison , mathematical modelling , Zimbabwe

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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 Up-to-date HIV epidemiological estimates are important for policy decision- making, but surveillance data can be out of date.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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