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. 2019 May 15;570(7760):189–193. doi: 10.1038/s41586-019-1200-9

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 5 — The process used to produce HIV prevalence estimates among adults in sub-Saharan Africa involved three main parts. In the data-processing steps (green), data were identified, extracted and prepared for use in the HIV prevalence model and in covariate models. In the modelling phase (orange), we used these data and covariates in a stacked generalization ensemble model and spatiotemporal Gaussian process model. In the post-processing phase (blue), we calibrated the prevalence estimation to match GBD 2017 estimates at the national level, aggregated prevalence estimates to the first- and second-level administrative subdivisions in each country and calculated the number of people living with HIV.