South Africa District HIV Estimates combine district-level data about HIV from several sources in a statistical model:

District HIV estimates are developed using a customised version of the Naomi model, a small-area estimation model for estimating HIV prevalence and PLHIV, ART coverage, and new HIV infections at district level by sex and five-year age group. The model combines district-level data about multiple outcomes from several sources in a Bayesian statistical model to produce consistent estimates of multiple outcomes of interest.

Download Naomi estimates calibrated to Thembisa 4.8 outputs (by sex and 5-year age group)

For raw (uncalibrated) Naomi estimates or previous versions, see the tab Previous versions.

About

South Africa District Estimates are developed through a technical collaboration of:

in partnership with the South Africa Department of Health, South Africa National AIDS Council, UNAIDS, US Centers for Disease Control (CDC), and the Human Sciences Research Council (HSRC).

Estimates are reviewed by the South Africa Estimates and Modelling Technical Working Group, chaired by the National Department of Health and South Africa National AIDS Council.

For further information, questions, or feedback about South Africa District HIV Estimates, please contact:

Download full results

Full estimates in spreadsheet format:

Naomi estimates calibrated to Thembisa 4.8 outputs (by sex and 5-year age group)
Raw Naomi estimates (Not adjusted to Thembisa 4.8 outputs)

For planning purposes the version of estimates calibrated to Thembisa 4.8 outputs by sex and 5-year age group are recommended.

Within the zipped folder, the spreadsheet indicators.csv contains estimates of all indicators stratified by:

  • Area: national, province, district
  • Sex: both, female, male
  • Age group: 5-year age groups, all ages, 0-14, 15-49, 15-64, 15+, 50+, 15-24, 25-34, 35-49, 50-64, 64+, age 0, 1-4
  • Time: June 2017, September 2022, December 2024, and projections to September 2025, September 2026, and September 2027

For each estimate, the mean estimate and 95% uncertainty range are reported.

The supplementary spreadsheet indicators_art-sector.csv in the zipped folder contains custom output indicators for South Africa application of the Naomi model reporting the number and distribution of ART clients by sector where accessing treatment (public, private through medical aid schemes, cash paying, and from foreign sources), and custom PLHIV denominators for those accessing ART in the public sector.

The file boundaries.geojson contains geographic boundaries, which can be used producing maps of estimates.

Relationship to Thembisa estimates

Thembisa is a mathematical model of the South African HIV epidemic at national and province level. The Thembisa model represents the behaviours and processes that determine HIV transmission, disease progression, and effects of interventions. Thembisa reconstructs the full trajectory of the HIV epidemic in South Africa with medium range projections (1985-2040).

The Naomi model is a statistical model that balances evidence from multiple data sources at district level, but does not represent mechanisms of HIV transmission. Naomi is focused on current estimates (2024-25) for district-level planning purposes.

For some policy and planning purposes, it is useful for central values for district, provincial, and national estimates to exactly align. After fitting the model, a second version of district estimates are produced that are adjusted such that aggregated district estimates match to Thembisa provincial estimates, interpolated to the relevant quarter for district estimates. Before adjusting district estimates, Thembisa provincial estimates are first adjusted to match Thembisa national estimates.

The table below summarises key elements of Thembisa and Naomi
Thembisa Naomi
Geographical level National, provincial District
Demographic inputs CARe model District estimates by Statistics SA, adjusted to Thembisa
Age stratification Single year (0, 1, …, 89, 90+) Five-year age groups 0 to 80+
Migration Migration between provinces contributes to changes in HIV prevalence Migration is not explicitly modelled
HIV incidence Population is divided into risk groups, key populations. Assumptions about sexual risk behaviour and HIV transmission drive incidence. By sex, age, and district (10+ population only). Depends on transmission rate, district prevalence, and district ART coverage
HIV disease progression Acute HIV + 4 CD4 stages Not explicitly modelled
Diagnosis, testing Testing rates depend on age, sex, CD4 stage, year Not included
ART uptake Depends on CD4 stage, sex, age, year Depends on district, sex, age, year
Adjustment for cross-border ART Accounts for clients accessing ART from foreign programmes (Thembisa 4.8) Persons have a small probability of receiving ART at facilities in a neighbouring district to their residence
Mortality Depends on age, sex, current CD4 (if untreated), time since ART start and baseline CD4 (if treated) HIV survival based on survival probabilities from Thembisa by sex, age, province (not a model output)
Calibration to HIV prevalence data HSRC & DHS surveys; Antenatal prevalence surveys; Key population surveys SABSSM VI (HSRC 2022), SABSSM VI (HSRC 2017) & SA DHS 2016 surveys; Routine antenatal HIV testing prevalence
Inclusion of programme data HIV testing data, ART totals, viral suppression, VMMCs ART totals
Most recent programme data March 2024 December 2024
Other calibration data Recorded deaths (national); Self-reported testing history ART coverage among women attending first ANC

Methods: Naomi model

South Africa District HIV Estimates are developed using the Naomi model. The Naomi model is a small-area estimation model for estimating HIV prevalence and PLHIV, ART coverage, and new HIV infections at district level by sex and five-year age group. The model combines district-level data about multiple outcomes from several sources in a Bayesian statistical model to produce consistent estimates of multiple outcomes of interest.

The model focuses on current estimates and short-term one-year ahead projections for HIV programme planning purposes.

Population

District-level population estimates by sex and age group are drawn from the Statistics South Africa Mid-Year Population Estimates1, and adjusted to match the Thembisa 4.8 province-level population by sex and age group. Stats SA MYPE 2024 consider Census 2022 results and other data sources to construct district population estimates and projections. Therefore they deviate from the primary census results in some districts for total population and population composition.

HIV prevalence

The first step is to produce cross-sectional estimates for HIV prevalence, ART coverage, and HIV incidence in June 2017 and September 2022, the mid-points of the most recent nationally-representative household surveys.

For HIV prevalence, the model is calibrated to survey data about HIV prevalence by district, sex, and five-year age group from the Sixth South African National HIV Prevalence, Incidence, and Behaviour survey (SABSSM VI), the National HIV Prevalence, Incidence, Behaviour and Communication Survey (SABSSM) 20172 and Demographic and Health Survey (DHS) 20163. Since the survey sample size in each district is relatively small, routinely reported data about HIV prevalence among pregnant women attending their first ANC visit, extracted from the national District Health Management Information System (DHIS), are used to improve estimates of the spatial pattern of HIV.

ART coverage

ART coverage by district, age, and sex is estimated from household survey data about the presence of ARV biomarkers in HIV-positive survey respondents. Routinely reported ART coverage among pregnant women prior to first ANC visit is used as a covariate for the spatial pattern of ART coverage. The ART coverage and HIV prevalence are also calibrated so that total number on ART matches the number of adults and children accessing treatment in each district through public sector and private sector treatment provision, reported through the national DHIS and Council for Medical Schemes, respectively.

ART attendance in neighbouring districts

A challenge for estimating treatment coverage at district level is that persons may access ART services in a different district than their residence, for example if facilities are closer or perceived to provide better services. The model allows for a probability that resident PLHIV access ART in a neighbouring district. The prior assumption is that the large majority of PLHIV will access ART in their district of residence, but this probability can vary based on district data about the number receiving ART compared to HIV prevalence, ART coverage and population.

From 2025, a custom version of the Naomi model for South Africa also incorporates a proportion of residents on ART in each district who access ART from foreign sources outside South Africa (e.g. immigrants receiving ART from public sector programmes in their home country). Estimates for the proportion of ART recipients from foreign sources are constructed to be consistent with Thembisa 4.8 and at district level proportional to the proportion of international migrants from Census 2022.

HIV incidence

HIV incidence in 2017 is estimated from the proportion recently infected from the SABSSM 2017 survey. HIV incidence is determined by the district-level HIV prevalence and ART coverage such that the estimated HIV incidence is related to the prevalence of unsuppressed HIV in the population. The sex and age distribution of new infections is estimated using incidence rate ratios from Thembisa.

Projection to December 2024 and September 2025, September 2026, and September 2027

The next step of the model is to conduct a one-step projection of the population to December 2024. Population estimates are updated with Statistics South Africa district projections adjusted to Thembisa population projections. The number of PLHIV is projected from 2022 to 2024 based on survival estimates by province, sex, and age group from Thembisa over the same period (which accounts for HIV disease progression and effects of ART scale up on reducing AIDS mortality). ART coverage is updated based on the number on ART in December 2024 from DHIS and medical scheme reporting. Finally, for programme planning purposes, a short term projection is calculated to September 2025, September 2026, and September 2027 using the same methods. The number on ART is projected based on Thembisa model projection for rates of further scale up.

Paediatric estimates

For paediatric estimates, where household survey data have low numbers of observed cases, the model uses the ratio of female adult HIV prevalence to child HIV prevalence from Thembisa provincial results to estimate the average district-level prevalence among children and the new paediatric infections between 2017 and 2022. Paediatric ART coverage is determined by the number of children receiving ART in each district and SABBSM survey data about ART coverage among children.

Versions

A preliminary version of the Naomi model was first developed in South Africa in 2019 and district estimate were publicly released at www.hivdata.org.za in November 2020.

The model has been used in UNAIDS-supported national HIV estimates in 40 other sub-Saharan African countries. Future development of Naomi will extend the model for additional data sources and indicators including the HIV care cascade and prevention indicators.

Prevalence & PLHIV

Prevalence is the estimated number of people (aged 15-49 years) living with HIV in the district divided by the total resident population aged 15-49 years.