HIV Prevalence Projections
The Georgia DPH HIV/AIDS Epidemiology section has developed an interactive tool that can be used to estimate HIV prevalence projections for different rates of HIV diagnosis, and transitions between populations with viral loads of <200, >200, or no viral load reported in a given year. This tool is an estimate based on current knowledge of rates of HIV transmission, and calculations based on surveillance data. Such estimates do not offer definitive accuracy in HIV prevalence predictions, but may be helpful in planning HIV prevention interventions.
This tool was featured during the 2016 CSTE Annual Conference as a poster, which can be found here.
Using your enhanced HIV/AIDS Reporting System (eHARS) data, you can enter your own local data into this tool to project future HIV prevalence under different conditions of HIV testing, retention in care, and viral suppression.
To use the tool, do the following:
- Download and install the free Wolfram CDF Player.
- Open this web page using the 32-bit Firefox or Internet Explorer web browsers on Windows 10/8/7 or with 32-bit Firefox or Safari on macOS.
- Click on the HIV Prevalence Predictions Users Guide to walk through the steps on how to use your eHARS data to create estimates for your jurisdiction.
- Enter different values to see how the tool works:
Do more testing: Double diagnosis rates by increasing δ from 0.23 to 0.46 and see what happens.
► The number of undiagnosed drops substantially but there is little change in future prevalence.
Find the out-of-care: Double the rates of ϵn (re-entry from Missing VL to NVS) and ϵs (re-entry from Missing VL to VS)
► Prevalence increases less sharply; many more people with VS means many more people using HIV services.
Improve retention in care: Halve the rates on (transition from NVS to Missing VL) and os. (transition from VS to Missing VL)
► Prevalence increases less sharply and more people in care, but the effect is less strong than finding those currently out of care and re-engaging them.
Note that your browser may block the interactive content below by default; if so, click here to reload this page using an unsecure connection.
In the interactive graphic below,
u= undiagnosed population
n= population diagnosed and in care but not virally suppressed
s= population diagnosed, in care, and virally suppressed
m= population diagnosed but not in care, for which viral load information is missing
δ= per capita rate of new diagnosis
τ∗= per capita rate of transmission from population *
σ= per capita rate at which viral suppression is achieved while under care
ρ= per capita rate of recidivism while under care
o∗= per capita rate of transition out of care from population *
ϵ∗= per capita rate of (re-) entry into care and into population *
μ∗= per capita rate of mortality from population *
Page last updated 03/07/17