# **2.1.3. Modeled incidence**

HIV epidemic dynamics, including incidence, can be inferred through mathematical modeling, taking into account multiple factors associated with the epidemic (demographics, risk factors, prevalence, probability of transmission, ART coverage, etc.). Several modeling approaches that estimate new HIV infections and incidence exist from individual research groups and organizations, including the Modes of Transmission <u>(http://www.who.int/bulletin/volumes/90/11/12-102574/en/index.html) and Actuarial</u> Society of South Africa models (http://aids.actuarialsociety.org.za/404.asp?pageid=3145). UNAIDS has been using Spectrum modeling software for over a decade to estimate national HIV epidemic trends, including recent reporting of incidence trends. Using a set of input parameters and model equations, the Estimation and Projection Package (EPP) with Spectrum estimates HIV incidence by fitting prevalence trends in ANC data (Brown et al. 2010; Bao et al. 2012), incorporating inputs such as ART coverage and mortality rates. These projections are then calibrated to national population-based survey estimates of HIV prevalence to generate national HIV prevalence and incidence estimates, and then stratified by age and sex based on demographic data. The most updated version of Spectrum (as of January 2014) now included DHS/AIS in the fitting process and has eliminated the second calibration step.

It is important to keep in mind that EPP/Spectrum generates estimates for both national HIV prevalence and incidence, which may or may not be in agreement with empirical measurements, including nationally reported data. Preliminary work from Eaton et al. suggested that when comparing EPP prevalence trends to that from DHS/HH survey data, prevalence declines may have been overestimated by the modelling process in southern African countries (Jeff Eaton, personal communication).

EPP and Spectrum are updated frequently, adding in new features to improve user interface and refine the model. These updates to the model include better assumptions and the addition of recent data that should improve upon previous estimates and more accurately model HIV population dynamics. While these improvements to the software package are positive, one must be aware that updates to the model can change previous estimates and outputs generated by earlier versions of Spectrum. Comparing estimates from 2010 and 2013 UNAIDS Global HIV/AIDS Reports (the 2012 Spectrum version represents an update from its 2010 version), there is no consistent trend in how the different versions influence historical incidence trajectories between 2001 and 2009 ( **Figure 1** and **2** ). Out of a group of Sub-Saharan African countries, some countries (Burkina Faso, Central African Republic, Namibia, Rwanda, South Africa, Swaziland, Zimbabwe) show a greater decrease in incidence rate from 2001-2009 using the 2010 Spectrum estimates ( **Figure 1** ); other countries (Botswana, Congo, Ghana, Kenya, Lesotho, Malawi, Mozambique, Nigeria, Tanzania, Uganda, Zambia) show greater decreases using 2012 Spectrum incidence rate estimates ( **Figure 2** ). Figures 1 and 2 are only meant to provide an illustration of how much incidence estimates can change 16