A new infectious disease model for estimating and projecting HIV/AIDS epidemics

As the global HIV pandemic enters its fourth decade, countries have collected longer time series of surveillance data, and the AIDS-specific mortality has been substantially reduced by the increasing availability of antiretroviral treatment. A refined model with a greater flexibility to fit longer time series of surveillance data is desired.

In this article, we present a new epidemiological model that allows the HIV infection rate, r(t), to change over years. The annual change of infection rate is modelled by a linear combination of three key factors: the past prevalence, the past infection rate and a stabilisation condition. We focus on fitting the antenatal clinic (ANC) data and household surveys which are the most commonly available data source for generalised epidemics defined by the overall prevalence being above 1%. A hierarchical model is used to account for the repeated measurement within a clinic. A Bayesian approach is used for the parameter estimation.

We evaluate the performance of the newly proposed model on the ANC data collected from urban and rural areas of 31 countries with generalised epidemics in sub-Sahara Africa. The three factors in the proposed model all have significant contributions to the reconstruction of r(t) trends. It improves the prevalence fit over the classic Estimation and Projection Package model and provides more realistic projections when the classic model encounters problems.

The proposed model better captures the main pattern of the HIV/AIDS dynamic. It also retains the simplicity of the classic model with a few interpretable parameters that are easy to interpret and estimate.