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Estimation and Short-Term prediction of the course of the HIV epidemic using demographic and health survey Methodology-Like Data

  • 2015/06/19
Type of publication
  • Articles
Authors
  • Blaizot S
  • Riche B
  • Maman D
  • Mukui I
  • Kirubi B
  • Etard JF
  • Ecochard R
Themes
  • VIH
Abstract
BACKGROUND
Mathematical models have played important roles in the understanding of epidemics and in the study of the impacts of various behavioral or medical measures. However, modeling accurately the future spread of an epidemic requires context-specific parameters that are difficult to estimate because of lack of data. Our objective is to propose a methodology to estimate context-specific parameters using Demographic and Health Survey (DHS)-like data that can be used in mathematical modeling of short-term HIV spreading.
 
METHODS AND FINDINGS
The model splits the population according to sex, age, HIV status, and antiretroviral treatment status. To estimate context-specific parameters, we used individuals' histories included in DHS-like data and a statistical analysis that used decomposition of the Poisson likelihood. To predict the course of the HIV epidemic, sex- and age-specific differential equations were used. This approach was applied to recent data from Kenya. The approach allowed the estimation of several key epidemiological parameters. Women had a higher infection rate than men and the highest infection rate in the youngest age groups (15-24 and 25-34 years) whereas men had the highest infection rate in age group 25-34 years. The immunosuppression rates were similar between age groups. The treatment rate was the highest in age group 35-59 years in both sexes. The results showed that, within the 15-24 year age group, increasing male circumcision coverage and antiretroviral therapy coverage at CD4 ≤ 350/mm3 over the current 70% could have short-term impacts.
 
CONCLUSIONS
The study succeeded in estimating the model parameters using DHS-like data rather than literature data. The analysis provides a framework for using the same data for estimation and prediction, which can improve the validity of context-specific predictions and help designing HIV prevention campaigns.