A Bayesian approach to Geo spatial analysis of HIV viral load data

Abstract

HIV is currently ranked among the leading causes of death in Kenya and in the world, with an estimated 1.5 Million Kenyans living with HIV and 28,000 deaths recorded annually as a result of AIDS related illnesses. In 2014, UNAIDS launched a 90-90-90 strategy the aim was to diagnose 90 per cent of all HIV- positive persons, provide antiretroviral therapy (ART) for 90 percent of those diagnosed, and achieve viral suppression for 90 per cent of those treated by 2020. This study is motivated by the need to assess the 3rd 90; viral suppression for 90 per cent of those ART treated and seeks to analyze one statistical paradigm (Bayesian) that have conventionally been used for geospatial trends. Use of Bayesian approach has been used previously to assess the prevalence and incidence of diseases however, this dissertation seeks to evaluate Bayesian Approach to spatial trends of HIV Viral Load Suppression in Kenya. We revisit the theoretical framework of the Bayesian Approach and apply real data from the Kenyan setting spanning from 2012 to 2017. Results show Bayesian Approach to be robust, in depth and entails more information when modelling spatio-trends of Viral Load suppression. Further, First Line ART regimen, HIV-TB co-infection and retention rates are significant predictors of Viral Load suppression spread.