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Document Type
Spatial Analysis Reports
Publication Topic(s)
Geographic Information
Language
English
Recommended Citation
Mayala, Benjamin. K., Samir Bhatt, and Peter Gething. 2020. Predicting HIV/AIDS at Subnational Levels using DHS Covariates related to HIV. DHS Spatial Analysis Reports No. 18. Rockville, Maryland, USA: ICF.
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Publication ID
SAR18

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Abstract:

Accelerating the scale-up of HIV prevention and treatment approaches will limit the epidemic to more manageable levels and enable countries to move toward the elimination phase. In this context, the World Health Organization produced new treatment guidelines and UNAIDS set the 90-90-90 targets in which by 2020, 90% of all people living with HIV will know their HIV status, 90% of all people with diagnosed HIV infection will receive sustained antiretroviral therapy, and 90% of all people receiving antiretroviral therapy will be virally suppressed. Most household surveys such as the Demographic Health Surveys and the AIDS Indicator Surveys are designed to provide reliable estimates of survey indicators primarily at the national level, as well as the first subnational administrative level. To better address the need for fine spatial and lower level (district) estimates, geospatial modelling techniques that can leverage existing survey data, spatial relationships between survey clusters, and relationships with geospatial covariates have become increasingly popular in mapping key development indicators at high spatial resolutions. In this report, we use a multitask Gaussian process (GP), in which we use aggregated spatial coordinates and fit multiple Gaussian processes to HIV prevalence and other indicators such as women who reported condom use at last high-risk sex with a non-cohabiting, non-marital partner or the number of partners in lifetime for women. However, instead of using these Gaussian processes independently, we also model the cross correlations between all indicators. This methodology allows for all indicators to inform each other and to increase the predictive power of the model. In addition to prediction, the joint multitask framework also allows for a unified treatment of uncertainty that provides robust uncertainty intervals. For the first time, the proposed approach uses indicators that are actually relevant to predicting HIV prevalence. We also utilize a Bayesian framework and therefore have a robust treatment of uncertainty. We foresee many applications of this approach to other diseases in the future.

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