Multivariate spatial modelling for predicting missing HIV prevalence rates among key populations.

Ending the HIV/AIDS pandemic is among the sustainable development goals for the next decade. To overcome the problem caused by the imbalances between the need for care and the limited resources, we shall improve our understanding of the local HIV epidemics, especially for key populations at high risk of HIV infection. However, HIV prevalence rates for key populations have been difficult to estimate because their HIV surveillance data are very scarce. This paper develops a multivariate spatial model for predicting unknown HIV prevalence rates among key populations. The proposed multivariate conditional auto-regressive model efficiently pools information from neighbouring locations and correlated populations. As the real data analysis illustrates, it provides more accurate predictions than independently fitting the sub-epidemic for each key population. Furthermore, we investigate how different pieces of surveillance data contribute to the prediction and offer practical suggestions for epidemic data collection. [ABSTRACT FROM AUTHOR]