English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Ending the HIV/AIDS pandemic is among the Sustainable Development Goals forthe next decade. In order to overcome the gap between the need for care and theavailable resources, better understanding of HIV epidemics is needed to guidepolicy decisions, especially for key populations that are at higher risk forHIV infection. Accurate HIV epidemic estimates for key populations have beendifficult to obtain because their HIV surveillance data is very limited. Inthis paper, we propose a so-called joint spatial conditional auto-regressivemodel for estimating HIV prevalence rates among key populations. Our modelborrows information from both neighboring locations and dependent populations.As illustrated in the real data analysis, it provides more accurate estimatesthan independently fitting the sub-epidemic for each key population. Inaddition, we provide a study to reveal the conditions that our proposal gives abetter prediction. The study combines both theoretical investigation andnumerical study, revealing strength and limitations of our proposal.

A Joint Spatial Conditional Auto-Regressive Model for Estimating HIV Prevalence Rates Among Key Populations