In most countries in the world outside of sub-Saharan Africa, HIV is largely concentrated in sub-populations whose behavior puts them at higher risk of contracting and transmitting HIV, such as people who inject drugs, sex workers and men who have sex with men. Estimating the size of these sub-populations is important for assessing overall HIV prevalence and designing effective interventions. We present a Bayesian hierarchical model for estimating the sizes of local and national HIV key affected populations. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion. The proposed model is used to estimate the numbers of people who inject drugs in Bangladesh.

中文翻译：

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使用贝叶斯分层模型估计来自多个数据源的 HIV 感染风险人群的规模

在撒哈拉以南非洲以外的世界上大多数国家，艾滋病毒主要集中在其行为使他们感染和传播艾滋病毒的风险更高的亚群中，例如注射毒品的人、性工作者和男男性行为者. 估计这些亚群的规模对于评估总体 HIV 流行率和设计有效的干预措施很重要。我们提出了一个贝叶斯分层模型，用于估计当地和国家 HIV 重点受影响人群的规模。该模型包含多个常用数据源，包括映射数据、调查、干预、捕获-重新捕获数据、来自组织的估计或猜测以及专家意见。提议的模型用于估计孟加拉国注射毒品的人数。