论文标题
一种结合多个数据源的贝叶斯分层建模方法：大小估计的案例研究
A Bayesian hierarchical modeling approach to combining multiple data sources: A case study in size estimation
论文作者
论文摘要
为了有效地打击艾滋病毒/艾滋病大流行，某些关键人群中有针对性的干预措施起着至关重要的作用。这样的关键人群的例子包括性工作者，注入毒品的人以及与男人发生性关系的男人。尽管对这些关键人群的规模进行准确的估计很重要，但任何直接接触或计数这些人群成员的尝试都是困难的。结果，间接方法用于产生尺寸估计。已经提出了多种估计此类人群规模的方法，但通常会带来矛盾的结果。因此，有必要有一种合并和调和这些估计的原则方法。为此，我们提出了一个贝叶斯分层模型，用于估计关键种群的大小，结合了来自不同信息来源的多个估计值。提出的模型利用了多年的数据，并明确对所使用的数据源中的系统错误进行了建模。我们使用该模型来估计在乌克兰注入毒品的人的规模。我们评估模型的适当性，并比较每个数据源对最终估计的贡献。
To combat the HIV/AIDS pandemic effectively, targeted interventions among certain key populations play a critical role. Examples of such key populations include sex workers, people who inject drugs, and men who have sex with men. While having accurate estimates for the size of these key populations is important, any attempt to directly contact or count members of these populations is difficult. As a result, indirect methods are used to produce size estimates. Multiple approaches for estimating the size of such populations have been suggested but often give conflicting results. It is therefore necessary to have a principled way to combine and reconcile these estimates. To this end, we present a Bayesian hierarchical model for estimating the size of key populations that combines multiple estimates from different sources of information. The proposed model makes use of multiple years of data and explicitly models the systematic error in the data sources used. We use the model to estimate the size of people who inject drugs in Ukraine. We evaluate the appropriateness of the model and compare the contribution of each data source to the final estimates.
