A BAYESIAN HIERARCHICAL MODEL FOR COMBINING MULTIPLE DATA SOURCES IN POPULATION SIZE ESTIMATION.

IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Annals of Applied Statistics Pub Date : 2022-09-01 Epub Date: 2022-07-19 DOI:10.1214/21-AOAS1556
Jacob Parsons, Xiaoyue Niu, Le Bao
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Abstract

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.

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在种群数量估计中结合多种数据源的贝叶斯分层模型。
为有效防治艾滋病毒/艾滋病,对某些关键人群采取有针对性的干预措施至关重要。这些关键人群包括性工作者、注射毒品者和男男性行为者。虽然准确估计这些关键人群的规模非常重要,但任何试图直接接触或统计这些人群成员的尝试都很困难。因此,我们采用间接方法来估算人口规模。人们提出了多种估算此类人口规模的方法,但结果往往相互矛盾。因此,有必要制定一种原则性的方法来合并和协调这些估计值。为此,我们提出了一个贝叶斯分层模型,用于估算重点人群的规模,该模型综合了来自不同信息来源的多种估算结果。所提出的模型利用了多年的数据,并对所用数据源的系统误差进行了明确建模。我们使用该模型估算了乌克兰注射吸毒者的规模。我们对模型的适当性进行了评估,并比较了每个数据源对最终估算结果的贡献。
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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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