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关于艾滋病毒/艾滋病背景下人口预测的队列成分模型的更多内容:莱斯利矩阵表示法及新估计值。

More on the cohort-component model of population projection in the context of HIV/AIDS: A Leslie matrix representation and new estimates.

作者信息

Thomas Jason R, Clark Samuel J

机构信息

Center for Demography of Health and Aging, University of Wisconsin-Madison.

出版信息

Demogr Res. 2011 Jul 5;25:39-102. doi: 10.4054/DemRes.2011.25.2.

Abstract

This article presents an extension of the cohort-component model of population projection (CCMPP) first formulated by Heuveline (2003) that is capable of modeling a population affected by HIV. Heuveline proposes a maximum likelihood approach to estimate the age profile of HIV incidence that produced the HIV epidemics in East Africa during the 1990s. We extend this work by developing the Leslie matrix representation of the CCMPP, which greatly facilitates the implementation of the model for parameter estimation and projection. The Leslie matrix also contains information about the stable tendencies of the corresponding population, such as the stable age distribution and time to stability. Another contribution of this work is that we update the sources of data used to estimate the parameters, and use these data to estimate a modified version of the CCMPP that includes (estimated) parameters governing the survival experience of the infected population. A further application of the model to a small population with high HIV prevalence in rural South Africa is presented as an additional demonstration. This work lays the foundation for development of more robust and flexible Bayesian estimation methods that will greatly enhance the utility of this and similar models.

摘要

本文介绍了由休弗林(2003年)首次提出的人口预测队列成分模型(CCMPP)的扩展模型,该模型能够对受艾滋病毒影响的人群进行建模。休弗林提出了一种最大似然方法来估计艾滋病毒发病率的年龄分布,正是这种分布导致了20世纪90年代东非的艾滋病毒流行。我们通过开发CCMPP的莱斯利矩阵表示法来扩展这项工作,这极大地促进了该模型在参数估计和预测方面的实施。莱斯利矩阵还包含有关相应人群稳定趋势的信息,如稳定年龄分布和达到稳定的时间。这项工作的另一个贡献是,我们更新了用于估计参数的数据来源,并使用这些数据来估计CCMPP的一个修改版本,该版本包括(估计的)控制感染人群生存经验的参数。作为进一步的示范,还介绍了该模型在南非农村一个艾滋病毒高流行率的小群体中的应用。这项工作为开发更强大、更灵活的贝叶斯估计方法奠定了基础,这将大大提高该模型及类似模型的实用性。

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