论文标题
一个联合空间有条件自动回归模型，用于估计关键人群中的HIV患病率
A Joint Spatial Conditional Auto-Regressive Model for Estimating HIV Prevalence Rates Among Key Populations
论文作者
论文摘要
结束艾滋病毒/艾滋病大流行是未来十年的可持续发展目标之一。为了克服护理需求与可用资源之间的差距，需要更好地了解艾滋病毒流行病来指导政策决策，尤其是对于艾滋病毒感染风险较高的关键人群。由于其HIV监测数据非常有限，因此很难获得关键人群的艾滋病毒流行估计。在本文中，我们提出了一个所谓的关节空间有条件自身回火模型，用于估计关键人群中的HIV患病率。我们的模型从相邻位置和依赖人群借用信息。如实际数据分析中所示，它提供了比独立拟合每个关键人群的次级流行的更准确的估计值。此外，我们提供了一项研究，以揭示我们的建议提供更好预测的条件。该研究结合了理论研究和数值研究，揭示了我们提议的强度和局限性。
Ending the HIV/AIDS pandemic is among the Sustainable Development Goals for the next decade. In order to overcome the gap between the need for care and the available resources, better understanding of HIV epidemics is needed to guide policy decisions, especially for key populations that are at higher risk for HIV infection. Accurate HIV epidemic estimates for key populations have been difficult to obtain because their HIV surveillance data is very limited. In this paper, we propose a so-called joint spatial conditional auto-regressive model for estimating HIV prevalence rates among key populations. Our model borrows information from both neighboring locations and dependent populations. As illustrated in the real data analysis, it provides more accurate estimates than independently fitting the sub-epidemic for each key population. In addition, we provide a study to reveal the conditions that our proposal gives a better prediction. The study combines both theoretical investigation and numerical study, revealing strength and limitations of our proposal.
