Department of Statistics, Pennsylvania State University, University Park, 323 Thomas Building, University Park, PA 16802, USA.
Sex Transm Infect. 2012 Dec;88 Suppl 2(Suppl_2):i58-64. doi: 10.1136/sextrans-2012-050689. Epub 2012 Oct 30.
As the global HIV pandemic enters its fourth decade, countries have collected longer time series of surveillance data, and the AIDS-specific mortality has been substantially reduced by the increasing availability of antiretroviral treatment. A refined model with a greater flexibility to fit longer time series of surveillance data is desired.
In this article, we present a new epidemiological model that allows the HIV infection rate, r(t), to change over years. The annual change of infection rate is modelled by a linear combination of three key factors: the past prevalence, the past infection rate and a stabilisation condition. We focus on fitting the antenatal clinic (ANC) data and household surveys which are the most commonly available data source for generalised epidemics defined by the overall prevalence being above 1%. A hierarchical model is used to account for the repeated measurement within a clinic. A Bayesian approach is used for the parameter estimation.
We evaluate the performance of the newly proposed model on the ANC data collected from urban and rural areas of 31 countries with generalised epidemics in sub-Sahara Africa. The three factors in the proposed model all have significant contributions to the reconstruction of r(t) trends. It improves the prevalence fit over the classic Estimation and Projection Package model and provides more realistic projections when the classic model encounters problems.
The proposed model better captures the main pattern of the HIV/AIDS dynamic. It also retains the simplicity of the classic model with a few interpretable parameters that are easy to interpret and estimate.
随着全球艾滋病大流行进入第四个十年，各国已经收集了更长时间序列的监测数据，并且由于抗逆转录病毒治疗的普及，艾滋病特定死亡率已经大大降低。人们希望有一种更灵活的模型，可以更好地拟合更长时间序列的监测数据。
在本文中，我们提出了一种新的流行病学模型，该模型允许 HIV 感染率 r(t)随时间变化。通过线性组合三个关键因素来模拟每年感染率的变化：过去的流行率、过去的感染率和稳定条件。我们专注于拟合产前检查 (ANC) 数据和家庭调查，这是最常见的通用流行情况数据源，定义为总体流行率超过 1%。使用分层模型来解释诊所内的重复测量。使用贝叶斯方法进行参数估计。
我们在撒哈拉以南非洲的 31 个国家的城市和农村地区的 ANC 数据上评估了新提出的模型的性能。该模型中的三个因素都对 r(t)趋势的重建有显著贡献。它改善了经典估计和预测软件包模型对流行率的拟合，并在经典模型遇到问题时提供了更现实的预测。
该模型更好地捕捉了 HIV/AIDS 动态的主要模式。它还保留了经典模型的简单性，具有几个易于解释和估计的可解释参数。
