Reconstructing HIV Epidemics from HIV Phylogenetics

 描述(申请人提供)：尽管正在进行预防运动，但艾滋病毒流行在全球范围内持续存在。为了监测干预运动的效果，更广泛地说，为了估计人类人口的发病率和流行率，必须准确地重建疾病传播模式。我们的项目汇集了来自临床、分子生物学、流行病学、数学和进化领域的经验丰富的研究人员团队。我们将仔细检查系统发育的局限性，并开发统计方法来解决和量化它们对传播重建的影响。我们将建立新的系统动力学推断方法，充分考虑这些限制，并纳入社会网络和流行病学科学中发展的最新统计进展。我们将使用七个描述艾滋病毒在宿主内和宿主间进化和传播的大型数据集。这些数据包括来自美国、欧洲、前苏联和非洲的数千例艾滋病毒病例，由艾滋病毒序列、临床和人口数据描述。该项目的主要假设是，HIV-1的进化过程记录了受其流行病史影响的基因突变。因此，在这个项目中，我们的目标是从HIV的系统发育分析中提取流行病学信息，并将其与临床、人口学和地理数据相结合，以推动当前最先进的流行病学推断方法的界限。我们的具体目标是：1)开发一个更现实的宿主内HIV进化动力学模型；2)联合推断未观察到的传播历史和病毒系统发生；以及3)创建促进分子流行病学推断的公共软件资源。

 DESCRIPTION (provided by applicant): Despite ongoing prevention campaigns, the HIV epidemic persists on a global scale. To monitor the effects of intervention campaigns, and more generally, to estimate incidence and prevalence in human populations, patterns of disease spread must be accurately reconstructed. Our project brings together a team of experienced researchers from clinical, molecular biology, epidemiological, mathematical, and evolutionary fields. We will carefully examine phylogenetic limitations and develop statistical methods to address and quantify their effects on transmission reconstruction. We will build novel phylodynamic inference methods that adequately take these limitations into account, and incorporate recent statistical advances developed in social network and epidemiological sciences. We will use seven large datasets describing both HIV within-host and between-host evolution and transmission. These data comprise many thousands of HIV cases from the US, Europe, former Soviet Union, and Africa, described by HIV sequence, clinical and demographic data. The overarching hypothesis of this project is that the evolutionary process of HIV-1 records genetic mutations affected by its epidemiological history. Thus, in this project we aim to extract epidemiological information from phylogenetic analyses of HIV and integrate it with clinical, demographical, and geographical data to push the boundaries of current state-of-the-art epidemiological inference methods. Our specific aims are: 1) Develop a more realistic within-host model of HIV evolutionary dynamics; 2) Jointly infer the unobserved transmission history and virus phylogeny; and 3) Create public software resources that facilitate molecular epidemiology inferences.