HIV--Emergence of Drug Resistance

描述(由申请人提供)：本提案的目标是将临床医学与数学建模相结合，以设计艾滋病毒(基于抗逆转录病毒疗法(ARV)和降低风险的组合)的流行病控制策略，以最大限度地提高公众健康利益。我们将为旧金山感染艾滋病毒的同性恋社区设计疫情控制策略，目前旧金山的艾滋病毒流行率为30%。我们将利用三种主要的方法来完成我们的研究目标：开发和分析传播模型，将传播模型与数据相匹配，以及使用新的统计和系统发育分析来分析数据集。我们有三个具体的目标：(I)建立和分析新的传播模型，(Ii)利用现有的时间相关序列数据来建立统计模型，以确定传播模型的输入分布，以及(Iii)使用传播模型来设计流行病控制策略。为了完成具体目标1，我们将：(A)制定一系列新的传播模型，可用于评估和预测抗逆转录病毒对旧金山同性恋社区艾滋病毒流行动态和演变的影响，(B)使用这些模型预测耐药发生率和流行率，(C)将数据纳入理论预测，以及(D)确定哪些耐药产生(DRG)机制在艾滋病毒耐药的出现和传播中最重要。我们将评估DRG机制对耐药艾滋病毒流行的贡献，原因包括：患者、医生、病毒株和治疗方案。为了完成具体目标2，我们将：(A)使用分类和回归树(CART)开发耐药表型的分布，以及(B)使用贝叶斯系统发生重建来估计作为DRG机制的函数的个人特异性突变率。为了完成具体目标3，我们将：(A)根据各种评估标准，使用医疗干预措施(ARV)和行为干预措施(减少风险)，设计详细的疫情控制战略，以及(B)使用评估标准来评估医疗干预措施和行为干预措施之间的权衡。

DESCRIPTION (provided by applicant): The objective of this proposal is to link clinical medicine with mathematical modeling in order to design epidemic control strategies for HIV (based upon combination antiretroviral therapies (ARV) and risk reduction) that will maximize public health benefit. We will design epidemic control strategies for the HIV-infected gay community in San Francisco, where the current prevalence of HIV is 30%. We will accomplish our research objective by utilizing three main methodological approaches: developing and analyzing transmission models, fitting transmission models to data, and using novel statistical and phylogenetic analyses to analyze data sets. We have three specific aims: (i) to formulate and analyze new transmission models, (ii) to develop statistical models using existing time dependent sequence data to specify distributions of inputs for the transmission models, and (iii) to use the transmission models to design epidemic control strategies. To complete specific aim 1 we will: (a) formulate a series of new transmission models that can be used to evaluate and to predict the effects of ARV on the epidemic dynamics and the evolution of the HIV epidemic in the gay community in San Francisco, (b) use the models to predict the incidence and the prevalence of drug resistance, (c) incorporate data into the theoretical predictions, and (d) identify which drug-resistance-generating (DRG) mechanisms are the most important in contributing to the emergence and transmission of drug resistant HIV. We will evaluate the contribution of the DRG mechanisms on the epidemic of drug resistant HIV that are due to: the patient, the doctor, the viral strain, and the treatment regimen. To complete specific aim 2 we will: (a) develop a distribution of drug resistant phenotypes using Classification and Regression Trees (CART), and (b) estimate person-specific mutation rates as a function of DRG mechanisms using Bayesian phylogenetic reconstruction. To complete specific aim 3 we will: (a) design detailed epidemic control strategies based upon a variety of evaluation criteria using both medical interventions (ARV) and behavioral interventions (risk reduction), and (b) use the evaluation criteria to evaluate trade-offs between the medical and behavioral interventions.