Excellence in Research: Mathematical Analysis of the Prevention of HIV with PrEP and HAART Treatment

卓越研究：PrEP 和 HAART 治疗预防 HIV 的数学分析

• 批准号: 2000044
• 负责人: Katharine Gurski
• 金额: $ 34.49万
• 依托单位: Howard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000044&HistoricalAwards=false
• 关键词: Excellence; Research; Mathematical; Analysis; Prevention

Prevention, mitigation, and eradication of HIV has been the focus of governments, scientists, and public health professionals for decades. Recent reports, such as the “Ending the HIV Epidemic: A Plan for America” distributed by the US Department of Health and Human Services, have generated a renewed hope for the millions of people around the world affected by this disease. To achieve this goal, it is imperative to understand the most effective use of available resources in light of the new prophylaxis treatment and the continued improvements of the highly active anti-retroviral treatment, particularly in the case of non-compliance. A realistic mathematical model, including these two treatments, as well as disproportional effects on disparate subpopulations, is essential for effective resource utilization. The broader impacts of this proposal are far-reaching from a public health perspective, but will also serve as a platform to train primarily underrepresented undergraduate and graduate students at the interface of mathematics and medicine. This collaboration between researchers at Howard University, University of Maryland, Baltimore County, and the National Institutes of Health will train students to work in a collaborative team environment focused on addressing a pressing scientific problem.Realistic mathematical models can suggest best courses of intervention that can significantly reduce the number of HIV infections. The effect of the pre-exposure prophylaxis treatment (PrEP) will be included in a previously developed mathematical model of HIV. Previous models of imperfect vaccines have shown the potential for a backwards bifurcation that can dramatically affect the dynamics of the epidemic. As a result, the same intervention that brings a model without a vaccine to the disease-free equilibrium, could drive the dynamics of the model with PrEP to a stable endemic equilibrium. Transmission of HIV depends on the viral-load of the infected individual. Thus, realistic mathematical models must include the effect of non-steady highly active anti-retroviral treatment (HAART) through structured treatment interruptions, lack of adherence to drug regimen and drug resistance. Non-steady HAART treatment regimes and the subsequent two-way movement between virally suppressed and chronically infected will require using non-exponential treatment stages into the model, which will be fitted to publicly available Multicenter AIDS Cohort Study (MACS) data from the Johns Hopkins Bloomberg School of Public Health. Since public health data will be used in the mathematical model, parameter identifiability will be addressed structurally and practically. Several numerical tools for accessing structural identifiability of the model will be used. The profile likelihood method will be used to assess the practical identifiability of model parameters and to provide confidence intervals for parameter estimation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

几十年来，预防、缓解和根除艾滋病毒一直是各国政府、科学家和公共卫生专业人员的重点。最近的报告，如美国卫生和人类服务部分发的“结束艾滋病毒流行病：美国计划”，为世界各地受这一疾病影响的数百万人带来了新的希望。为了实现这一目标，必须了解如何最有效地利用现有资源，因为有了新的预防性治疗和高效抗逆转录病毒治疗的不断改进，特别是在不遵守规定的情况下。一个现实的数学模型，包括这两种治疗方法，以及对不同亚群的不成比例的影响，是有效利用资源的必要条件。从公共卫生的角度来看，这一提议的更广泛影响是深远的，但也将作为一个平台，在数学和医学的界面上培训主要代表性不足的本科生和研究生。霍华德大学、马里兰州大学巴尔的摩县分校和美国国立卫生研究院的研究人员之间的这项合作将培养学生在一个合作的团队环境中工作，专注于解决一个紧迫的科学问题。现实的数学模型可以建议最佳的干预方案，可以显着减少艾滋病毒感染的数量。暴露前预防性治疗（PrEP）的效果将纳入先前开发的HIV数学模型中。以前的不完美疫苗模型已经显示出向后分叉的可能性，这可能会极大地影响流行病的动态。因此，将没有疫苗的模型带到无病平衡的相同干预可以将具有PrEP的模型的动力学驱动到稳定的地方病平衡。艾滋病毒的传播取决于受感染个体的病毒载量。因此，现实的数学模型必须包括非稳定的高活性抗逆转录病毒治疗（HAART）的效果，通过结构化的治疗中断，缺乏遵守药物治疗方案和耐药性。非稳定的HAART治疗方案以及随后在病毒抑制和慢性感染之间的双向移动将需要在模型中使用非指数治疗阶段，该模型将与约翰霍普金斯彭博公共卫生学院公开提供的多中心艾滋病队列研究（MACS）数据相拟合。由于公共卫生数据将用于数学模型，参数可识别性将在结构上和实际解决。将使用几种数值工具来评估模型的结构可识别性。剖面似然法将用于评估模型参数的实际可识别性，并为参数估计提供置信区间。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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• DOI: 10.30707/lib10.1.1682014077.793816
• 发表时间: 2023
• 期刊: Letters in Biomathematics
• 作者: Prosper, Olivia;Gurski, Katharine;Teboh-Ewungkem, Miranda I.;Peace, Angela;Feng, Zhilan;Reynolds, Margaret;Manore, Carrie
• 通讯作者: Manore, Carrie

An Agent-Based Model of COVID-19 on the Diamond Princess Cruise Ship

• DOI: 10.1137/21s1462520
• 发表时间: 2022
• 期刊: SIAM Undergraduate Research Online
• 作者: Naomi A. Rankin

The effect of PrEP uptake and adherence on the spread of HIV in the presence of casual and long-term partnerships

• DOI: 10.3934/mbe.2022555
• 发表时间: 2022-01-01
• 期刊: MATHEMATICAL BIOSCIENCES AND ENGINEERING
• 影响因子: 2.6
• 作者: Gutowska，S. J.;Hoffman，K. A.;Gurski，K. F.
• 通讯作者: Gurski，K. F.