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Collaborative Research: Modeling the opioid and HIV epidemics

合作研究：模拟阿片类药物和艾滋病毒流行

基本信息

批准号：
1951595
负责人：
Maia Martcheva
金额：
$ 15万
依托单位：
University of Florida
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：
2020-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951595&HistoricalAwards=false
关键词：
Collaborative Research Modeling opioid HIV

项目摘要

In the last 15 years, US deaths due to the opioid epidemic have quadrupled. The increase in opioid use, and in particular, in injection drug use, has both directly and indirectly contributed to the spread of infectious diseases such as HIV. Drug use also lowers inhibitions, which in turn facilitates more HIV transmission. This has resulted in a leveling off of the decrease of HIV cases in the US. The goal of this project is to develop novel, data-driven, multi-scale models of the intertwined opioid and HIV epidemics. Such models will lead to a better understanding of the interplay of the epidemics and can be used to guide public health policy, for instance to reverse the recent trends in HIV cases. The models will use data on HIV and opioid use in the US as well as viremia data for individuals who both have HIV and use drugs. Considering the dynamic interplay of the two epidemics will provide insights that are missing when one focuses on either alone, including how processes within individuals bear on dynamics at the population scale. Our preliminary results have already identified promising methods of control that targeting the drug abuse epidemic could reduce HIV risk among drug users. The PIs will test the robustness of these control strategies in multiple modeling scenarios. This research will help identify improved strategies to control these two intertwined epidemics.To understand the two epidemics, a multi-scale immuno-epidemiological model of the opioid and HIV epidemics will be developed, analyzed and used for simulations. This model explicitly includes the dynamics of HIV within hosts and the effect of such dynamics on transmission of HIV between hosts, as well as the effect of opioid use on each level of infectious disease dynamics. The model will be linked to multi-scale data, and identifiability analyses will be performed. A novel network version of the multi-scale model will be developed that allows modeling of individuals with varying number of contacts. The impact of the combined effects of contact network structure and within-host dynamics on the two linked epidemics will be investigated both analytically and numerically, using novel analytical techniques that will be developed. Finally, optimal control approaches will be applied to the multi-scale immuno-epidemiological network model in order to characterize effect of control strategies. To study the robustness of the two control methods identified in the preliminary results (targeting drug abuse and reducing HIV risk among drug users), a comparison of these methods with results of the optimal control models will be made. Rigorous incorporation of multi-scale data and simulations will uncover any dependence of the best control strategy on which modeling framework is employed. All these results will be synthesized in a book on immuno-epidemiological modeling that incorporates elements of this research, in the context of a broad perspective on this rapidly developing field. The book will be useful to graduate students, academics, and practitioners.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.

在过去的15年里，美国因阿片类药物流行而死亡的人数翻了两番。类阿片使用的增加，特别是注射吸毒的增加，直接和间接地助长了艾滋病毒等传染病的传播。吸毒还降低了抑制力，这反过来又助长了艾滋病毒的传播。这导致美国艾滋病毒病例的减少趋于平稳。该项目的目标是开发新的、数据驱动的、多尺度的阿片类药物和艾滋病毒流行病交织模型。这种模式将有助于更好地了解流行病之间的相互作用，并可用于指导公共卫生政策，例如扭转艾滋病毒病例最近的趋势。这些模型将使用美国艾滋病毒和阿片类药物使用的数据，以及艾滋病毒感染者和吸毒者的病毒血症数据。考虑到这两种流行病的动态相互作用，将提供人们只关注其中一种流行病时所缺乏的见解，包括个人内部的过程如何影响人口规模的动态。我们的初步结果已经确定了有希望的控制方法，这些方法针对药物滥用流行病，可以减少吸毒者中的艾滋病毒风险。PI将在多个建模场景中测试这些控制策略的鲁棒性。这项研究将有助于确定控制这两种相互交织的流行病的改进策略。为了了解这两种流行病，将开发、分析并用于模拟阿片类药物和艾滋病毒流行病的多尺度免疫流行病学模型。该模型明确包括宿主体内HIV的动态和这种动态对宿主之间HIV传播的影响，以及阿片类药物使用对传染病动态各个层面的影响。该模型将与多尺度数据相关联，并将进行可识别性分析。一个新的网络版本的多尺度模型将被开发，允许不同数量的接触的个人建模。接触网络结构和宿主内动力学对两种相互关联的流行病的综合影响将使用将要开发的新的分析技术进行分析和数值研究。最后，将最优控制方法应用于多尺度免疫流行病学网络模型，以表征控制策略的效果。为了研究初步结果中确定的两种控制方法（针对药物滥用和减少吸毒者中的艾滋病毒风险）的稳健性，将对这些方法与最佳控制模型的结果进行比较。多尺度数据和模拟的严格结合将揭示采用建模框架的最佳控制策略的任何依赖性。所有这些结果将综合在一本关于免疫流行病学建模的书中，该书结合了这项研究的要素，并在这个快速发展的领域的广泛视角的背景下。这本书将是有用的研究生，学者和从业者。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准的评估支持。