IHBEM: Modeling the influence of individual and network behavior on HIV/HCV transmission dynamics

在世界各地，政府和社区都在努力控制艾滋病毒/艾滋病的传播。然而，尽管取得了戏剧性的进展，但仍有一些个人很难控制疫情。其中一个人口是由注射毒品者(PWID)组成的。在撒哈拉以南非洲以外的地区，大约五分之一的新艾滋病毒感染者在PWID，而在美国，截至2018年底，PWID的人数估计为370万人。丙型肝炎(丙型肝炎)是另一种对PWID有不成比例影响的病毒。为了消除或控制艾滋病毒和丙型肝炎病毒，重要的是要了解驱动传播的因素，并学习如何最好地进行干预以防止病毒的传播。这些因素在PWID中很难识别，因为PWID的社会网络复杂，包括他们的注射伙伴和他们所在的社区。本工作的目标是改进和发展干预措施，以控制PWID中HIV和丙型肝炎的传播。该项目将行为、社会经济和流动性数据与传染病传播动态相结合。跨学科研究团队将流行病学、临床医学、行为科学、工程学和数学建模结合在一起。该项目利用大型国际数据集开发复杂的传输模型，然后使用机器学习和其他新的数据科学技术来填补缺失的数据并对模型进行训练。这些模型可用于任何社区，以了解导致当地PWID社区艾滋病毒和丙型肝炎传播的因素。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

All around the world, governments and communities have worked to control the spread of HIV/AIDS. However, despite dramatic progress, there are populations of individuals in which epidemic control has been difficult. One of these populations is composed of people who inject drugs (PWID). About one in five new HIV infections outside of Sub-Sharan Africa are in PWID, and in the US the number of PWID was estimated to be 3.7 million at the end of 2018. Hepatitis C (HCV) is another virus that has had a disproportionate impact on PWID. To eliminate or control HIV and HCV it is important to understand the factors that drive transmission and learn how best to intervene to prevent transmission of the viruses. These factors are difficult to identify among PWID because of the complex social networks of PWID, including their injection partners and the communities in which they live.The goal of this work is to improve and develop interventions to control the spead of HIV and HCV in PWID. This project integrates behavioral, socioeconomic and mobility data with infectious disease transmission dynamics. The interdisciplinary research team brings together epidemiology, clinical medicine, behavioral science, engineering, and mathematical modeling. The project leverages large international datasets to develop complex transmission models and then uses machine learning and other novel data science techniques to fill in missing data and train the models. These models can be adapted to be used in any community to understand the factors that drive HIV and HCV transmission in their local PWID community.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.