Estimating the Impact of Structural Factorson HIV Transmission: A Multi-agent Spatial Simulation Modeling Study

项目总结/摘要 非洲裔美国人/黑人和白人之间艾滋病毒发病率的差距继续存在， 成长通过改变个人行为减少艾滋病毒感染率的干预措施 在非裔美国人/黑人中实施。不幸的是， 在缩小两个种族群体之间的差距方面没有成功。许多研究表明 邻域级因素（例如，邻里劣势，贫困和监禁） 显著影响艾滋病毒相关的风险行为，参与检测和治疗， 非裔美国人/黑人，特别是那些 被认为是异性恋。在这项研究中，我们将开发一个多智能体空间模拟模型， 将估计邻里水平因素的影响，如邻里劣势， 非裔美国人/黑人异性恋成年人的艾滋病毒发病率 可以减少这一人群传播的干预措施。首先，我们要实现空间 动态转换为基于代理的模型，以创建多代理空间仿真模型。我们将 然后估计邻里劣势对异性恋人群中HIV感染率的影响 非裔美国人/黑人成人。接下来，我们将评估移民和中产阶级化对 通过将移徙纳入模型，在这一人群中的艾滋病毒发病率。使用创新 方法，如多智能体空间仿真建模，我们将模拟复杂的 个人层面的行为，网络和邻里之间的相互作用，以估计 艾滋病毒对人口的结构性影响，并测试结构性干预措施。我们将 第一个开发空间动态模型来研究非洲异性恋者中的艾滋病毒 通过模型的发展，在城市环境中的美国/黑人成年人。本研究 也将被用作一个框架，以估计对人口的结构性影响， 美国境内的司法管辖区，以减少与艾滋病毒有关的差距和结构，允许 为了繁荣。

PROJECT SUMMARY/ABSTRACT The disparity in HIV incidence rates between African Americans/Blacks and whites continues to grow. Interventions aimed towards reducing HIV incidence by changing individual-level behaviors have been implemented among African Americans/Blacks. Unfortunately, there has been little to no success in closing the gap between the two racial groups. Many studies have determined neighborhood-level factors (e.g., neighborhood disadvantage, poverty, and incarceration) significantly influence HIV-related risk behaviors, engagement in testing and treatment, and adherence to HIV care and mortality among African Americans/Blacks, especially those who identify as heterosexual. In this study, we will develop a multi-agent spatial simulation model that will estimate the effect of neighborhood-level factors such as neighborhood disadvantage and gentrification on HIV incidence among heterosexual African American/Black adults to develop interventions that can reduce transmission among this population. We will first implement spatial dynamics into an agent-based model to create the multi-agent spatial simulation model. We will then estimate the effect of neighborhood disadvantage on HIV incidence among heterosexual African American/Black adults. Next, we will evaluate the effect of migration and gentrification on HIV incidence within this population by integrating migration into the model. Using innovative methods such as multi-agent spatial simulation modeling, we will simulate the complex interactions between individual-level behaviors, networks, and neighborhoods for estimating the structural influence of HIV on a population level and test structural-level interventions. We will be the first to develop a spatially dynamic model for studying HIV among heterosexual African American/Black adults in an urban setting through the development of the model. This research will also be used as a framework for estimating structural influences on populations in different jurisdictions within the United States to reduce HIV-related disparities and the structures that allow it to prosper.