Stigma toward people living with HIV/AIDS (PLWHA) has impeded the response to the disease across the world. Widespread stigma leads to poor adherence of preventative measures while also causing PLWHA to avoid testing and care, delaying important treatment. Stigma is clearly a hugely complex construct. However, it can be broken down into components which include internalized stigma (how people with the trait feel about themselves) and enacted stigma (how a community reacts to an individual with the trait). Levels of HIV/AIDS-related stigma are particularly high in sub-Saharan Africa, which contributed to a surge in cases in Kenya during the late twentieth century. Since the early twenty-first century, the United Nations and governments around the world have worked to eliminate stigma from society and resulting public health education campaigns have improved the perception of PLWHA over time, but HIV/AIDS remains a significant problem, particularly in Kenya. We take a data-driven approach to create a time-dependent stigma function that captures both the level of internalized and enacted stigma in the population. We embed this within a compartmental model for HIV dynamics. Since 2000, the population in Kenya has been growing almost exponentially and so we rescale our model system to create a coupled system for HIV prevalence and fraction of individuals that are infected that seek treatment. This allows us to estimate model parameters from published data. We use the model to explore a range of scenarios in which either internalized or enacted stigma levels vary from those predicted by the data. This analysis allows us to understand the potential impact of different public health interventions on key HIV metrics such as prevalence and disease-related death and to see how close Kenya will get to achieving UN goals for these HIV and stigma metrics by 2030.

对艾滋病毒/艾滋病感染者 (PLWHA) 的污名阻碍了全世界对该疾病的反应。普遍的污名导致对预防措施的依从性差，同时也导致 PLWHA 避免检测和护理，延迟重要的治疗。污名显然是一个极其复杂的结构。但是，它可以分解为包括内在污名（具有该特质的人如何看待自己）和已制定的污名（社区如何对具有该特质的个人做出反应）的组件。在撒哈拉以南非洲，与艾滋病毒/艾滋病相关的污名化程度特别高，这导致 20 世纪后期肯尼亚的病例激增。自二十世纪初以来，联合国和世界各国政府一直致力于消除社会的耻辱感，由此产生的公共卫生教育运动随着时间的推移提高了对 PLWHA 的认识，但 HIV/AIDS 仍然是一个重大问题，特别是在肯尼亚。我们采用数据驱动的方法来创建一个与时间相关的污名函数，该函数同时捕获人口中内化和制定的污名水平。我们将其嵌入到 HIV 动态分区模型中。自 2000 年以来，肯尼亚的人口几乎呈指数级增长，因此我们重新调整了我们的模型系统，以创建一个耦合系统，用于衡量 HIV 流行率和寻求治疗的感染者比例。这使我们能够从已发布的数据中估计模型参数。我们使用该模型来探索一系列情景，其中内化或制定的污名水平与数据预测的不同。该分析使我们能够了解不同的公共卫生干预措施对关键 HIV 指标（例如流行率和与疾病相关的死亡）的潜在影响，并了解肯尼亚到 2030 年实现这些 HIV 和污名指标的联合国目标的距离。