Leveraging Big Data Science to Focus the HIV Response in Countries with Generalized HIV Epidemics

提议的目标的首要目标是利用新的方法处理大量未充分利用的数据集 评估在全球流行环境中日益具体的艾滋病毒应对措施的潜在影响 撒哈拉以南非洲(SSA)在降低总体艾滋病毒发病率方面取得了进展。此应用程序高度响应多个 最近的特别关注通知(NOSI)的感兴趣领域：利用大数据遏制艾滋病毒(非-AI-21- 054)。此外，这些目标与艾滋病毒大流行的当前现实相一致。虽然总的发病率稳定在 在过去15年中有所下降，2020年有超过150万人新感染艾滋病毒，其中包括100万人 横跨SSA。艾滋病毒的风险并不均匀地分布在世界任何地方。虽然特定的关键人群 在许多高收入环境中被认为感染艾滋病毒的风险增加，总体人口结构是 通常被用来代表整个SSA的艾滋病毒流行。这一构造通常否定了 艾滋病毒的获得和传播，包括在无避孕套的性行为中传播风险增加 男性之间，性工作，吸毒，以及变性人之间的感染和监禁 人口。 我们提出了一套雄心勃勃的目标，将利用关键人群的现有艾滋病毒相关数据以及 辅助数据，包括来自社交媒体的数据、搜索模式、空间数据、社会经济和移民数据。我们 我将组装多个数据源并集成这些数据，以建立一个全面的数据仓库，以 估计关键的特定人群指标，包括艾滋病毒发病率和流行率、人口规模、 参与艾滋病毒治疗级联，以及结构性决定因素。这些估计值，增加了小幅 数据稀疏的区域估计方法将通知动态传输模型估计差分 重点人群中艾滋病毒进一步传播的风险，并更好地满足重点人群的需求 与普通人群的方法相比。最后，我们将利用规模非常大且未得到充分利用的计划 与执行伙伴合作，为艾滋病毒检测、预防和治疗方案提供数据。喀麦隆， 肯尼亚、塞内加尔和南非将被作为示范国家，因为有足够的数据， 他们是自愿的政府，他们代表了各自SSA地区共同的艾滋病毒流行类型。 目标1：建立灵活、全面和可访问的数据仓库，整理与艾滋病毒有关的和可访问的现有数据仓库 2000年以来社科局重点人群相关辅助数据。目标2：使用小面积估计 方法和空间统计使用可用的直接和辅助数据来推断人口规模、流行率和 参与关键人群的治疗级联。目标3：确定传播人群的特征 在每个环境中的关键人群中艾滋病毒的归属比例，包含不同的艾滋病毒继续风险 在多个时间范围内传输。目标4：评估常规收集的计划数据，以便为裁剪提供信息 以及调整为重点人群提供艾滋病毒预防和治疗的实施战略。

The overarching goal of the proposed aims is to leverage novel methods with large and underutilized data sets to evaluate the potential impact of increasingly specific HIV responses across generalized epidemic settings in Sub-Saharan Africa (SSA) in reducing overall HIV incidence. This application is highly responsive to multiple areas of interest in the recent Notice of Special Interest (NOSI): Harnessing Big Data to Halt HIV (NOT-AI-21- 054). Moreover, these aims align with current realities of the HIV pandemic. While overall incidence has steadily declined over the last 15 years, over 1.5 million people newly acquired HIV in 2020 including one million people across SSA. The risk for HIV is not evenly distributed anywhere in the world. And while specific key populations are recognized to be at increased risk of HIV in many higher income settings, a general population construct is often used to represent HIV epidemics across SSA. This construct typically negates proximal determinants of HIV acquisition and transmission, including heightened transmission risks in the contexts of condomless sex between men, sex work, and drug use, as well as infections among transgender people and incarcerated populations. We propose an ambitious set of aims that will leverage available HIV-related data for key populations as well as auxiliary data including from social media, search patterns, spatial data, socioeconomic and migration data. We will assemble multiple data sources and integrate these data to build a comprehensive data warehouse to estimate key population-specific indicators including HIV incidence and prevalence, population size, engagement in the HIV treatment cascade, and structural determinants. These estimates, augmented by small area estimation methods where data are sparse, will inform dynamic transmission models to estimate differential risks of onward HIV transmission among key populations and to better address the needs of key populations compared with general-population approaches. Finally, we will leverage very large and underutilized program data for HIV testing, prevention, and treatment programs in partnership with implementing partners. Cameroon, Kenya, Senegal, and South Africa will be used as exemplar countries given that there exists sufficient data, willing governments, and they represent common HIV epidemic typologies in their respective regions of SSA. Aim 1: Build a flexible, comprehensive, and accessible data warehouse collating available HIV-related and relevant auxiliary data for key populations from 2000 onward in SSA. Aim 2: Employ small area estimation methods and spatial statistics using available direct and auxiliary data to infer population size, prevalence, and engagement in the treatment cascade for key populations. Aim 3: Characterize the transmission population attributable fraction for HIV among key populations in each setting, incorporating differential risks of onward HIV transmission over multiple time horizons. Aim 4: Evaluate routinely collected program data to inform tailoring and adaptation of implementation strategies for delivery of HIV prevention and treatment for key populations.