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

利用大数据科学重点关注艾滋病毒流行国家的艾滋病毒应对工作

• 批准号: 10548465
• 负责人: Stefan David Baral
• 金额: $ 77.33万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-29 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10548465
• 关键词: AIDS prevention; Address; Africa South of the Sahara; Area; Big Data; Cameroon; Communities; Country; Data; Data Collection; Data Set; Drug usage; Eastern Africa; Epidemic; Epidemiologist; Funding; General Population; Goals; Government; HIV; HIV risk; Heterogeneity; Household; Human immunodeficiency virus test; Imprisonment; Incidence; Income; Individual; Infection; Investments; Kenya; Leadership; Letters; Mathematics; Measures; Methods; Modeling; National Institute of Allergy and Infectious Disease; Pattern; Pennsylvania; Persons; Population; Population Attributable Risks; Population Sizes; Predisposition; Prevalence; Prevention program; Public Health Schools; Research Design; Resources; Risk; Senegal; South Africa; Southern Africa; Surveys; Time; Typology; Universities; Unsafe Sex; Work; arm; base; big-data science; data warehouse; experience; flexibility; heterogenous data; high risk; implementation strategy; interest; mathematical model; men; migration; models and simulation; multidimensional data; multiple data sources; novel; pandemic disease; programs; response; sex; social media; socioeconomics; statistics; time use; transgender; transmission process; treatment program

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.

拟议目标的总体目标是利用大型和未充分利用的数据集的新方法 评估在普遍流行的情况下，越来越具体的艾滋病毒应对措施的潜在影响， 撒哈拉以南非洲在降低艾滋病毒总体发病率方面的作用。此应用程序高度响应多个 特别关注通知（NOSI）：利用大数据遏制艾滋病毒（NOT-AI-21- 054）。此外，这些目标符合艾滋病毒流行病的当前现实。虽然总体发病率稳步上升， 在过去的15年里，艾滋病毒感染率下降，2020年有150多万人新感染艾滋病毒，其中包括100万人。 在SSA。艾滋病毒的风险在世界任何地方都不是均匀分布的。虽然特定的关键人群 被认为在许多高收入环境中感染艾滋病毒的风险增加， 通常用来代表整个撒哈拉以南非洲的艾滋病流行情况。这种结构通常否定了 艾滋病毒的感染和传播，包括在无安全套性行为中传播风险的增加 男性之间，性工作和毒品使用，以及跨性别者和被监禁者之间的感染 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， 辅助数据，包括社交媒体、搜索模式、空间数据、社会经济和移民数据。我们 将集合多个数据源并整合这些数据，以构建一个全面的数据仓库， 估计关键的特定人口指标，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和结构性决定因素。这些估计，增加了小 数据稀疏的区域估计方法将通知动态传输模型来估计差分 艾滋病毒在重点人群中进一步传播的风险，并更好地满足重点人群的需求 与一般人群的方法相比。最后，我们将利用非常大且未充分利用的项目， 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用作范例国家，因为有足够的数据， 他们代表了撒南非洲各自地区的共同艾滋病毒流行类型。 目标1：建立一个灵活、全面和可访问的数据仓库， 从2000年起，在撒哈拉以南非洲的重点人群的相关辅助数据。目标2：采用小面积估计 方法和空间统计使用现有的直接和辅助数据来推断人口规模，患病率， 参与关键人群的治疗级联。目标3：确定传播人群的特征 每种环境下关键人群中艾滋病毒的可归因比例，包括艾滋病毒传播的不同风险 在多个时间范围内传输。目标4：评估定期收集的项目数据，为定制提供信息 并调整实施战略，为重点人群提供艾滋病毒预防和治疗。