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: 10673799
• 负责人: Stefan David Baral
• 金额: $ 72.07万
• 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/10673799
• 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; big-data science; data integration; 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; pandemic response; programs; response; sex; social media; socioeconomics; statistics; structural determinants; 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具有大型且未充分利用的数据集的新颖方法 评估在普遍流行病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) 降低艾滋病毒总体发病率。该应用程序对多个 最近特别关注通知 (NOSI) 中的关注领域：利用大数据阻止艾滋病毒 (NOT-AI-21- 054）。此外，这些目标符合当前艾滋病毒大流行的现实。虽然总体发病率稳步上升 过去 15 年来有所下降，2020 年有超过 150 万人新感染艾滋病毒，其中 100 万人 横跨SSA。艾滋病毒的风险在世界任何地方分布并不均匀。虽然特定的重点人群 被认为在许多高收入环境中感染艾滋病毒的风险增加，一般人口结构是 通常用于代表整个撒哈拉以南非洲地区的艾滋病毒流行情况。这种结构通常否定近端决定因素 艾滋病毒的感染和传播，包括无安全套性行为中传播风险的增加 男性之间、性工作和吸毒，以及变性人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，将利用关键人群和艾滋病毒相关的可用数据 辅助数据包括来自社交媒体、搜索模式、空间数据、社会经济和移民数据。我们 将汇集多个数据源并整合这些数据构建一个综合的数据仓库 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和结构决定因素。这些估计，加上小 数据稀疏的面积估计方法将通知动态传输模型来估计差异 重点人群中艾滋病毒继续传播的风险，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程序 与实施伙伴合作提供艾滋病毒检测、预防和治疗计划的数据。喀麦隆, 鉴于有足够的数据，肯尼亚、塞内加尔和南非将被用作示范国家， 愿意的政府，它们代表了撒哈拉以南非洲各自地区常见的艾滋病毒流行类型。 目标 1：建立一个灵活、全面且可访问的数据仓库，整理可用的艾滋病毒相关和 2000年以来SSA重点人群的相关辅助数据。目标 2：采用小面积估计 使用现有的直接和辅助数据来推断人口规模、患病率和人口数量的方法和空间统计 参与重点人群的级联治疗。目标 3：描述传播人群的特征 每个环境中关键人群的艾滋病毒归因分数，包括不同的艾滋病毒继续传播风险 多个时间范围内的传输。目标 4：评估定期收集的项目数据，为定制提供信息 调整实施战略，为重点人群提供艾滋病毒预防和治疗。

项目成果

Adjusting for hidden biases in sexual behaviour data: a mechanistic approach.

调整性行为数据中隐藏的偏见：机械方法。

• 发表时间: 2023-08-20
• 作者: Knight, Jesse;Wang, Siyi;Mishra, Sharmistha
• 通讯作者: Mishra, Sharmistha

Quantifying the impact of cascade inequalities: a modelling study on the prevention impacts of antiretroviral therapy scale-up in Eswatini.

量化级联不平等的影响：关于斯威士兰扩大抗逆转录病毒治疗的预防影响的模型研究。

• 发表时间: 2024-02-18
• 作者: Knight, Jesse;Ma, Huiting;Sithole, Bheki;Khumalo, Lungile;Wang, Linwei;Schwartz, Sheree;Muzart, Laura;Matse, Sindy;Mnisi, Zandile;Kaul, Rupert;Escobar, Michael;Baral, Stefan;Mishra, Sharmistha
• 通讯作者: Mishra, Sharmistha