Rapid Tests for Recent Infection (RTRI) for Precision Public Health in Sub-Saharan Africa: Next-Generation Strategies Amid Changing HIV Epidemiology

摘要/总结 艾滋病毒是撒哈拉以南非洲（SSA）的主要死亡原因，但新感染率正在下降。因此，在本发明中， 一些艾滋病毒服务的效率正在下降：艾滋病毒预防正在减少每名服务对象的感染， 艾滋病毒检测产量（即，导致新诊断的测试比例）正在下降。鉴于资源限制， 艾滋病毒预防和检测项目需要提高效率，以保持发展势头， 疫情控制。具体而言，可以使用精确的公共卫生方法来集中服务， 健康效益将是最大的。精确的公共卫生一直是具有挑战性的，以实施在SSA的 由于大多数传播并不局限于可识别的关键人群， 精确的回答。直到最近，描述艾滋病毒传播的唯一工具一直是系统发育分析 这对于大规模的实时反应来说太慢和昂贵。快速检测近期 艾滋病病毒感染（RTRI）最近在撒哈拉以南非洲可用，提供了一个新的机会，以确定主动传播 集群并精确响应。RTRI检测大约一年出现的高亲和力抗体 艾滋病毒感染后。如果只检测到低亲合力抗体，则这种感染可能发生在过去一年。 赞比亚是RTRI的早期采用者，并在2021年对RTRI的三分之二阳性艾滋病毒检测进行了随访。尽管 赞比亚的RTRI计划规模庞大，尚未对其影响或成本效益进行评估， 疫情应对阈值尚未优化。然而，现在收集的大量数据 RTRI计划可以指导更高性能的下一代疫情应对策略。我们的团队已被 赞比亚艾滋病毒政策、方案实施、卫生经济学、生物伦理学、地理空间分析方面的专业知识， 机器学习和传染病建模。我们帮助开发了唯一的艾滋病毒社交网络模型， 经严格验证，可预测SSA的艾滋病毒发病率、流行率和传播模式， 十多年来，国际和国家一级的艾滋病毒政策决策一直使用这一方法。我们建议合作伙伴 与赞比亚传染病研究中心（CIDRZ）合作，这是一个赞比亚运营的非政府组织， 十多年来，在艾滋病毒政策制定和实施（包括RTRI）方面支持卫生部，以（目标1） 确定RTRI计划是否可以帮助赞比亚实现其流行病控制目标，（目标2）衡量 成本和估计RTRI计划的成本效益，以及（目标3）使用“大数据”方法设计 下一代疫情应对措施，考虑到敏感性与特异性、精确性与 易用性和精确性与避免收集可能造成耻辱的数据元素。同时追求 科学目标和假设与NOSI“利用大数据遏制艾滋病毒”一致，我们还将直接 通过提供实时分析、艾滋病毒爆发地图和RTRI政策决策的投入，发挥政策影响。

ABSTRACT/SUMMARY HIV is a leading cause of death in sub-Saharan Africa (SSA), but rates of new infections in declining. As a result, some HIV services are becoming less efficient: HIV prevention is averting fewer infections per client served, and HIV testing yield (i.e., the fraction of tests leading to new diagnoses) is declining. Given resource constraints, HIV prevention and testing programs will need to increase efficiency in order to maintain momentum toward epidemic control. Specifically, a precision public health approach could be used to focus services where their health benefits would be greatest. Precision public health has been challenging to implement in SSA’s generalized HIV epidemics because most transmission is not confined to identifiable key populations, eluding precise responses. Until recently, the only tool for describing HIV transmission has been phylogenetic analysis of HIV genome sequences, which is too slow and costly for real-time response at scale. Rapid tests for recent HIV infection (RTRI) recently became available in SSA, offering a novel opportunity to identify active transmission clusters and respond with precision. RTRI detects high-avidity antibodies that appear approximately one year after HIV infection. If only low-avidity antibodies are detected, this infection likely occurred in the past year. Zambia is an early adopter of RTRI and followed-up two-thirds of positive HIV tests with RTRI in 2021. Despite the large scale of Zambia’s RTRI program, it has not been evaluated in terms of impact or cost-effectiveness, and outbreak response thresholds have not been optimized. However, the wealth of data now collected by the RTRI program could guide higher-performing, next-generation outbreak response strategies. Our team has expertise in Zambian HIV policy, program implementation, health economics, bioethics, geospatial analysis, machine learning, and infectious disease modeling. We helped to develop the only HIV social network model rigorously validated to predict HIV incidence, prevalence, and transmission patterns in SSA, which has been used for international and country-level HIV policy decision-making for over a decade. We propose to partner with the Center for Infectious Disease Research in Zambia (CIDRZ), a Zambian-run NGO that has been supporting MoH for over a decade in HIV policy-making and implementation (including RTRI), to (Aim 1) determine whether the RTRI program can help Zambia achieve its epidemic control goals, (Aim 2) measure the cost and estimate the cost-effectiveness of the RTRI program, and (Aim 3) use “big data” methods to design next-generation outbreak responses, taking into account trade-offs of sensitivity vs. specificity, precision vs. ease-of-use, and precision vs. avoidance of collecting potentially stigmatizing data elements. While pursuing scientific Aims and hypotheses aligned with the NOSI “Harnessing Big Data to Halt HIV”, we will also have direct policy impact by providing real-time analytics, HIV outbreak mapping, and input for RTRI policy decision-making.