Measuring spillover effects of reactive, focal malaria elimination interventions

项目摘要/摘要 这项拟议的K01奖项将支持流行病学家Jade Benjamin-chung博士的职业发展 在加州大学伯克利分校(UC)流行病学和生物统计学系。本杰明博士- 钟的职业目标是成为将严格的生物统计学方法应用于传染性疾病的领导者 控制和消除疾病。为了支持她的职业发展，这份申请提出了一项她将 导致填补消除疟疾干预措施研究的一个重要空白。随着疟疾传播的减少 变得更加异质，其特点是传播的焦点热点。全面覆盖 干预变得不切实际，也不划算。反应性、焦点干预通过以下方式针对热点 向居住在出现监测的有症状疟疾病例附近的人提供抗疟疾药物 地点。当地提供的干预措施旨在减少传播到重点治疗区以外的人， 包括无症状疟疾病例，他们被认为是#年大部分传播的罪魁祸首。 消除设置。因此，关于干预措施是否减少了干预接受者的疾病的信息 (“直接效应”)与干预集群中的非干预接受者(即“溢出效应”)对 了解这些干预措施是否可以消除疟疾，但目前的研究尚未估计到这一点 溢出效应。这项研究将估计特定地点和集合的直接影响和溢出效应在三个 低疟疾传播环境下反应性、重点消除疟疾干预的整群随机试验 在纳米比亚、斯威士兰和赞比亚。具体目标是：(1)评估直接影响和溢出效应 反应性重点消灭疟疾干预措施对恶性疟发病率和流行率的影响 以及(2)评估反应性、重点消除疟疾干预措施的直接影响和溢出影响 根据距离干预、干预覆盖范围和从事件病例检测到的时间而有所不同。证据： 溢出效应将表明，反应性、有针对性的干预措施有望在以下情况下消除疟疾 扩大了规模。没有溢出效应将表明干预措施没有中断传播；如果 因此，有关感染的空间结构的信息将告知谁和多少人要治疗 使用重新设计的干预措施。这项研究将应用基于机器学习的新方法来估计 适用于传染病数据的因果关系。本申请提出为期4年的培训计划。 包括加州大学伯克利分校的两位顶尖生物统计学家和加州大学伯克利分校的两位疟疾流行病学家的指导 旧金山。本杰明-钟博士的培训目标是(1)发展机器学习和因果关系方面的技能 相关数据的推断方法，(2)了解疟疾生物学和流行病学，(3)加强HER 具备软件开发和空间分析技能。加州大学伯克利分校是本杰明-钟博士 推进她的职业目标，因为它在生物统计学方面拥有领先的教员，在研究和研究方面有着良好的记录 教授传染病和生物统计学，并与加州大学旧金山分校的消除疟疾倡议密切相关。

PROJECT SUMMARY / ABSTRACT This proposed K01 award will support the career development of Dr. Jade Benjamin-Chung, an Epidemiologist in the Division of Epidemiology & Biostatistics at the University of California (UC), Berkeley. Dr. Benjamin- Chung’s career goal is to become a leader in the application of rigorous biostatistical methods to infectious disease control and elimination. To support her career development, this application proposes a study she will lead to fill an important gap in research on malaria elimination interventions. As malaria transmission declines it becomes more heterogeneous and is characterized by focal hot spots of transmission. Blanket coverage of interventions becomes impractical and is not cost-effective. Reactive, focal interventions target hot spots by delivering antimalarials to people residing near to a symptomatic malaria case that presents to a surveillance site. Focally delivered interventions aim to reduce transmission to those outside focal treatment zones, including to asymptomatic malaria cases, who are thought to be responsible for the majority of transmission in elimination settings. Thus, information about whether interventions reduce illness among intervention recipients (“direct effects”) vs. non-intervention recipients in intervention clusters (i.e., “spillover effects”) is critical to understanding whether these interventions can eliminate malaria, yet current studies have not estimated such spillover effects. This study will estimate site-specific and pooled direct effects and spillover effects in three cluster-randomized trials of reactive, focal malaria elimination interventions in low malaria transmission settings in Namibia, Swaziland, and Zambia. The specific aims are to (1) estimate direct effects and spillover effects of reactive, focal malaria elimination interventions on Plasmodium falciparum malaria incidence and prevalence and (2) assess whether direct effects and spillover effects of reactive, focal malaria elimination interventions vary by distance to intervention, intervention coverage, and time from incident case detection. Evidence of spillover effects would suggest that reactive, focal interventions hold promise for malaria elimination when scaled up. The absence of spillover effects would suggest that interventions did not interrupt transmission; if so, information about the spatial configuration of infections would inform who and how many people to treat using redesigned interventions. This study will apply novel machine learning-based methods for estimation of causal effects appropriate for infectious disease data. This application proposes a 4-year training plan including mentorship from two leading biostatisticians at UC Berkeley and two malaria epidemiologists at UC San Francisco. Dr. Benjamin-Chung’s training goals are to (1) develop skills in machine learning and causal inference methods for dependent data, (2) learn about malaria biology and epidemiology, and (3) enhance her software development and spatial analysis skills. UC Berkeley is the optimal place for Dr. Benjamin-Chung to advance her career goals because of its leading faculty in biostatistics, strong track record of research and teaching in infectious diseases and biostatistics, and close proximity to UCSF’s Malaria Elimination Initiative.