Genetic and social network analysis to target interventions for malaria elimination

项目摘要/摘要 这项拟议的K01奖项将支持助理助教詹妮弗·史密斯博士的职业发展 加州大学旧金山分校流行病学和生物统计学系教授 (加州大学旧金山分校)史密斯博士的职业目标是成为一名在寄生虫方面拥有综合专业知识的独立研究员 基因分型和人类网络分析，以优化消除传染病的干预措施。至 支持她的职业发展，这项申请提出了一项研究，利用收集的数据作为 目前正在对疟疾高危人群进行研究，并使用新的遗传和社会网络分析来 解决阻碍实现消除疟疾目标的紧迫挑战。随着疟疾的传播 下降，越来越大比例的寄生虫库聚集在特定的亚种群中 感染风险高，在接触和利用疟疾方面往往面临重大障碍 干预措施。虽然像世界卫生组织这样的规范机构建议在 已知的疟疾高危人群，关于这些人群在多大程度上推动疟疾的证据有限 传播、有针对性干预措施的影响或如何优化覆盖面。通过横截面和 对收集的遗传和社会网络数据进行时间分析，作为现有的、单独资助的 在高危人群中对针对性疟疾干预措施的基于人群的评估，本K01建议 调查流动人口和常住人口感染之间的遗传联系及其在社会中的作用 网络在接受疟疾干预措施方面发挥了作用。其具体目的是(1)量化寄生虫的遗传 不同时期流动人口和常住人口内部和之间的连通性和传播潜力 点和空间尺度，(2)评价社会网络属性对疟疾预防的影响 措施，以及(3)建立传输网络模型，并评估替代干预策略的影响 在外来务工人员和常驻农业工人中。这项研究将提供关于疟疾高风险如何 人口有助于传播动态，告知如何利用社交网络来改善 干预吸收，并量化有针对性的干预对总体传播的影响。建议数 研究将建立在史密斯博士流行病学方法的基础上，并包括一项为期5年的培训计划 包括遗传和疟疾流行病学、社会网络分析和 加州大学旧金山分校、南加州大学和加州大学伯克利分校的数学模型。史密斯博士的培训目标 是(1)获得疟疾遗传流行病学知识和遗传数据的应用分析方法，(2) 发展先进的社交网络理论和分析方法方面的专业知识，以及(3)在 数学建模。研究结果将作为R01实施和评估的基础 纳米比亚北部疟疾高危人群的网络干预。

PROJECT SUMMARY/ABSTRACT This proposed K01 award will support the career development of Dr. Jennifer Smith, an Assistant Adjunct Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco (UCSF). Dr. Smith's career goal is to become an independent researcher with combined expertise in parasite genotyping and human network analyses to optimize interventions for infectious disease elimination. To support her career development, this application proposes a study that leverages data collected as part of ongoing research in malaria high-risk populations and uses novel genetic and social network analyses to address an urgent challenge preventing achievement of malaria elimination targets. As malaria transmission declines, an increasingly large proportion of the parasite reservoir is clustered in specific sub-populations with high exposure to infection and who often face significant barriers to accessing and utilizing malaria interventions. While normative bodies like the World Health Organization recommend a targeted response in known malaria high-risk populations, there is limited evidence on the extent to which these populations drive transmission, the impact of targeted interventions or how to optimize coverage. Through cross-sectional and temporal analysis of genetic and social network data collected as part of an existing, separately funded population-based evaluation of targeted malaria interventions in high-risk populations, this K01 proposes to investigate genetic connectivity between infections in migrant and resident populations and the role social networks play in uptake of malaria interventions. The specific aims are to (1) quantify parasite genetic connectivity and transmission potential within and between migrant and resident populations at different time points and spatial scales, (2) evaluate the influence of social network attributes on uptake of malaria prevention measures, and (3) model transmission networks and estimate the impact of alternative intervention strategies in migrant and resident agricultural workers. This study will provide crucial knowledge on how malaria high-risk populations contribute to transmission dynamics, inform how social networks can be leveraged to improve intervention uptake, and quantify the impact of targeted interventions on overall transmission. The proposed research will build on Dr. Smith's foundation in epidemiologic methods and include a 5-year training plan including mentorship from leaders in genetic and malaria epidemiology, social network analysis and mathematical modelling at UCSF, University of Southern California and UC Berkeley. Dr. Smith's training goals are to (1) gain knowledge in malaria genetic epidemiology and applied analytic approaches for genetic data, (2) develop expertise in advanced social network theory and analytic methods, and (3) obtain training in mathematical modelling. The findings will be used as a foundation for an R01 to implement and evaluate network-based interventions among malaria high-risk populations in northern Namibia.