Genetic and social network analysis to target interventions for malaria elimination

遗传和社会网络分析以制定消除疟疾的干预措施

• 批准号: 10646229
• 负责人: Jennifer Linnea Smith
• 金额: $ 14.49万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-22 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646229
• 关键词: Achievement; Address; Agricultural Workers; Biometry; California; Characteristics; Communicable Diseases; Communities; Country; Cross-Sectional Studies; Culicidae; Data; Disease Clusterings; Epidemiologic Methods; Epidemiology; Evaluation; Exposure to; Farm; Foundations; Funding; Genetic; Genotype; Geography; Goals; Health; Health Service Area; Health Services Accessibility; Heterogeneity; Human; Income; Individual; Infection; Influentials; Intervention; Knowledge; Malaria; Malaria prevention; Measures; Mentored Research Scientist Development Award; Mentors; Mentorship; Migrant; Modeling; Namibia; Network-based; Parasites; Pathway Analysis; Pattern; Persons; Play; Population; Positioning Attribute; Prevention Measures; Recommendation; Research; Research Personnel; Resolution; Risk; Role; San Francisco; Seasons; Social Network; Stigmatization; Surveys; Testing; Time; Training; Travel; Universities; Variant; World Health Organization; analytical method; career; career development; cost; design; epidemiologic data; genetic analysis; genetic approach; genetic epidemiology; high risk; high risk population; improved; infection risk; infectious disease model; intervention delivery; malaria transmission; mathematical model; novel; peer; population based; prevent; professor; response; scale up; skills; social; social influence; spatiotemporal; theories; transmission process; uptake

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.

项目概要/摘要 拟议的 K01 奖项将支持助理助理 Jennifer Smith 博士的职业发展 加州大学旧金山分校流行病学与生物统计学系教授 （加州大学旧金山分校）。史密斯博士的职业目标是成为一名具有寄生虫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。到 为了支持她的职业发展，该应用程序提出了一项研究，该研究利用了收集的数据 正在进行的针对疟疾高危人群的研究，并使用新颖的遗传和社交网络分析来 应对阻碍实现消除疟疾目标的紧迫挑战。作为疟疾传播 下降，越来越多的寄生虫储存库聚集在特定的亚种群中 感染率高且在获取和利用疟疾方面经常面临重大障碍的人 干预措施。虽然世界卫生组织等规范机构建议采取有针对性的应对措施 已知的疟疾高危人群，关于这些人群的驱动程度的证据有限 传播、有针对性的干预措施的影响或如何优化覆盖范围。通过横截面和 对作为现有的单独资助的一部分收集的遗传和社交网络数据进行时间分析 对高危人群的针对性疟疾干预措施进行基于人群的评估，本 K01 建议 调查流动人口和常住人口感染之间的遗传关联性以及社会影响 网络在疟疾干预措施的采用中发挥着作用。具体目标是（1）量化寄生虫遗传 不同时间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）评估社交网络属性对疟疾预防的影响 措施，以及 (3) 对传输网络进行建模并估计替代干预策略的影响 农民工和常住农业工人。这项研究将提供关于疟疾高风险如何传播的重要知识。 人口对传播动态做出贡献，告知如何利用社交网络来改善 干预措施的采用情况，并量化有针对性的干预措施对总体传播的影响。拟议的 研究将建立在史密斯博士的流行病学方法基础上，并包括一个为期 5 年的培训计划 包括来自遗传和疟疾流行病学、社交网络分析和 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建模。史密斯博士的培训目标 (1) 获得疟疾遗传流行病学知识和遗传数据应用分析方法，(2) 发展高级社交网络理论和分析方法的专业知识，以及（3）获得以下方面的培训： 数学建模。研究结果将作为 R01 实施和评估的基础 对纳米比亚北部疟疾高危人群实施基于网络的干预措施。