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.

项目摘要/摘要 拟议的K01奖将支持助理兼职詹妮弗·史密斯博士的职业发展 加州大学旧金山分校流行病学与生物统计学系教授 （UCSF）。史密斯博士的职业目标是成为具有寄生虫联合专业知识的独立研究人员 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 支持她的职业发展，该申请提出了一项研究，该研究利用了作为一部分收集的数据 正在进行的对疟疾高风险人群的研究，并使用新颖的遗传和社交网络分析来 应对阻止实现疟疾消除靶标的紧急挑战。作为疟疾传播 下降，越来越大的寄生虫储层聚集在特定的亚人群中 高暴露于感染，他们经常面临疟疾的重大障碍 干预措施。虽然像世界卫生组织这样的规范机构建议在 已知的疟疾高风险人群，这些人群驱动的程度有限的证据有限 传输，目标干预措施的影响或如何优化覆盖范围。通过横截面和 作为现有，单独资助的一部分收集的遗传和社交网络数据的时间分析 基于人群的高风险人群中有针对性疟疾干预措施的评估，该K01提议 调查移民和居民人口中感染之间的遗传连通性以及社会角色 网络在吸收疟疾干预措施中发挥作用。具体目的是（1）量化寄生虫遗传 在不同时间，移民和居民之间的连通性和传输潜力 点和空间尺度，（2）评估社交网络属性对预防疟疾摄取的影响 措施和（3）模型传输网络并估算替代干预策略的影响 在移民和居民农业工作者中。这项研究将提供有关疟疾如何高风险的重要知识 人口有助于传播动态，告知如何利用社交网络以改善 干预吸收，并量化目标干预措施对整体传输的影响。提议 研究将以史密斯博士的流行病学方法为基础，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领导者的指导，社交网络分析和 南加州大学和加州大学伯克利分校的UCSF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和应用遗传数据的分析方法的知识，（2） 开发高级社交网络理论和分析方法的专业知识，（3）获得培训 数学建模。这些发现将用作R01实施和评估的基础 纳米比亚北部疟疾高风险人群的基于网络的干预措施。