Geographic origins and dispersal of drug-resistant M. tuberculosis in South Africa: Advancing strategies for early detection

南非耐药结核分枝杆菌的地理起源和传播：推进早期检测策略

• 批准号: 10523608
• 负责人: Tyler Steven Brown
• 金额: $ 19.84万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-02 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10523608
• 关键词: Address; Award; Bayesian Analysis; COVID-19; Car Phone; Cessation of life; Cities; Collection; Communicable Diseases; Computer Models; Containment; Data; Data Sources; Detection; Development; Disease; Disease Outbreaks; Drug resistance; Drug resistance in tuberculosis; Drug resistant Mycobacteria Tuberculosis; Early Diagnosis; Early identification; Environment; Epidemic; Epidemiologist; Epidemiology; Evaluation; Evolution; Feedback; Funding; Future; Genetic; Genetic Models; Genetic Variation; Geographic Locations; Geography; Goals; HIV; Health Services Accessibility; Household; Human; Individual; Infection; Infectious Disease Epidemiology; Link; Location; Malaria; Maps; Mentors; Mentorship; Methods; Migrant; Modeling; Movement; Multiple drug resistant Mycobacteria Tuberculosis; Mycobacterium tuberculosis; Pattern; Pharmaceutical Preparations; Pharmacoepidemiology; Physicians; Population; Population Genetics; Predisposition; Process; Public Health; Public Health Schools; Research; Research Activity; Rural; Sampling; Science; Scientist; South Africa; Specific qualifier value; Surveys; Testing; Training; Training Activity; Tuberculosis; Uncertainty; United States National Institutes of Health; Widespread Disease; Work; antimicrobial resistant infection; base; career; career development; co-infection; experience; genetic signature; genomic data; genomic epidemiology; health care delivery; high risk; improved; infectious disease model; innovation; insight; interdisciplinary approach; mathematical model; microbial; microbial genomics; multiple data sources; novel; novel strategies; novel therapeutics; prevent; public health relevance; public health research; resistant strain; rural area; rural-urban migration; skills; spatial epidemiology; surveillance strategy; transmission process; urban area

PROJECT SUMMARY Background: South Africa is a unique, high-risk environment for the emergence of novel drug-resistant strains of Mycobacterium tuberculosis (Mtb). Novel drug-resistant Mtb strains are often identified only after they have achieved sustained transmission and dispersed into larger geographic areas away from their origin location. Strategies for early detection of drug-resistant Mtb strains, at the critical juncture between outbreak and epidemic, are lacking. Candidate: I am infectious disease physician and epidemiologist, with experience in genomic epidemiology, mathematical modeling, and field-level public health research and health service delivery. My research employs multiple data sources (including geolocated microbial genomic data and mobile phone-associated movement data) to understand how epidemics emerge and spread. The overarching objective of my work is to improve and innovate strategies for surveillance and containment of drug-resistant infections, focused on malaria and tuberculosis. Training: I am seeking career development support to pursue my long- term goal of becoming an independent NIH-funded physician scientist. To achieve my research and career goals, I will pursue additional training in Bayesian statistics, spatial population genetics, network science, and research management skills. Mentors and Environment: Dr. Caroline Buckee, a global leader in infectious disease epidemiology and mobility-informed infectious disease modeling, will oversee my research and training activities as primary mentor. Dr. Barun Mathema, a scientific expert in the tuberculosis genomic epidemiology and tuberculosis transmission, will provide focused scientific support as my co-mentors. Key collaborators on this project include Dr. Shaheed Vally Omar (surveillance for drug-resistant TB in South Africa), Dr. Ted Cohen (mathematical modeling and epidemiology of tuberculosis), Dr. Sheetal Silal (agent-based modeling of infectious disease dynamics), and Dr. Sarita Shah (TB surveillance and control). Dr. Marc Lipsitch, Dr. Yonatan Grad, Dr. Louise Ivers, Dr. Neil Schluger, and Dr. Frank Tanser will form a Scientific Advisory Board, who will provide annual feedback on my research and training activities. I will train and conduct research activities during the K08 award at the Harvard T.H. Chan School of Public Health Center for Communicable Disease Dynamics. Research: I will examine novel strategies for early detection of drug-resistant Mtb in South Africa through research activities under three specific aims: (1) Using geolocated Mtb genomic data to estimate likely origin locations and transmission hubs for multiple drug-resistant Mtb strains currently circulating in South Africa; (2) Developing an individual-based computational model of Mtb transmission in South Africa incorporating human mobility data and Mtb within-host evolution; (3) Evaluating multiple candidate surveillance strategies for early detection and containment of novel drug-resistant strains of Mtb using individual-based modeling.

项目概要 背景：南非是新型耐药菌株出现的独特高风险环境 结核分枝杆菌（Mtb）。新的耐药 Mtb 菌株通常只有在获得耐药性后才能被识别。 实现了持续传播并分散到远离其起源位置的更大地理区域。 在爆发和爆发之间的关键时刻及早发现耐药结核分枝杆菌菌株的策略 疫情，缺乏。候选人：我是传染病医师和流行病学家，具有以下方面的经验： 基因组流行病学、数学模型和现场公共卫生研究和卫生服务 送货。我的研究采用了多个数据源（包括地理定位的微生物基因组数据和移动数据） 电话相关的移动数据）来了解流行病是如何出现和传播的。总体目标 我的工作是改进和创新耐药感染的监测和遏制策略， 重点关注疟疾和结核病。培训：我正在寻求职业发展支持以追求我的长期目标 成为一名由 NIH 资助的独立医师科学家的长期目标。为了实现我的研究和职业目标， 我将接受贝叶斯统计、空间群体遗传学、网络科学和研究方面的额外培训 管理技能。导师与环境：传染病领域的全球领导者 Caroline Buckee 博士 流行病学和流动性知情的传染病模型，将监督我的研究和培训活动 作为主要导师。 Barun Mathema 博士，结核病基因组流行病学科学专家 结核病传播，将作为我的共同导师提供有针对性的科学支持。这方面的主要合作者 项目包括 Shaheed Vally Omar 博士（南非耐药结核病监测）、Ted Cohen 博士 （结核病的数学模型和流行病学），Sheetal Silal 博士（基于代理的传染性模型） 疾病动态）和 Sarita Shah 博士（结核病监测和控制）。 Marc Lipsitch 博士、Yonatan Grad 博士、 Louise Ivers、Neil Schluger 博士和 Frank Tanser 博士将组成一个科学顾问委员会，该委员会将提供 对我的研究和培训活动的年度反馈。我将在K08期间进行培训和研究活动 哈佛大学 T.H.陈公共卫生学院传染病动态中心。 研究：我将研究在南非早期检测耐药结核分枝杆菌的新策略 根据三个具体目标开展的研究活动：(1) 使用地理定位的结核分枝杆菌基因组数据来估计可能的起源 目前在南非流行的多种耐药结核菌菌株的地点和传播中心； (2) 开发南非 Mtb 传播的基于个体的计算模型，其中包含人类 移动数据和 Mtb 主机内演化； (3) 评估早期的多种候选监测策略 使用基于个体的模型检测和遏制结核分枝杆菌的新型耐药菌株。