MIDAS Center for Communicable Disease Dynamics

MIDAS 传染病动态中心

• 批准号: 9335878
• 负责人: MARC LIPSITCH
• 金额: $ 225万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9335878
• 关键词: Academia; Accounting; Achievement; Address; Antibiotic Resistance; Biological; Car Phone; Collaborations; Communicable Diseases; Computer Simulation; Data; Data Sources; Development; Discipline; Dose; Drug resistance; Education and Outreach; Emerging Communicable Diseases; Epidemiology; Evolution; Foundations; Funding; Genetic; Genetic Recombination; Genomics; Gonorrhea; Heterogeneity; Infection; Intervention; Journalism; Knowledge; Malaria; Maps; Mathematics; Measures; Mediating; Methodology; Methods; Modeling; Peer Review; Pneumococcal Infections; Policies; Population; Prevalence; Public Health; Public Health Schools; Publications; Readiness; Recruitment Activity; Research; Research Activity; Research Infrastructure; Risk; Route; Severities; Source; Statistical Models; Testing; Time; Training; Tuberculosis; Vaccines; Virus; Work; analytical method; disease transmission; mathematical model; next generation; pathogen; public health emergency; resistance mechanism; response; software development; theories; transmission process

We propose a competing renewal of the MIDAS Center for Communicable Disease Dynamics (CCDD) at Harvard School of Public Health. Building on a four-year record of over 170 peer-reviewed publications and significant achievements in outreach and education, we propose a multipronged approach to extend the activities of this Center of Excellence into the next funding period. Combining expertise in mathematical and statistical modeling of infection with related disciplines, we will undertake methodologic development and application to a range of public health problems. Project I. Pathogen Genomics and Evolution develops computational and statistical models to infer local and global transmission and clustering of infections, antigenic change by recombination, and evolution under vaccine-mediated selection. Project II. New Analytic Methods for New Data Sources develops and tests statistical approaches for interpreting participatory surveillance and mobile phone data for parameterization and testing of transmission-dynamic models, uses hydrological modeling to build risk maps for malaria, and develops the statistical theory of negative controls. Project III. Accounting for Measured and Unmeasured Heterogeneity in Host Populations proposes a variety of studies to assess the contribution of host heterogeneity to model parameter estimation for infections with different transmission routes and creates models incorporrating two key sources of such heterogeneity: virus interference and dose-response relationships. Project IV. Understanding and Controlling Antibiotic Resistance studies drug resistance in gonorrhea, tuberculosis, and pneumococcal infections, applying methods from earlier projects to understand the genetic and selective origins of resistance and the mechanisms underlying observed heterogeneities in its prevalence. Project V. Collaborative Modeling for Assessment and Optimization in Public Health proposes work with public health partners to address two key aspects of their work ~ burden/severity estimation and optimization of interventions - using a range of modeling approaches. Software development and sharing and Policy Studies are integral parts of our research activities. In Outreach and Education we propose an ambitious set of initiatives aimed at pipeline and recruiting, training the next generation of modelers, and engaging colleagues in policy, academia and journalism.

我们提议对 MIDAS 传染病动态中心 (CCDD) 进行竞争性更新，地址： 哈佛大学公共卫生学院。建立在 170 多篇同行评审出版物的四年记录之上， 鉴于在外展和教育方面取得了重大成就，我们建议采取多管齐下的方法来扩大 该卓越中心的活动纳入下一个资助期。结合数学和数学方面的专业知识 与相关学科的感染统计模型，我们将进行方法开发和 应用到一系列公共卫生问题。项目 I. 病原体基因组学和进化发展 推断局部和全球传播以及感染聚集的计算和统计模型， 重组引起的抗原变化以及疫苗介导的选择下的进化。项目二。新分析 新数据源方法开发和测试解释参与性的统计方法 监视和移动电话数据用于传输动态模型的参数化和测试，使用 建立疟疾风险图的水文模型，并发展阴性对照的统计理论。 项目三。考虑东道国人口中可测量和不可测量的异质性提出了多种 评估宿主异质性对感染模型参数估计的贡献的研究 不同的传播途径，并创建了包含这种异质性的两个关键来源的模型：病毒 干扰和剂量反应关系。项目四。了解和控制抗生素 耐药性研究淋病、结核病和肺炎球菌感染的耐药性，应用 早期项目中的方法，用于了解抗性的遗传和选择性起源以及 观察到其患病率异质性的潜在机制。项目 V. 协作建模 公共卫生评估和优化建议与公共卫生合作伙伴合作解决两个关键问题 他们工作的各个方面〜负担/严重性估计和干预措施的优化 - 使用一系列 建模方法。软件开发和共享以及政策研究是我们的有机组成部分 研究活动。在外展和教育中，我们提出了一系列雄心勃勃的举措，旨在管道 招募、培训下一代建模师，并让政策界、学术界和 新闻业。