Dynamic data-driven decision models for infectious disease control

用于传染病控制的动态数据驱动决策模型

基本信息

批准号：
8703900
负责人：
ALISON P GALVANI
金额：
$ 73.09万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-06-01 至 2019-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8703900
关键词：
Address Antibodies Antiviral Agents Awareness Basic Science Behavior Control Behavioral Biological Biosensing Techniques Brazil Budgets Centers for Disease Control and Prevention (U.S.)Child Collaborations Combined Vaccines Communicable Diseases Complex Connecticut Cost Effectiveness Analysis Coupling Culicidae Data Data Sources Decision Making Decision Modeling Dengue Detection Disease Disease Outbreaks Early Diagnosis Economics Ecosystem Electronic Health Record Epidemic Evaluation Funding Future Game Theory Goals Grant Health Health Personnel Health Services Influenza Injection of therapeutic agent Institute of Medicine (U.S.)Interdisciplinary Study Internet Intervention Israel Louisiana Measures Methods Modeling Molecular Monitor Online Systems Pertussis Policies Population Dynamics Population Surveillance Procedures Prophylactic treatment Public Health Rabies Readiness Resources Respiratory syncytial virus Schedule Science Source System Texas Time Translations Uncertainty Vaccines Vector-transmitted infectious disease Ventilator Viral West Nile virus Work authority base data modeling design disease transmission disorder control disorder prevention fight against flu high risk improved infectious disease model innovation insight mathematical model network models next generation sequencing novel novel strategies pandemic influenza pandemic preparedness public health relevance response seasonal influenza social surveillance strategy tool transmission process trend user friendly software user-friendly vector control

项目摘要

DESCRIPTION (provided by applicant): We aim to improve infectious disease surveillance and control through mathematical modeling, optimization, and translational collaborations with public health decision makers. Methodologically, we will advance the application of mathematical modeling to inform public heath policy decisions by (i) integrating large-scale optimization, economic analyses, and uncertainty quantification into mathematical models of disease transmission in complex and dynamic populations, and by (ii) developing goal-oriented optimization methods for integrating diverse data sources to improve infectious disease surveillance systems. We will apply these approaches using data on influenza, respiratory syncytial virus (RSV), pertussis, West Nile virus (WNV), and dengue from around the world to elucidate the complex drivers of outbreaks and control and to identify highly effective, economical, and feasible control policies. We will disseminate our models and results to public health authorities and develop user-friendly modeling tools to facilitate preparedness and real-time decision- making regarding the optimal distribution of limited disease control resources. Thus, our interdisciplinary research will expand the methodological toolkit for modeling infectious disease dynamics, provide better strategies for tracking and mitigating epidemics, and make science, data, and models more broadly accessible to public health agencies engaged in the global fight against infectious diseases.

描述（由申请人提供）：我们旨在通过与公共卫生决策者的数学建模，优化和翻译合作来改善传染病的监视和控制。从方法论上讲，我们将通过（i）通过（i）将大规模优化，经济分析和不确定性量化整合到复杂和动态人群中疾病传播的数学模型中，以及（ii）开发以目标的优化方法来整合感染性疾病的多样化的方法来提高疾病传播的数学模型，以提高数学建模的应用来为公共卫生政策决策提供信息。我们将使用有关流感，呼吸道合胞病毒（RSV），百日咳，西尼罗河病毒（WNV）和来自世界各地的登革热的数据应用这些方法，以阐明疫情和控制的复杂驱动因素，并确定高效，经济，经济，经济，可行的控制政策。我们将向公共卫生当局传播我们的模型和结果，并开发对用户友好的建模工具，以促进有关有限疾病控制资源的最佳分配的准备和实时决策。因此，我们的跨学科研究将扩展用于建模传染病动态的方法学工具包，为追踪和减轻流行病提供更好的策略，并使科学，数据和模型可用于参与全球抗击感染性疾病的公共卫生机构更广泛地使用。