Modeling disease dynamics on large, detailed, co-evolving networks

在大型、详细、共同进化的网络上对疾病动态进行建模

• 批准号: 7914448
• 负责人: STEPHEN G EUBANK
• 金额: $ 78.66万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7914448
• 关键词: Address; Algorithms; Area; Behavior; Communicable Diseases; Communication; Complex; Data; Detection; Disease; Disease model; Drug Formulations; Elements; Goals; Health Policy; Health Resources; Intervention; Modeling; Network-based; Outcome; Policy Maker; Public Health; Research; Structure; Suggestion; Testing; base; combat; design; improved; infectious disease model; insight; model development; novel; public health relevance; response; simulation; tool; verification and validation

DESCRIPTION (provided by applicant): Models of infectious disease can inform public health policy decisions. The long-term goal of this research is to create network-based models of disease that become useful tools for public health policy-makers. To this end, we propose research tasks in the following areas: surveillance and detection; dynamics of infectious diseases at several spatio-temporal scales; response strategies; behavior; conceptual development of models; model verification and validation; and dissemination. These tasks will test the hypotheses that: (a) the detailed structure of interaction networks and the effect of disease on that structure have important consequences for public health; and (b) models based on complex, dynamic networks of interacting elements can overcome several significant hurdles to their acceptance and routine use by public health policy-makers. Novel algorithms for efficient simulation and characterization of large (100 million node) networks, along with insight gained from interactions with policy makers, will be used to accomplish the following specific aims: (1) improve our understanding of network-based models of disease; (2) compare models to each other and to available data; (3) extend existing models to new domains; (4) extend the range of questions to which models are applied; (5) improve communications about model outcomes. Each of these aims addresses one of the common hurdles encountered in applying modeling results to public health policy. Public Health Relevance: This research will create network-based models of infectious disease that become useful tools for public health policy-makers. These models will guide the design of targeted interventions that optimize allocation of public health resources to combat the spread of infectious disease.

描述（由申请人提供）：传染病模型可以为公共卫生政策决策提供信息。这项研究的长期目标是创建基于网络的疾病模型，成为公共卫生政策制定者的有用工具。为此，我们提出了以下领域的研究任务：监测和检测;在几个时空尺度上的传染病动态;响应策略;行为;模型的概念开发;模型验证和确认;以及传播。这些任务将检验以下假设：（a）相互作用网络的详细结构和疾病对该结构的影响对公共卫生具有重要影响;（B）基于相互作用要素的复杂动态网络的模型可以克服公共卫生决策者接受和日常使用的几个重大障碍。高效模拟和表征大规模核反应堆的新算法（1亿个节点）网络，沿着从与决策者的互动中获得的洞察力，将用于实现以下具体目标：（1）提高我们对基于网络的疾病模型的理解;（2）将模型相互比较并与现有数据进行比较;（3）将现有模型扩展到新的领域;（4）扩大模型适用的问题范围;（5）改善关于模型结果的沟通。这些目标中的每一个都解决了将建模结果应用于公共卫生政策时遇到的常见障碍之一。 公共卫生相关性：这项研究将创建基于网络的传染病模型，成为公共卫生政策制定者的有用工具。这些模型将指导设计有针对性的干预措施，优化公共卫生资源的分配，以遏制传染病的传播。