Synthetic Information Systems for Better Informing Public Health Policymakers

综合信息系统为公共卫生决策者提供更好的信息

• 批准号: 8321110
• 负责人: STEPHEN G EUBANK
• 金额: $ 12.01万
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8321110
• 关键词: Address; Adherence; Adverse effects; Age; Awareness; Behavior; Behavioral; Communicable Diseases; Communities; Complex; Containment; Coupled; Decision Making; Demographic Analyses; Detection; Disease; Disease Outbreaks; Economics; Elasticity; Epidemiology; Event; Goals; Health; Health Policy; Health Sciences; Immunity; Individual; Infectious Disease Epidemiology; Influenza A Virus, H1N1 Subtype; Informatics; Information Systems; Information Technology; Intervention; Investigation; Language; Learning; Lifting; Logistics; Methods; Modeling; Outcome; Pharmacologic Substance; Pharmacotherapy; Policies; Policy Maker; Population; Prevalence; Process; Public Health; Publishing; Randomized Controlled Trials; Research; Safety; Schools; Severity of illness; Sociology; Software Tools; Specific qualifier value; System; Time; Ursidae Family; Work; analog; base; contagion; data modeling; design; economic impact; epidemiological model; evidence base; flexibility; improved; influenza outbreak; mathematical model; mathematical theory; models and simulation; pandemic disease; pandemic influenza; phase 3 study; prevent; response; simulation; social; surveillance data; theories; tool

DESCRIPTION Our hypothesis is that synthetic information systems built by integrating relevant mathematical models can provide timely, comprehensive situational awareness and course-of-action analysis that policymakers can and will use to inform their response to infectious disease outbreaks. By synthetic information systems we mean software tools that synthesize diverse, seemingly incommensurate data, models, and causal hypotheses into plausible and justifiable pictures of a specific population and locality that support analysis of demographically and/or geographically targeted interventions. By comprehensive, we mean the tools include constraints and consequences due to behavior, sociology, logistics, and economics as well as health sciences. By provide and inform we mean that, rather than define studies and publish prescriptive policy guidance ourselves, we will create tools that allow analysts and other end users to explore policy and implementation options themselves. We will evaluate this hypothesis by tailoring to epidemiology our synthetic information technologies developed in a variety of decision-informatics contexts. We will extend these methods to address specific lessons learned during our efforts to engage policymakers in the 2009 influenza pandemic. Specific aims: 1. Create a synthetic information set tailored to infectious disease epidemiology that provides users distributional estimates of the health, social, and financial consequences of outbreaks and interventions in any target subpopulation. This includes designing and implementing a well-defined language for specifying outbreak and intervention scenarios flexibly, sophisticated models and simulations of disease spread, and methods for analyzing the resulting information. 2. Develop integrated dynamical models for individuals' behaviors relevant to the spread of disease and opinions (e.g. prevalence elasticity and sociological theories of complex contagion). 3. Compare the rankings of interventions given by compartmental and individual-based models. The comparison will trace differences in outcomes to specific differences between the models. 4. Conduct a comprehensive investigation of community-based, non-pharmaceutical interventions in an influenza outbreak. In the course of achieving these aims, we will introduce a formal mathematical treatment of multi- perspective, multi-theory, coupled network dynamical processes into epidemiology and epidemiological modeling.

我们的假设是，通过整合相关数学模型构建的综合信息系统可以提供及时，全面的态势感知和行动过程分析，决策者可以并将用于通知他们对传染病爆发的反应。通过合成信息系统，我们的意思是软件工具，合成不同的，似乎不相称的数据，模型和因果关系的假设，以合理和合理的图片，一个特定的人口和地点，支持分析人口和/或地理目标的干预措施。全面的，我们的意思是工具包括由于行为，社会学，物流和经济学以及健康科学的约束和后果。我们所说的“提供和通报”是指，我们将创建工具，使分析人员和其他最终用户能够自己探索政策和实施方案，而不是自己定义研究和发布规范性的政策指导。我们将评估这一假设定制流行病学我们的合成信息技术在各种决策信息学的背景下发展。我们将扩大这些方法，以处理我们在2009年流感大流行中与决策者接触的努力中吸取的具体经验教训。具体目标：1.创建针对传染病流行病学的合成信息集，为用户提供任何目标亚群中爆发和干预措施的健康，社会和经济后果的分布估计。这包括设计和实施一种定义良好的语言，用于灵活地指定爆发和干预方案，复杂的疾病传播模型和模拟，以及分析结果信息的方法。 2.为与疾病传播和观点相关的个人行为开发综合动力学模型（例如，流行弹性和复杂传染的社会学理论）。 3.比较房室模型和基于个体的模型给出的干预措施的排名。这种比较将把结果的差异追溯到模型之间的具体差异。 4.对流感暴发中以社区为基础的非药物干预措施进行全面调查。在实现这些目标的过程中，我们将引入一个正式的数学处理的多视角，多理论，耦合网络动力学过程的流行病学和流行病学建模。