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: 8318031
• 负责人: STEPHEN G EUBANK
• 金额: $ 77.49万
• 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/8318031
• 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.

描述我们的假设是，通过整合相关的数学模型构建的合成信息系统可以提供及时，全面的情境意识和行动过程分析，而决策者可以并且将使用这些分析来告知他们对传染病暴发的反应。通过综合信息系统，我们指的是软件工具，这些软件工具可以合成多样化的数据，模型和因果假设，分为特定人群的合理且合理的图片，这些图片以及支持人口统计学和/或地理针对性的干预措施的分析。通过综合，我们的意思是，由于行为，社会学，后勤和经济学以及健康科学，工具包括限制和后果。通过提供和通知，我们的意思是，我们将创建工具，而不是定义研究并发布规范性政策指导，而是允许分析师和其他最终用户自己探索策略和实施选项本身。我们将通过针对流行病学调整我们的合成信息技术来评估这一假设。我们将扩展这些方法，以解决我们在2009年流感大流行中吸引决策者的努力期间所学到的特定教训。具体目的：1。创建一个针对传染病流行病学量身定制的合成信息，为用户提供对任何目标亚种群中暴发和干预措施的健康，社会和财务后果的分布估计。这包括设计和实施定义明确的语言，以灵活地指定爆发和干预场景，复杂的模型和疾病蔓延模型以及分析结果信息的方法。 2。为个人的行为开发与疾病和观点传播相关的综合动力学模型（例如，复杂传染的流行弹性和社会学理论）。 3。比较由隔间和基于个体模型给出的干预措施的排名。比较将在模型之间的特定差异中追踪结果的差异。 4.对流感疫情中的基于社区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，我们将对多角度，多理论，耦合网络动态过程进行正式数学处理，以使其成为流行病学和流行病学建模。