Synthetic Information Systems for Better Informing Public Health Policymakers

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方案分析，决策者可以并将使用这些分析来告知他们对传染病爆发的反应。通过综合信息系统，我们指的是将各种各样的、看似不相称的数据、模型和因果假设综合成特定人口和地区的可信和合理的图像的软件工具，这些图像支持对人口和/或地理上有针对性的干预措施的分析。通过全面，我们的意思是工具包括约束和后果由于行为，社会学，物流，经济学以及健康科学。通过提供和通知，我们的意思是，我们将创建工具，允许分析人员和其他最终用户自己探索政策和实施方案，而不是自己定义研究和发布说明性政策指导。我们将通过裁剪流行病学来评估这一假设，我们在各种决策信息学背景下开发的综合信息技术。我们将扩展这些方法，以处理我们在2009年流感大流行中促使决策者参与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综合信息集，向用户提供疫情和干预措施在任何目标亚人群中对健康、社会和财务后果的分布估计。这包括设计和实施一种定义良好的语言，以便灵活地指定爆发和干预情景、复杂的疾病传播模型和模拟，以及分析所产生信息的方法。2. 开发与疾病和观点传播相关的个体行为的综合动力学模型（例如流行弹性和复杂传染的社会学理论）。3. 比较部门模型和个人模型给出的干预措施排名。这种比较将把结果的差异归结为模型之间的具体差异。4. 对流感疫情中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，我们将在流行病学和流行病学建模中引入多视角，多理论，耦合网络动态过程的正式数学处理。

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.