Synthetic Information Systems for Better Informing Public Health Policymakers

描述我们的假设是，通过整合相关数学模型建立的综合信息系统可以提供及时、全面的形势感知和行动过程分析，政策制定者可以并将使用这些信息来为他们对传染病暴发的反应提供信息。我们所说的综合信息系统是指软件工具，它将各种看似不相称的数据、模型和因果假设综合成可信和合理的特定人口和地区的图景，以支持对人口和/或地理目标干预措施的分析。我们所说的综合，是指这些工具包括行为、社会学、物流、经济学以及健康科学的限制和后果。通过提供和通知，我们的意思是，我们将创建允许分析师和其他最终用户自己探索政策和实施选项的工具，而不是自己定义研究和发布说明性的政策指导。我们将通过调整我们在各种决策信息学背景下开发的合成信息技术的流行病学来评估这一假设。我们将推广这些方法，以处理我们在努力让决策者参与2009年流感大流行期间吸取的具体经验教训。具体目标：1.创建为传染病流行病学量身定做的综合信息集，为用户提供对任何目标亚人群爆发和干预的健康、社会和财务后果的分布估计。这包括设计和实施一种定义明确的语言，以灵活地指定暴发和干预情景、复杂的疾病传播模型和模拟，以及分析产生的信息的方法。2.建立与疾病传播和观点相关的个人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.比较隔室模型和基于个体的模型给出的干预措施的排名。比较将追溯结果的差异，以确定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进行全面调查。在实现这些目标的过程中，我们将把多视角、多理论、耦合网络动态过程的正式数学处理引入流行病学和流行病学建模。

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.