Collaborative Research: Simulation-Based Policy Analysis for Reducing Ebola Transmission Risk in Air Travel

合作研究：基于模拟的政策分析，降低航空旅行中的埃博拉传播风险

• 批准号: 1525012
• 负责人: Matthew Scotch
• 金额: $ 10万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525012&HistoricalAwards=false
• 关键词: Collaborative; Research; Simulation; Based; Policy

The researchers on this project will build fine-scale simulation models for infection spread during air travel in order to identify the causes of infection spread, as well as procedures and policies that will limit its spread without major disruption to air travel. The researchers propose to create a super-computing based application that will provide useful insight to decision makers - such as medical responders, policy makers etc. dealing with Ebola. The models can be applied at the level of individual flights; thus not only can the spread of Ebola be understood for a particular flight, but also procedures can be developed that will limit the infection risk for that flight.An air-transport model, which operates at the level of individual flights, will be integrated with a phylogeographic model (that captures the historical processes responsible for the existing geographic distributions of individuals) in order to analyze the spread of infection between geographic regions when potentially infected populations are being transported into, out of and across regions, by air travel. The result of this work will help identify policy as well as operational solutions to reduce the risk of a pandemic, and also set up scalable software capabilities on a readily available infrastructure that can be used to address the current Ebola pandemic, as well as respond quickly to new emergencies. This proposal integrates the PIs? combined expertise in modeling human movement in planes, modeling the spread of infections, building a software infrastructure for decision support, and large-scale parallel computing. The models will be integrated using the Complex Systems framework (CSF, developed by a member of the proposing team) that provides a comprehensive decision support environment for analysis. Through this project, and in addition, CSF?s scalability will be enhanced to petascale machines, and methods will be developed to reduce the large computational cost of these simulations. Results of this work will provide valuable insight to policy makers. In particular, fine grain questions regarding plane travel can be asked and answered, such as: (a) the risk air travel has on disease spread to neighboring regions (b) can a change to another type of a plane lead to reduced risk of transmission? (c) Do certain seating arrangement or changes to boarding and disembarkation processes reduce the likelihood of transmission?

该项目的研究人员将为航空旅行期间的感染传播建立精细规模的模拟模型，以确定感染传播的原因，以及在不对航空旅行造成重大干扰的情况下限制其传播的程序和政策。研究人员建议创建一个基于超级计算的应用程序，为决策者提供有用的见解，例如处理埃博拉病毒的医疗响应人员、政策制定者等。该模型可以应用于单个航班的层面；因此，不仅可以了解特定航班的埃博拉病毒传播情况，还可以制定限制该航班感染风险的程序。在单个航班层面运行的航空运输模型将与系统发育地理学模型（捕捉造成个体现有地理分布的历史过程），以便分析当潜在感染人群通过航空旅行进、出和跨区域时，地理区域之间的感染传播。这项工作的结果将有助于确定政策和运营解决方案，以降低大流行的风险，并在现成的基础设施上建立可扩展的软件功能，可用于应对当前的埃博拉大流行，并快速做出反应到新的紧急情况。该提案整合了 PI？结合了飞机上人体运动建模、感染传播建模、构建决策支持软件基础设施以及大规模并行计算方面的专业知识。这些模型将使用复杂系统框架（CSF，由提议团队的成员开发）进行集成，该框架为分析提供了全面的决策支持环境。此外，通过该项目，CSF 的可扩展性将增强至千万亿级机器，并且将开发方法来减少这些模拟的大量计算成本。这项工作的结果将为政策制定者提供宝贵的见解。特别是，可以询问和回答有关飞机旅行的细粒度问题，例如：(a) 航空旅行对疾病传播到邻近地区的风险 (b) 更换为另一种类型的飞机是否可以降低传播风险？ (c) 某些座位安排或登机和下机流程的改变是否会降低传播的可能性？