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

当埃博拉病毒广泛传播时，众所周知，大部分传播是通过乘飞机旅行的受感染乘客，然后将埃博拉病毒传播到远离感染地点的地方。了解埃博拉病毒如何在航空旅行中传播对于有效应对这类传播至关重要。为了帮助理解这一点，该项目的研究人员将建立航空旅行期间感染传播的精细模拟模型，以确定感染传播的原因，以及在不严重干扰航空旅行的情况下限制其传播的程序和政策。研究人员提议创建的基于超级计算的应用程序将为决策者提供有用的见解，例如医疗响应者、政策制定者等，以应对埃博拉病毒在航空旅行中的传播。这些模型旨在应用于单个航班的层面，这样就可以在特定航班的空中了解埃博拉病毒的传播情况，并制定程序，限制该航班上所有人的感染风险。在个别航班层面运行的航空运输模型将与系统地理模型（捕捉导致现有个人地理分布的历史过程）相结合，以便在潜在受感染人群通过航空旅行进出区域和跨区域运输时，分析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操作解决方案，以减少大流行的风险，并在现成的基础设施上建立可扩展的软件能力，可用于应对当前的埃博拉大流行，并对新的紧急情况作出快速反应。该建议整合了pi在平面上模拟人体运动、模拟感染传播、构建决策支持软件基础设施和大规模并行计算方面的综合专业知识。这些模型将使用复杂系统框架（CSF，由提议团队的一名成员开发）进行集成，该框架为分析提供了一个全面的决策支持环境。通过这个项目，此外，CSF？S的可扩展性将被增强到千兆级机器，并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决策者提供有价值的见解。特别是，可以提出和回答关于飞机旅行的细微问题，例如：(a)航空旅行对疾病向邻近地区传播的风险(b)更换另一种类型的飞机是否会降低传播风险？(c)某些座位安排或登机和下机程序的改变是否会减少传播的可能性？

When Ebola has spread widely, it is known that much of the spread is through infected passengers that travel by air, to then spread Ebola far afield from the site of their infection. Understanding how Ebola spreads during air travel is critical in effectively dealing with this type of spreading. To help gain this understanding, 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 super-computing based applications that researchers propose to create will provide useful insight to decision makers - such as medical responders, policy makers etc. dealing with Ebola spread during air travel. The models are intended to be applied at the level of individual flights, so that the spread of Ebola can be understood for a particular flight while it is in the air, and procedures developed that will limit the infection risk for everyone on 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?