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，对于有效应对这种传播至关重要。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，以及限制其传播而不会对航空旅行造成重大干扰的程序和政策。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，在航空旅行中处理埃博拉病毒传播。这些模型旨在应用于单个航班，以便了解特定航班在空中的埃博拉病毒传播情况，并制定程序，限制该航班上每个人的感染风险。将与地理模型相结合，（它捕捉了对现有个人地理分布负责的历史过程）以便分析可能受感染的人群通过航空旅行被运送到区域内外和跨区域时，地理区域之间的感染传播情况。这项工作的结果将有助于确定政策和业务解决方案，以减少大流行病的风险，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病，并迅速应对新的紧急情况。该提案整合了PI在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和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，由提议小组的一名成员开发）进行整合，该框架为分析提供了全面的决策支持环境。通过这个项目，此外，CSF？的可扩展性将增强到千万亿次机器，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宝贵的见解。特别是，可以提出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）更换另一种类型的飞机是否会导致降低传播风险？(c)某些座位安排或登机和下机程序的改变是否降低了传播的可能性？

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?