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，人们知道，大部分传播是通过受感染的乘客传播的，这些乘客乘坐飞机旅行，然后将埃博拉病毒传播到远离感染地点的地方。了解埃博拉病毒是如何在航空旅行中传播的，对于有效应对这种传播至关重要。为了帮助了解这一点，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。研究人员计划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--如医疗响应人员、政策制定者等，以应对航空旅行期间埃博拉病毒的传播。这些模型旨在应用于个人航班层面，这样就可以了解埃博拉病毒在空中的传播情况，并制定程序，限制该航班上每个人的感染风险。在个人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，以分析潜在感染人口通过航空旅行进出和跨越地区时在地理区域之间的传播。这项工作的结果将有助于确定政策和业务解决方案，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能力，可用于应对当前的埃博拉大流行，以及对新的紧急情况做出快速反应。这项提案整合了PI在对人体在平面上的运动进行建模、对感染传播进行建模、为决策支持构建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(由提议团队的一名成员开发的复杂系统框架)进行集成，该框架为分析提供全面的决策支持环境。通过这个项目，此外，CSF·S将把可伸缩性提高到千万亿级机器上，并将开发方法来降低这些模拟的巨大计算成本。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)航空旅行对疾病传播到邻近地区的风险；(B)改用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降低传播的可能性？

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?