Collaborative Research: Petascale Simulation of Viral Infection Propogation through Air Travel

航空旅行已被确定为流感等感染传播的主要因素，这促使人们呼吁限制航空旅行，以防止疾病爆发的传播。然而，这样的限制会带来相当大的经济和人力成本。因此，有必要评估航空旅行对病毒疾病传播的影响程度，并确定能够在不对航空旅行造成重大干扰的情况下减缓其传播的政策选择。计算机模拟在评估可行的政策选择和探索决策者决策的潜在后果方面发挥着至关重要的作用。Vipra项目的目标是使用Blue Waters Leadance计算系统创建一个大规模并行模拟基础设施，为处理通过航空旅行传播病毒感染的决策者提供有用的见解。这种基础设施将有助于制定新的政策，降低大流行的可能性，为NSF促进我国健康、繁荣和福利的使命做出贡献。Vipra项目使用了一个精细的人类运动模型，可以捕捉详细的动态，因此可以评估程序中的细微变化的影响，这些变化可能会大幅降低通过航空旅行传播流行病的可能性。这种精细的方法随之而来的是计算成本高的瓶颈。在Blue Waters的前期工作中，与埃博拉病毒疾病在飞机上传播有关的计算的主要部分所需的时间已从几个小时减少到大约20分钟，在大约7万个内核上运行。这些优化使得在紧急情况之前分析预期的政策变得容易。然而，在决策会议过程中，潜在新政策的结果需要在几分钟内产生。此外，在机场出现的更复杂情况下，空气传播的病毒疾病将需要更大的计算工作量。这可以使用Blue Waters上的GPU并通过进一步将计算扩展到更大的并行度来处理。这一蓝水分配将使部署优化的决策支持系统成为可能，该系统可以在新的紧急情况的时间限制下分析政策或程序选项。

Air travel has been identified as a leading factor in the spread of infections, such as influenza, and this has motivated calls for limitations on air travel in order to prevent the spread of disease outbreak. However, such limitations carry considerable economic and human costs. Consequently, it is necessary to evaluate the extent of impact of air travel on spread of viral diseases and to also identify policy options that can mitigate its spread without major disruption to air travel. Computer simulations play a crucial role in evaluating viable policy options and exploring potential consequences of decisions taken by policy makers. The goal of Project VIPRA is to use the Blue Waters leadership computing system to create a massively parallel simulation infrastructure that will provide useful insight to decision makers dealing with viral infection propagation through air travel. This infrastructure will enable formulation of new policies that will reduce the likelihood of a pandemic, contributing to NSF's mission to advance the health, prosperity and welfare of our nation.Project VIPRA uses a fine-scale model for human movement, which can capture detailed dynamics, and so can evaluate the impact of subtle changes in procedures that have the potential to bring a substantial reduction in the likelihood of an epidemic spreading through air travel. This fine-scale approach comes with the bottleneck of high computational cost. In preliminary work on Blue Waters, the time required for the primary component of the computation, related to spread of Ebola Virus Disease in an airplane, has been reduced from several hours to around 20 minutes per run on approximately 70,000 cores. These optimizations make it easy to analyze anticipated policies ahead of an emergency. However, in the course of a decision meeting, results for potential new policies need to be produced within a couple of minutes. In addition, air-borne viral diseases, in the more complex situations seen in airports, will require even greater computational effort. This can be handled using GPUs on Blue Waters and by further scaling the computation to greater parallelism. This Blue Waters allocation will enable deployment of an optimized decision support system that can analyze policy or procedural options in the time constraints of a new emergency.