支持实时 HPC 流行病学的新型多集群工作流系统:调查疫苗接受度对 COVID-19 传播的影响

IF 3.4 3区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Parantapa Bhattacharya , Dustin Machi , Jiangzhuo Chen , Stefan Hoops , Bryan Lewis , Henning Mortveit , Srinivasan Venkatramanan , Mandy L. Wilson , Achla Marathe , Przemyslaw Porebski , Brian Klahn , Joseph Outten , Anil Vullikanti , Dawen Xie , Abhijin Adiga , Shawn Brown , Christopher Barrett , Madhav Marathe
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引用次数: 0

摘要

我们介绍的 MacKenzie 是一个高性能计算驱动的多集群工作流系统,在 COVID-19 大流行期间被反复用于配置和执行细粒度的美国国家级流行病模拟模型。在 COVID-19 大流行期间,Mackenzie 为联邦和弗吉尼亚州的政策制定者提供了大量 "假设 "情景的实时支持,并在 COVID-19 向疾病流行阶段过渡时继续用于回答相关问题。MacKenzie 是一种新颖的高性能计算元调度程序,可执行美国规模的仿真模型和相关工作流,这些模型和工作流通常会带来巨大的大数据挑战。元调度程序优化了工作流中仿真的总执行时间,有助于提高人类的整体工作效率。作为使用 MacKenzie 进行研究的一个范例,我们介绍了一项建模研究,旨在了解接受疫苗对控制 COVID-19 在美国传播的影响。我们使用了一个 2.88 亿节点的合成社会接触网络(数字孪生),该网络覆盖美国 50 个州和华盛顿特区,由 3300 个县组成,每天有 120 亿次互动。用于流行病模拟的基于代理的高分辨率模型使用了有关疾病进展、疫苗吸收、生产计划、接受趋势、流行率和社会距离准则的现实信息。计算实验表明,对于上述模拟工作量,MacKenzie 能够很好地扩展到 10K CPU 内核。我们的建模结果表明,与更快和更加速的疫苗接种相比,由于疫苗接种犹豫而导致的疫苗接种率降低会使全美避免的感染人数从 670 万降至 450 万,避免的死亡总人数从 3940 万降至 2820 万。尽管两种方案的最终疫苗覆盖率相同,但还是出现了这种情况。我们还发现,如果各州的疫苗接种率都能提高 10%,那么全美可避免的感染人数将从 450 万增加到 470 万(提高 4.4%),可避免的死亡总人数将从 2.82 万增加到 2.99 万(提高 6%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-if” scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity.

As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10 K CPU cores.

Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4 K to 28.2 K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2 K to 29.9 K (a 6% improvement) nationwide.

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来源期刊
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing 工程技术-计算机:理论方法
CiteScore
10.30
自引率
2.60%
发文量
172
审稿时长
12 months
期刊介绍: This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing. The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.
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