Supplement for Cloud Computing: Alcohol Use Disorder Treatment Simulation

项目摘要/摘要 在酒精使用、酒精相关问题和酒精健康利用方面存在显著差异 在美国跨人口子组的服务。统计建模和统计方法的研究进展 计算能力使开发复杂的模拟模型成为可能 评估政策和卫生服务干预措施对酒精相关结果的潜在影响 这些结果之间的差距。通过预测不同干预措施对人口亚组的影响 随着时间的推移，模拟建模可以帮助识别特定类型的酒精健康服务并确定其优先顺序 最有可能减少差距和促进长期戒酒公平的干预措施 精神障碍(澳元)。正在进行的父母资助，酒精使用障碍治疗模拟：建模 治疗对美国酒精相关差异的影响(R01AA029812-02)，地址 国家酒精滥用和酒精中毒研究所的目标是了解在哪里以及如何增加 在整个保健过程中获得酒精健康服务，以促进健康公平。我们的模拟 模型特别适合于使用云基础设施的增强型计算。我们将该模型应用于一个 地理上明确的模拟人口代表加利福尼亚州和德克萨斯州的人口，我们 随着时间的推移，对主要人群亚群的澳门氏症严重程度和恢复做出长期预测。使用 通过补充资金，我们将通过使用新的云技术来增强现有项目。这将会 通过改进计算能力，促进更真实的模拟条件并实现新的发现 研究的效率。补编的具体目的是(1)建立概念验证 举例并评估能够为消息提供可扩展集群计算的云服务选项 通过基于接口的仿真建模软件；(2)将仿真结果进行计算比较 受限的笔记本电脑运行模式，在可扩展的云计算上运行相同的模式所产生的结果 以及(3)评估建立和运行仿真模型的成本(包括数据上传， 运行时和数据下载)，以便在云解决方案上执行，而不是在 高性能笔记本电脑。收到补充资金将允许创建一个更现实的 模拟，以通过产生更好的保健服务利用率估计数和 冲击力。云服务与非云选项的比较将为 国家卫生研究院关于与这些特定用例相关的成本和战略 无法访问传统超级计算机的机构通过 云。

PROJECT SUMMARY/ABSTRACT Marked disparities are found for alcohol use, alcohol-related problems, and utilization of alcohol health services across demographic subgroups in the United States. Advances in statistical modeling and computing capabilities have enabled the development of sophisticated simulation models helpful for assessing potential impacts of policies and health services interventions on alcohol-related outcomes and on disparities in those outcomes. By projecting effects of different interventions across population subgroups over time, simulation modeling can help identify and prioritize specific types of alcohol health services interventions that may best reduce disparities and promote equity in long-term recovery from alcohol use disorder (AUD). The ongoing parent grant, Alcohol Use Disorder Treatment Simulation: Modeling Treatment Impacts on Alcohol-related Disparities in the United States (R01AA029812-02), addresses the National Institute on Alcohol Abuse and Alcoholism’s goal to understand where and how to increase access to alcohol health services across the continuum of care to promote health equity. Our simulation model is particularly well-suited for enhanced computing using cloud infrastructure. We apply the model to a geographically explicit simulated population representing the populations of California and Texas, and we make long-term projections for AUD severity and recovery for key population subgroups over time. With supplemental funding, we would enhance the existing project by using new cloud technologies. This would facilitate more realistic simulation conditions and enable new discoveries by improving the computational efficiencies of the research. The Specific Aims for the Supplement are to (1) build proof-of-concept examples and evaluate cloud service options capable of providing scalable cluster computing for Message Passing Interface–based simulation modeling software; (2) compare simulation results for a computationally constrained laptop-run model with results from running the same model on a scalable cloud computing cluster; and (3) evaluate the costs for setting up and running the simulation model (including data uploads, run time, and data downloads) for execution on the cloud solutions compared to running the model on a high-performance laptop. Receipt of supplemental funding will permit the creation of a more realistic simulation to enhance the results through the production of better estimates of health services utilization and impact. Comparisons of cloud services with non-cloud options will provide additional information to the National Institutes of Health concerning costs associated with these specific use cases and strategies for institutions without access to traditional supercomputers to access high-performance computing through the cloud.