Augmented mapping of the Extreme Heat and Cold Events (EHE/ECE) at continental scale with cloud-based computing

利用基于云的计算对大陆范围内的极热和极冷事件 (EHE/ECE) 进行增强测绘

• 批准号: 10826885
• 负责人: Francesca Dominici
• 金额: $ 23.02万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10826885
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Area; Cardiovascular Diseases; Censuses; Climate; Cloud Computing; Communities; Complex; Computing Methodologies; Data; Databases; Dedications; Development; Event; Exposure to; Geography; Health; High Performance Computing; Hybrids; Infrastructure; Link; Long-Term Effects; Maps; Memory; Mental disorders; Methodology; Methods; Modeling; Monitor; Outcome; Patients; Performance; Population; Reproducibility; Research; Resources; Respiratory Disease; Services; Specific qualifier value; Temperature; Testing; Time; United States; Weather; climate data; cluster computing; cohort; computing resources; data pipeline; extreme heat; extreme weather; improved; parallel computer; parent grant; patient population; scale up; temporal measurement; tool; weather stations

Project Summary/Narrative Extreme heat and cold events (EHE/ECE) have been linked to a range of adverse health outcomes from exacerbated pre-existing conditions to mental illness and respiratory and cardiovascular disease Previous research has often determined the areas and population impacted by EHE/ECE through simplistic methods that assign temperature data from the closest weather station to the population being studied (e.g., a census tract or postal zipcode). Our preliminary analysis has demonstrated that dynamic spatial-temporal methodologies significantly alleviate misclassifications that tend to occur in conventional approaches. Implementing more sophisticated models with higher spatial and temporal resolution can pose computational complexity which hinders application and scalability of the dynamic models. Here we propose a hybrid method to leverage cloud computing resources to streamline and scale up EHE/ECE identification workflows with improved specification to help configuration of on-premises computing. Aim 1: Improving scalability and computational efficiency of detecting extreme climate events in-cloud versus on-premises computing We will develop and implement computational methodologies to (1) scale up the spatial interpolation methods at continental scale using parallel and distributed computing algorithms, (2) monitor and assess the performance of these algorithms in terms of computational time, memory allocation and storage resources compared to the dedicated server utilization and conventional High-Performance Computing (HPC) approach.We hypothesize that cloud computing will improve efficiency of current methods which have been implemented on on-premises computing infrastructure using all-in memory solutions and serialized data pipeline. We will leverage efficiencies of spatially enabled databases along with DevOps tools and services such as containerization, and automated deployment to streamline our research workflows. Aim 2: Improving accuracy and robustness of extreme climate events identification in-cloud versus on-premises computing We will assess the robustness of dynamic EHE/ECE delineation methods when applied to heterogeneous climatological data at continental geographies and beyond. Specifically, we will evaluate the extent to which cloud computing improves the accuracy of the spatial-temporal methods in identifying populations and areas impacted by EHE/ECE using different model parameterization scenarios. We hypothesize that cloud computing will improve the accuracy and robustness of EHE/ECE identification methods by (1) facilitating development of more complex models that take into account additional environmental variables, (2) by streamlining reproducibility practices that enables the wider scientific community to test and validate the models at multiple scales that results in more reliable models

项目摘要/叙述 极端炎热和寒冷事件（EHE/ECE）与一系列不良健康结果有关 加剧原有病症，导致精神疾病、呼吸系统疾病和心血管疾病 研究通常通过简单化的方法确定受 EHE/ECE 影响的地区和人群 将最近气象站的温度数据分配给正在研究的人口（例如，人口普查） 地区或邮政编码）。我们的初步分析表明，动态时空 方法论显着减少了传统方法中容易发生的错误分类。 实现具有更高空间和时间分辨率的更复杂模型可以提高计算能力 复杂性阻碍了动态模型的应用和可扩展性。这里我们提出一种混合方法 利用云计算资源来简化和扩展 EHE/ECE 识别工作流程 改进的规范有助于配置本地计算。目标 1：提高可扩展性和 云中检测极端气候事件与本地计算的计算效率 我们将 开发和实施计算方法以（1）扩大空间插值方法 大陆尺度使用并行和分布式计算算法，（2）监控和评估性能 这些算法在计算时间、内存分配和存储资源方面与 专用服务器利用率和传统的高性能计算（HPC）方法。我们假设 云计算将提高已在本地实施的当前方法的效率 使用全内存解决方案和序列化数据管道的计算基础设施。我们将利用 空间支持的数据库以及 DevOps 工具和服务（例如容器化）的效率，以及 自动化部署以简化我们的研究工作流程。目标 2：提高准确性和鲁棒性 云中和本地计算的极端气候事件识别 我们将评估 应用于大陆异质气候数据的动态 EHE/ECE 圈定方法 地理及其他地区。具体来说，我们将评估云计算在多大程度上改善了 使用时空方法识别受 EHE/ECE 影响的人群和区域的准确性 不同的模型参数化场景。我们假设云计算将提高准确性 通过 (1) 促进更复杂模型的开发，提高 EHE/ECE 识别方法的鲁棒性 考虑到额外的环境变量，（2）通过简化再现性实践 使更广泛的科学界能够在多个尺度上测试和验证模型，从而产生更多结果 可靠的模型

项目成果

Association between timing and consistency of physical activity and type 2 diabetes: a cohort study on participants of the UK Biobank.

身体活动的时间和一致性与 2 型糖尿病之间的关联：一项针对英国生物银行参与者的队列研究。

• 发表时间: 2023-12
• 影响因子: 8.2
• 作者: Tian, Caiwei;Bürki, Charlyne;Westerman, Kenneth E;Patel, Chirag J
• 通讯作者: Patel, Chirag J

Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis.

评估与 2 型糖尿病纵向风险相关的累积行为因素的遗传贡献突出了肥胖和大脑代谢轴。

• 发表时间: 2024-01-31
• 作者: Carvalho, Nuno R G;He, Yixuan;Smadbeck, Patrick;Flannick, Jason;Mercader, Josep M;Udler, Miriam;Manrai, Arjun K;Moreno, Jordi;Patel, Chirag J
• 通讯作者: Patel, Chirag J

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK.

英国 2020 年 7 月和 2021 年 2 月 COVID-19 风险因素的优先顺序。

• 发表时间: 2023-03-30
• 作者: Tangirala, Sivateja;Tierney, Braden T;Patel, Chirag J
• 通讯作者: Patel, Chirag J

Spatio-temporal interpolation and delineation of extreme heat events in California between 2017 and 2021.

2017年至2021年加州极端高温事件的时空插值和圈定。

• DOI: 10.1016/j.envres.2023.116984
• 发表时间: 2023-08-01
• 期刊: Environmental research
• 影响因子: 8.3
• 作者: P. Fard;M. Chung;Hossein Estiri;C. Patel
• 通讯作者: C. Patel