Liu Sicong, Poccia Silvestro, Candan K Selçuk, Chowell Gerardo, Sapino Maria Luisa
School of Informatics, and Decision Systems Engineering, Arizona State University, Tempe.
Computer Science Department, University of Torino, Italy.
J Infect Dis. 2016 Dec 1;214(suppl_4):S427-S432. doi: 10.1093/infdis/jiw305.
Carefully calibrated large-scale computational models of epidemic spread represent a powerful tool to support the decision-making process during epidemic emergencies. Epidemic models are being increasingly used for generating forecasts of the spatial-temporal progression of epidemics at different spatial scales and for assessing the likely impact of different intervention strategies. However, the management and analysis of simulation ensembles stemming from large-scale computational models pose challenges, particularly when dealing with multiple interdependent parameters, spanning multiple layers and geospatial frames, affected by complex dynamic processes operating at different resolutions.
We describe and illustrate with examples a novel epidemic simulation data management system, epiDMS, that was developed to address the challenges that arise from the need to generate, search, visualize, and analyze, in a scalable manner, large volumes of epidemic simulation ensembles and observations during the progression of an epidemic.
epiDMS is a publicly available system that facilitates management and analysis of large epidemic simulation ensembles. epiDMS aims to fill an important hole in decision-making during healthcare emergencies by enabling critical services with significant economic and health impact.
经过精心校准的大规模疫情传播计算模型是在疫情紧急情况下支持决策过程的有力工具。疫情模型正越来越多地用于预测不同空间尺度上疫情的时空发展，并评估不同干预策略可能产生的影响。然而，大规模计算模型产生的模拟集合的管理和分析带来了挑战，特别是在处理多个相互依赖的参数时，这些参数跨越多个层次和地理空间框架，受到以不同分辨率运行的复杂动态过程的影响。
我们通过实例描述并说明了一种新型的疫情模拟数据管理系统epiDMS，该系统旨在应对在疫情发展过程中以可扩展方式生成、搜索、可视化和分析大量疫情模拟集合及观测数据时出现的挑战。
epiDMS是一个公开可用的系统，有助于管理和分析大型疫情模拟集合。epiDMS旨在通过提供具有重大经济和健康影响的关键服务，填补医疗紧急情况决策中的一个重要空白。
