Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics.-猫眼课题宝

DOI：

10.2196/24432

发表时间：

2020-12-18

期刊：

JMIR research protocols

影响因子：

1.7

通讯作者：

中科院分区：

其他

文献类型：

作者：

Li Z;Li X;Porter D;Zhang J;Jiang Y;Olatosi B;Weissman S

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752182/

人员流动是推动传染病空间传播的力量之一。到目前为止，在COVID-19大流行期间减少和跟踪人员流动已被证明能有效限制病毒的传播。用于监测和建模传染病的空间传播的现有方法依赖于各种数据源作为人类移动的代理，诸如航空旅行数据、移动的电话数据和钞票跟踪。然而，这些数据来源的内在局限性使我们无法系统地监测和分析不同空间尺度（从地方到全球）上的人类活动。来自社交媒体的大数据，如带有地理标记的推文，已被广泛用于人类流动性研究，但需要更多的研究来验证使用这些数据在全球传染病传播的背景下研究不同地理尺度（例如，从本地到全球）的人类流动的能力和局限性。这项研究旨在开发一种新的数据驱动的公共卫生方法，利用Twitter的大数据，结合其他人类移动数据源和人工智能，监测和分析不同空间尺度（从全球到区域再到地方）的人类移动。我们将首先开发一个具有优化时空索引的数据库来存储和管理本项目中收集的多光谱数据集。该数据库将连接到我们内部的Hadoop计算集群，以实现高效的大数据计算和分析。然后，我们将开发创新的数据模型，预测模型和计算算法，以有效地提取和分析人类运动模式，使用来自Twitter和其他人类移动数据源的地理标记的大数据，目的是提高公共卫生应急响应和疾病监测系统的态势感知和风险预测。该项目于2020年5月获得资助。我们已经开始了项目的数据收集、处理和分析。研究结果可以帮助政府官员、公共卫生管理人员、应急响应人员和研究人员回答大流行期间的关键问题，这些问题涉及州、县或社区当前和未来的传染风险，以及社交/物理距离做法在遏制病毒传播方面的有效性。DERR1-10.2196/24432

Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. DERR1-10.2196/24432