Li Zhenlong, Li Xiaoming, Porter Dwayne, Zhang Jiajia, Jiang Yuqin, Olatosi Bankole, Weissman Sharon
Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, United States.
Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
JMIR Res Protoc. 2020 Dec 18;9(12):e24432. doi: 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.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24432.
人类流动是推动传染病空间传播的因素之一。迄今为止，在新冠疫情期间减少并追踪人类流动已被证明在限制病毒传播方面有效。现有的监测和建模传染病空间传播的方法依赖于各种数据源作为人类流动的代理，如航空旅行数据、手机数据和纸币追踪。然而，这些数据源的内在局限性使我们无法在不同空间尺度（从局部到全球）对人类流动进行系统监测和分析。
来自社交媒体的大数据，如带有地理标记的推文，已广泛应用于人类流动性研究，但在全球传染病传播背景下，对于使用此类数据研究不同地理尺度（如从局部到全球）的人类流动的能力和局限性，仍需要更多研究来验证。本研究旨在开发一种新的数据驱动的公共卫生方法，利用来自推特的大数据以及其他人类流动数据源和人工智能，在不同空间尺度（从全球到区域再到局部）监测和分析人类流动。
我们将首先开发一个具有优化时空索引的数据库，以存储和管理本项目中收集的多源数据集。该数据库将连接到我们内部的Hadoop计算集群，以进行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，以利用来自推特的地理标记大数据和其他人类流动数据源有效提取和分析人类流动模式，目标是提高公共卫生应急响应和疾病监测系统中的态势感知和风险预测能力。
该项目截至2020年5月已获得资助。我们已开始该项目的数据收集、处理和分析工作。
研究结果可帮助政府官员、公共卫生管理人员、应急响应人员和研究人员在疫情期间回答有关一个州、县或社区当前及未来感染风险以及社会/物理距离措施在遏制病毒传播方面有效性的关键问题。
国际注册报告识别码（IRRID）：DERR1-10.2196/24432
