The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.
Nowadays, massive urban human mobility data are being generated from mobile phones, car navigation systems, and tra c sensors. Predicting the density and ow of the crowd or tra c at a citywide level becomes possible by using the big data and cu ing-edge AI technologies. It has been a very signi cant research topic with high social impact, which can be widely applied to emergency management, tra c regulation, and urban planning. In particular, by meshing a large urban area to a number of ne-grained mesh-grids, citywide crowd and tra c information in a continuous time period can be represented like a video, where each timestamp can be seen as one video frame. Based on this idea, a series of methods have been proposed to address video-like prediction for citywide crowd and tra c. In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets. We rst comprehensively review the state-of-the-art works of literature and formulate the density and in-out ow prediction problem, then conduct a thorough performance assessment for those methods. With this benchmark, we hope researchers can easily follow up and quickly launch a new solution on this topic.
Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g., police) and public service operators (e.g., subway/bus operator) to protect people’s safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent, which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.
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