Wang Qu, Fu Meixia, Wang Jianquan, Sun Lei, Huang Rong, Li Xianda, Jiang Zhuqing
School of Automation Science and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Shunde Innovation School, University of Science and Technology Beijing, Foshan, China.
EURASIP J Adv Signal Process. 2023;2023(1):18. doi: 10.1186/s13634-023-00984-6. Epub 2023 Feb 1.
A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.
包括新冠疫情和非典在内的大量疫情迅速席卷全球，夺走了许多人的宝贵生命。由于病毒具有隐匿性和快速传播性，在人力资源有限的情况下，很难追踪到轻症或无症状感染者。构建一个低成本的实时疫情预警系统，以识别与感染者有过接触的人员并确定他们是否需要隔离，是减缓疫情传播的有效手段。在本文中，我们提出了一种基于智能手机的零努力疫情预警方法，以减缓疫情传播。首先，我们通过分层注意力机制和时间卷积网络识别与疫情传播相关的语音活动。随后，我们通过智能手机内置的传感器估计用户之间的社交距离。此外，我们结合Wi-Fi网络日志和社交距离，全面判断用户之间是否存在时空接触并确定接触时长。最后，我们基于与疫情相关的语音活动、社交距离和接触时间来估计感染风险。我们在典型场景中进行了大量精心设计的实验，以充分验证所提出的方法。该方法不依赖于任何额外的基础设施和历史训练数据，有利于与疫情防控系统集成及大规模应用。
