With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

中文翻译：

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CovidSens：关于 COVID-19 可靠社会感知的愿景

随着 2019 年冠状病毒病 (COVID-19) 的螺旋式大流行，传播有关该疾病的准确和及时信息已变得至关重要。由于互联网连接和智能设备的普遍存在，社会传感正在成为一种动态的人工智能驱动的传感范式，用于从在线用户那里提取实时观察结果。在本文中，我们提出了 CovidSens，这是一种基于社会感知的风险警报系统的愿景，可以自发地获取和分析社会数据以推断 COVID-19 传播的状态。CovidSens 可以积极帮助公众了解 COVID-19 的传播情况，并通过推断未来的传播模式来识别风险高发区域。CovidSens 的概念源于三个观察结果：(1) 人们一直在通过在线社交媒体积极分享他们的健康状况和 COVID-19 经历，(2) 官方警告渠道和新闻机构比人们在社交媒体上报告他们对 COVID-19 的观察和经历的速度相对慢(3) 在线用户经常配备功能强大的移动设备，这些移动设备能够为数据处理和分析执行重要的设备上计算。我们设想了一个前所未有的机会，可以利用普通人产生的帖子来建立一个实时传感和分析系统，以收集和传播 COVID-19 传播的重要信息。具体来说，CovidSens 的愿景试图回答以下问题：在社交媒体上盛行的谣言和错误信息并存的情况下，如何提炼有关 COVID-19 的可靠信息？如何及时有效地向公众通报疫情的最新情况，提醒公众做好准备？如何利用边缘设备（例如智能手机、物联网设备、无人机）的计算能力来构建完全集成的基于边缘的社交传感平台，以快速检测 COVID-19 传播？在这份愿景文件中，我们讨论了 CovidSens 的作用，并确定了开发可靠的基于社会传感的风险警报系统的潜在挑战。我们设想源自多个学科（例如，人工智能、估计理论、机器学习、约束优化）的方法可以有效地应对挑战。最后，