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  sensing paradigm to collect real-time observations from online users. In this vision paper we propose CovidSens, the concept of social-sensing-based risk alerting systems to notify the general public  about the COVID-19 spread  . The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media . We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19? How to distill reliable information about the disease 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 ? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in implementing reliable social-sensing-based risk alerting systems. We envision that approaches originating from multiple disciplines (e.g. 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.
With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information . 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 COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas . The CovidSens concept is motivated by three observations: 1) people actively share their experience of COVID-19 via online social media, 2) official warning channels and news agencies are relatively slower than people reporting on social media , and 3) online users are frequently equipped with substantially capable mobile devices that can perform data processing and analytics. We envision unprecedented opportunity to leverage posts generated by ordinary people to build a real-time sensing and analytic system for gathering and circulating COVID-19 propagation data . Specifically, the vision of CovidSens attempts to answer the questions : How to distill reliable information about COVID-19 with prevailing rumors and misinformation ? How to inform the general public about the state of the spread timely and effectively ? How to leverage the computational power on edge devices to construct fully integrated edge-based social sensing platforms ? In this vision paper, we discuss the roles of CovidSens and identify potential challenges in developing reliable social sensing based risk alert systems. We envision that approaches originating from multiple disciplines can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.
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