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With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), ithas becoming inherently important to disseminate accurate and timelyinformation about the disease. Due to the ubiquity of Internet connectivity andsmart devices, social sensing is emerging as a dynamic AI-driven sensingparadigm to extract real-time observations from online users. In this paper, wepropose CovidSens, a vision of social sensing based risk alert systems tospontaneously obtain and analyze social data to infer COVID-19 propagation.CovidSens can actively help to keep the general public informed about theCOVID-19 spread and identify risk-prone areas. The CovidSens concept ismotivated by three observations: 1) people actively share their experience ofCOVID-19 via online social media, 2) official warning channels and newsagencies are relatively slower than people reporting on social media, and 3)online users are frequently equipped with powerful mobile devices that canperform data processing and analytics. We envision unprecedented opportunitiesto leverage posts generated by ordinary people to build real-time sensing andanalytic system for gathering and circulating COVID-19 propagation data.Specifically, the vision of CovidSens attempts to answer the questions: How todistill reliable information on COVID-19 with prevailing rumors andmisinformation? How to inform the general public about the state of the spreadtimely and effectively? How to leverage the computational power on edge devicesto construct fully integrated edge-based social sensing platforms? In thisvision paper, we discuss the roles of CovidSens and identify potentialchallenges in developing reliable social sensing based risk alert systems. Weenvision that approaches originating from multiple disciplines can be effectivein addressing the challenges. Finally, we outline a few research directions forfuture work in CovidSens.

CovidSens: A Vision on Reliable Social Sensing for COVID-19