  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 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 powerful mobile devices that can
perform data processing and analytics. We envision unprecedented opportunities
to leverage posts generated by ordinary people to build 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 on 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.
