CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Abstract

With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

“…The second major type of SPS melds social media signals with mobile physical sensor data for knowledge extraction. Examples of this type of SPS are: (i) contact tracing of contagious diseases such as COVID-19 with integrated social media and wearable sensors as illustrated in Figure 3 (f) (Rashid and Wang 2022a); (ii) road damage detection using satellite imagery and social media as illustrated in Figure 3 (e) (Zhang et al, 2020d); and (iii) anomaly detection with social airborne sensing (SAS) where social media signals are used to drive UAVs to locations involved with critical events such as natural disasters as illustrated in Figure 2 (Rashid et al, 2020b).…”