Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Abstract

The novel coronavirus disease (COVID-19) causes serious respiratory tract infections in humans, and worse leads to mortality in old-aged people or individuals with co-morbidities. Websites and online social platforms generate a gargantuan amount of data in myriad aspects namely-technology, global news, human healthcare, medicine, socio-political domain, etc., aiding to decipher significant knowledge using web mining. Since the outbreak, people from different geographical locations used hashtags about novel coronavirus. The FAMEC model, the Honghou Hybrid System (HHS), the COVID Tracking Project of Twitter are a few examples of computational intelligent online social trackers that have been devised to track the COVID-19 pandemic. Researchers have identified the significance of tweets to be consistent with the CDC and the WHO reports and discerned that mining of such personal tweets was effective to track, manage, and predict the mortality and morbidity rates, identify the geographic location of patients infected which would, in turn, lead to rapid treatment assessment, employment of telemedicine and sanitization of such regions. This chapter presents how computational intelligence along with online social networks can be used for tracking COVID-19 patients. IntroductionThe novel coronavirus, also called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic infectious disease known as coronavirus disease 2019 (COVID-19) which was initially observed in Wuhan city, China, in December 2019. This pandemic has led to a drastic change in the lifestyle around the world, with many people in quarantine, gigantic change in healthcare infrastructure,

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