SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Healthy City

Abir El Azzaoui; Sushil Kumar Singh; Jong Hyuk Park

doi:10.1016/j.scs.2021.102993

SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Healthy City

Sustain Cities Soc. 2021 Aug:71:102993. doi: 10.1016/j.scs.2021.102993. Epub 2021 May 7.

Authors

Abir El Azzaoui¹, Sushil Kumar Singh¹, Jong Hyuk Park¹

Affiliation

¹ Department of Computer Science and Engineering, Seoul National University of Science and Technology, (SeoulTech), Seoul 01811, Republic of Korea.

Abstract

Nowadays, the world is experiencing a pandemic crisis due to the spread of COVID-19, a novel coronavirus disease. The contamination rate and death cases are expeditiously increasing. Simultaneously, people are no longer relying on traditional news channels to enlighten themselves about the epidemic situation. Alternately, smart cities citizens are relying more on Social Network Service (SNS) to follow the latest news and information regarding the outbreak, share their opinions, and express their feelings and symptoms. In this paper, we propose an SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Sustainable Healthy City, where Twitter platform is adopted. Over 10000 Tweets were collected during two months, 38% of users aged between 18 and 29, while 26% are between 30 and 49 years old. 56% of them are males and 44% are females. The geospatial location is USA, and the used language is English. Natural Language Processing (NLP) is deployed to filter the tweets. Results demonstrated an outbreak cluster predicted seven days earlier than the confirmed cases with an indicator of 0.989. Analyzing data from SNS platforms enabled predicting future outbreaks several days earlier, and scientifically reduce the infection rate in a smart sustainable healthy city environment.

Keywords: Big Data Analysis; COVID-19; NLP; SNS; Smart Healthy City.