Computational social systems for COVID-19 emergency management and beyond

• Main Authors: ZHANG, Jun Jason, WANG, Fei-Yue, YUAN, Yong, XU, Guandong, LIU, Huan, GAO, Wei, JAMEEL, Shoaib, RAZZAK, Imran, EKLUND, Peter, AHMED, Sheraz, QIN, Rui, LI, Juanjuan, WANG, Xiao, YANG, De-Nian, TURGUT, Damla, BENSLIMANE, Abderrahim, PRASAD, Neeli, CHEN, Kwang-Cheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6685
• Institution: Singapore Management University

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.In June 2020, IEEE Transactions on Computational Social Systems (TCSS) launched the Special Issue on Computational Social Systems for COVID-19 Emergency Management and Beyond, aiming to provide the report of state-of-the-art research work from the global that addresses innovative techniques, applications, and results. The special issue received submissions all around the globe, and 13 articles were reviewed and recommended for publication, among them two from North America, two from China, two from India, three from Australia, one from Japan, and one from Pakistan. A quick scanning of the accepted articles is presented in the following.