02170nam 2200409 450 991080996250332120231110221437.01-80262-836-3(CKB)4950000000283502(MiAaPQ)EBC6787965(Au-PeEL)EBL6787965(EXLCZ)99495000000028350220220712d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierUsing data science to understand the coronavirus pandemic /guest editors Xin Tian, Wu He and Yunfei Xing[Place of publication not identified] :Emerald Publishing Limited,2021.1 online resource (81 pages)Information Discovery and Delivery,2398-6247 ;Volume 49, Number 3Cover -- Guest editorial -- Twitter users' coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods -- A comparative study of modified SIR and logistic predictors using local level database of COVID-19 in India -- An agent-based model for simulating COVID-19 transmissions on university campus and its implications on mitigation interventions: a case study -- An empirical investigation of precursors influencing social media health information behaviors and personal healthcare habits during coronavirus (COVID-19) pandemic -- An analysis of attitude of general public toward COVID-19 crises - sentimental analysis and a topic modeling study -- COVID-19 and India: what next? -- Forecasting mental health and emotions based on social media expressions during the COVID-19 pandemic.Information Discovery and Delivery COVID-19 Pandemic, 2020-Geographic information systemsCOVID-19 Pandemic, 2020-Geographic information systems.614.592414Tian XinHe WuXing YunfeiMiAaPQMiAaPQMiAaPQBOOK9910809962503321Using data science to understand the coronavirus pandemic4111718UNINA