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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aUsing data science to understand the coronavirus pandemic /$fguest editors Xin Tian, Wu He and Yunfei Xing
210  1$a[Place of publication not identified] :$cEmerald Publishing Limited,$d2021.
215   $a1 online resource (81 pages)
225 0 $aInformation Discovery and Delivery,$x2398-6247 ;$vVolume 49, Number 3
327   $aCover -- Guest editorial -- Twitter users' coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods -- A comparative study of modified SIR and logistic predictors using local level database of COVID-19 in India -- An agent-based model for simulating COVID-19 transmissions on university campus and its implications on mitigation interventions: a case study -- An empirical investigation of precursors influencing social media health information behaviors and personal healthcare habits during coronavirus (COVID-19) pandemic -- An analysis of attitude of general public toward COVID-19 crises - sentimental analysis and a topic modeling study -- COVID-19 and India: what next? -- Forecasting mental health and emotions based on social media expressions during the COVID-19 pandemic.
410  0$aInformation Discovery and Delivery 
606   $aCOVID-19 Pandemic, 2020-$xGeographic information systems
615  0$aCOVID-19 Pandemic, 2020-$xGeographic information systems.
676   $a614.592414
702   $aTian$b Xin
702   $aHe$b Wu
702   $aXing$b Yunfei
801  0$bMiAaPQ
801  1$bMiAaPQ
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906   $aBOOK
912   $a9910795354403321
996   $aUsing data science to understand the coronavirus pandemic$93707981
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