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dc.contributor.authorShahriari, Siroosen
dc.contributor.authorRashidi, Taha Hosseinen
dc.contributor.authorAzad, AKMen
dc.contributor.authorVafaee, Fatemehen
dc.date.accessioned2021-11-26T05:05:17Z
dc.date.available2021-11-26T05:05:17Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/2123/27075
dc.description.abstractA substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with modelling techniques to provide real-time insights. This study introduces a unified platform, COVIDSpread, which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform uses time series models to capture any possible non-linearity in the data. COVIDSpread enables lay users, and experts, to examine the data and develop several customized models with different restrictions such as models developed for a specific time window of the data. COVIDSpread is available here: http://vafaeelab.com/COVID19TS.html.en
dc.language.isoenen
dc.rightsOther
dc.subjectCOVID-19en
dc.subjectCoronavirusen
dc.titleCOVIDSpread: real-time prediction of COVID-19 spread based on time-series modellingen
dc.typeArticleen
dc.identifier.doi10.12688/f1000research.73969.1
dc.relation.otherAustralian Research Councilen
usyd.facultySeS faculties schools::Faculty of Medicine and Healthen


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