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• W3208075045 abstract "<ns3:p>A 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: <ns3:ext-link xmlns:ns4=http://www.w3.org/1999/xlink ext-link-type=uri ns4:href=http://vafaeelab.com/COVID19TS.html>http://vafaeelab.com/COVID19TS.html</ns3:ext-link>.</ns3:p>" @default.
• W3208075045 created "2021-11-08" @default.
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• W3208075045 date "2021-11-03" @default.
• W3208075045 modified "2023-09-24" @default.
• W3208075045 title "COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling" @default.
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• W3208075045 doi "https://doi.org/10.12688/f1000research.73969.1" @default.
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