COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling
| Field | Value | Language |
| dc.contributor.author | Shahriari, Siroos | en |
| dc.contributor.author | Rashidi, Taha Hossein | en |
| dc.contributor.author | Azad, AKM | en |
| dc.contributor.author | Vafaee, Fatemeh | en |
| dc.date.accessioned | 2021-11-26T05:05:17Z | |
| dc.date.available | 2021-11-26T05:05:17Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://hdl.handle.net/2123/27075 | |
| dc.description.abstract | 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: http://vafaeelab.com/COVID19TS.html. | en |
| dc.language.iso | en | en |
| dc.rights | Other | |
| dc.subject | COVID-19 | en |
| dc.subject | Coronavirus | en |
| dc.title | COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling | en |
| dc.type | Article | en |
| dc.identifier.doi | 10.12688/f1000research.73969.1 | |
| dc.relation.other | Australian Research Council | en |
| usyd.faculty | SeS faculties schools::Faculty of Medicine and Health | en |
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