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.
Funder
Australian Research Council
Subject
General Pharmacology, Toxicology and Pharmaceutics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine
Reference55 articles.
1. Pandemics and social capital: From the Spanish flu of 1918-19 to COVID-19.;T Fetzer,2020
2. Forecasting the novel coronavirus COVID-19.;F Petropoulos;PLoS One.,2020
3. The reproductive number of COVID-19 is higher compared to SARS coronavirus.;Y Liu;J. Travel Med.,2020
4. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand.;N Ferguson,2020
5. Early dynamics of transmission and control of COVID-19: a mathematical modelling study.;A Kucharski;Lancet Infect. Dis.,2020
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献