Open AccessPredicting COVID‐19 trends in Canada: a tale of four models
Author(s) -
Zhang Wandong,
Zhao W.G. Will,
Wu Dana,
Yang Yimin
Publication year - 2020
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs.2020.0017
Subject(s) - covid-19 , logistic regression , pandemic , econometrics , turning point , regression , regression analysis , demography , statistics , geography , computer science , medicine , economics , mathematics , virology , period (music) , sociology , physics , disease , pathology , outbreak , acoustics , infectious disease (medical specialty)
This study aims to offer multiple‐model informed predictions of COVID‐19 in Canada, specifically through the use of deep learning strategy and mathematical prediction models including long‐short term memory network, logistic regression model, Gaussian model, and susceptible‐infected‐removed model. Using the published data as of the 10th of April 2020, the authors forecast that the daily increased number of infective cases in Canada has not reached the peak turning point and will continue to increase. Therefore, Canada's healthcare services need to be ready for the magnitude of this pandemic.