Reddy Chimmula, Vinay Kumar
Zhang, Lei
2020-07-24T19:24:34Z
2020-07-24T19:24:34Z
2020
0960-0779
https://doi.org/10.1016/j.chaos.2020.109864
http://hdl.handle.net/20.500.12010/11115
On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global
pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China
around December 2019 and spread out all over the world within few weeks. Based on the public datasets
provided by John Hopkins university and Canadian health authority, we have developed a forecasting
model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel
research, we evaluated the key features to predict the trends and possible stopping time of the current
COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term
memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the
results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this
outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with
Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14th day predictions for 2 successive
days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our
knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases
6 páginas
image/jepg
Chaos, Solitons & Fractals
reponame:Expeditio Repositorio Institucional UJTL
instname:Universidad de Bogotá Jorge Tadeo Lozano
Epidemic transmission
Time series forecasting
Machine learning
Corona virus
COVID-19
Long short term memory (LSTM) networks
Time series forecasting of COVID-19 transmission in Canada using LSTM networks
Artículo
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/acceptedVersion
https://doi.org/10.1016/j.chaos.2020.109864