Outbreak trends of fatality rate into coronavirus disease-2019 using deep learning
International Journal of Medical Engineering and Informatics
; 15(1):70-83, 2023.
Article
in English
| EMBASE | ID: covidwho-2321993
ABSTRACT
The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11 March 2020. It was known to originate from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19), which is highly impulsive. This paper provides intrinsic algorithms namely - linear regression and long short-term memory (LSTM) using deep learning for time series-based prediction. It also uses the ReLU activation function and Adam optimiser. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provides an extensive model that shows a brief prediction about the number of cases and time for recovered, active and deaths rate till January 2021.Copyright © 2023 Inderscience Enterprises Ltd.
coronavirus disease-2019; covid-19; deep learning; linear regression; long short-term memory; lstm; pandemic analysis; time series forecasting; algorithm; article; artificial neural network; coronavirus disease 2019/ep [Epidemiology]; fatality; forecasting; human; linear regression analysis; machine learning; mathematical model; mortality rate; pandemic; prediction; scientific literature; short term memory; speech discrimination; speech intelligibility; time series analysis; vaccination
Full text:
Available
Collection:
Databases of international organizations
Database:
EMBASE
Type of study:
Experimental Studies
/
Prognostic study
Topics:
Vaccines
Language:
English
Journal:
International Journal of Medical Engineering and Informatics
Year:
2023
Document Type:
Article
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