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Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic
Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic
- Source :
- Computers, Materials & Continua. 67:2141-2160
- Publication Year :
- 2021
- Publisher :
- Computers, Materials and Continua (Tech Science Press), 2021.
-
Abstract
- The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases The real time data used has been collected from the World Health Organization (WHO) In the proposed research, we have considered three countries to test the proposed model, namely Saudi Arabia, Spain and Italy The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients Standard measure performance Mean squared Error (MSE), Root Mean Squared Error (RMSE), Mean error and correlation are employed to estimate the results of the proposed models The empirical results of the LSTM, using the correlation metrics, are 99 94%, 99 94% and 99 91% in predicting the number of confirmed cases in the three countries As far as the results of the LSTM model in predicting the number of death of Covid-19, they are 99 86%, 98 876% and 99 16% with respect to Saudi Arabia, Italy and Spain respectively Similarly, the experiment's results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19, using the correlation metrics, are 99 06%, 99 96% and 99 94%, whereas the results of the Holt-Trend model in predicting the number of death cases are 99 80%, 99 96% and 99 94% with respect to the Saudi Arabia, Italy and Spain respectively The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries Such findings provide better insights regarding the future of Covid-19 this pandemic in general The results were obtained by applying time series models, which need to be considered for the sake of saving the lives of many people
- Subjects :
- 0209 industrial biotechnology
Mean squared error
Coronavirus disease 2019 (COVID-19)
business.industry
Term memory
Deep learning
02 engineering and technology
Standard measure
World health
Computer Science Applications
Biomaterials
Correlation
020901 industrial engineering & automation
Mechanics of Materials
Modeling and Simulation
Pandemic
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Electrical and Electronic Engineering
business
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 15462226
- Volume :
- 67
- Database :
- OpenAIRE
- Journal :
- Computers, Materials & Continua
- Accession number :
- edsair.doi...........a1c850d90dab096aaf0e118a29e79d6e
- Full Text :
- https://doi.org/10.32604/cmc.2021.014498