Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.
Chaos Solitons Fractals
; 140: 110212, 2020 Nov.
Article
em En
| MEDLINE
| ID: mdl-32839642
ABSTRACT
COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.
AI, Artificial intelligence; AR, Autoregressive; ARIMA, Autoregressive integrated moving average; Bi-LSTM; Bi-LSTM, Bidirectional long short term memory; COVID-19; Corona virus; DL, Deep learning; Deep learning models; GRU; GRU, Gated recurrent network; LSTM, Long short term memory; MERS, Middle East respiratory syndrome; NN, Neural network; RF, Random forest; RNN, Recurrent neural network; SARIMA, Seasonal autoregressive integrated moving average; SARS, Severe acute respiratory syndrome; SIR, Susceptible-infective-removed; SVR, Support vector machine; WHO, World health organization; epidemic prediction
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Chaos Solitons Fractals
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Paquistão