Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.

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_s

SUBMITTER: Shahid F 

PROVIDER: S-EPMC7437542 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature