Covid-19 Cases and Recovery Previsions with Deep Learning Nested Sequence Prediction Models with Long Short-Term Memory (LSTM) Architecture
Keywords:
COVID-19, Deep Learning, LSTM, Nested Sequence PredictionAbstract
More than 854 thousand COVID-19 cases are confirmed among which at least 176 thousand have recovered as of March. 31, 2020. Success in controlling COVID-19 infections and recoveries requires a timely and accurate monitoring of the epidemic with rather limited data. In this study, we use a Deep Learning nested sequence prediction models with Long Short-Term Memory (LSTM) architecture for the continuous monitoring of the infection and recovering processes. This model was built based on the epidemic data evolution of 79 countries between the date of their first case and March 13, 2020. The data is based on 12 variables for cumulative case number prediction and 13 variables (among which the cumulative number of cases) for cumulative recoveries number prediction. Results are very promising despite the small amount of data provided. The model can evolve and become more accurate by continuously updating and enriching data by adding experiences of all affected countries. To our knowledge, our work is the only one to propose this conduct in the context of COVID-19, generalized for all affected countries. The methods used in this study can be used to inform and encourage the general public, public health professionals, clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.
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