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?:abstract
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COVID\'19 is an emerging disease and the precise epidemiological profile does not exist in the world Hence, the COVID\'19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO) Hence, an effective and optimal prediction of COVID\'19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data The proposed COVID\'19 prediction method uses the COVID\'19 data, which is the trending domain of research at the current era of fighting the COVID\'19 attacks thereby, to reduce the death toll However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively The Deep ConvLSTM classifier facilitates to predict the COVID\'19 from the time series data based on the fitness function Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID\'19 prediction Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID\'19 for China and Oman Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1 791, and the minimal RMSE of 1 338 based on confirmed cases in Oman In addition, the developed model achieved the death cases with the values of 1 609, and 1 268 for MSE and RMSE, whereas the MSE and the RMSE value of 1 945, and 1 394 is achieved by the developed model using recovered cases in China © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020
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