A hybrid deep learning model for COVID-19 prediction and current status of clinical trials worldwide

Abstract

Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARSCoV- 2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO's daily bulletin report as well as provides information regarding clinical trials across the world. To evaluate the suitability and performance of our proposed model compared to other well-established prediction models, an experimental study has been performed. To estimate the prediction results, the three performance measures, i.e., Root Mean Square Error (RMSE), Coefficient of determination (R2 Score), and Mean Absolute Percentage Error (MAPE) have been employed. The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models. The proposed model archives the lowest RMSE, lowest MAPE, and highest R2 Score throughout the testing, under varied selection criteria (country- wise). This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe. © 2021 Tech Science Press. All rights reserved.