Coronavirus Disease 2019 (COVID-19): Prediction Strategy Using Sequential Deep Learning Model

Abstract

Since the globe has faced extreme difficulties with COVID-19, Artificial Intelligence appeared to help to cope with this epidemic in an innumerable number of ways. Motivated by this, in this article, a robust prediction model called COVID-SDL has been proposed using Sequential Deep Learning (SDL) for predicting the total positive cases per day. In order to evaluate the performance of COVID-SDL, data samples used in the model have been collected from Italy’s COVID-19 situation reports. Besides this, the dataset has gone through the processes of cleaning, filtering, formatting and visualization. COVID-SDL utilizes the correlation information among the features that have strengthened the prediction capability. Also, the exploratory survey showed that 5 most salient features (Home Confinement, Deaths, Recovered, Current Positive Cases and Tests Performed) results better which is obtained from the mentioned dataset primarily composed of 17 features. In addition, to assist the prediction ability of COVID-SDL, ReLu (Rectified Linear Unit) activation function has been used which enhanced the robustness of the model. With a view to making the predictions highly accurate, Adam optimizer has been adopted which works by reducing the cost function and making further updates of the weights. Moreover, COVID-SDL has successfully obtained accuracy parameters such as MAE- 0.00037316, MSE- 0.00000018, RMSE- 0.00043476 and R2 Score- 0.99999 with providing the best fit curve of predicted data which covers 99.999% of the actual data. Furthermore, to prove the robustness of the COVID-SDL, a comparative test among the adaptive and non-adaptive optimizers has also been performed.