Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study

Sourabh Shastri, Kuljeet Singh, Sachin Kumar, Paramjit Kour and Vibhakar Mansotra

Chaos, Solitons & Fractals, 2020, vol. 140, issue C

Abstract: Covid-19 is a highly contagious virus which almost freezes the world along with its economy. Its ability of human-to-human and surface-to-human transmission turns the world into catastrophic phase. In this study, our aim is to predict the future conditions of novel Coronavirus to recede its impact. We have proposed deep learning based comparative analysis of Covid-19 cases in India and USA. The datasets of confirmed and death cases of Covid-19 are taken into consideration. The recurrent neural network (RNN) based variants of long short term memory (LSTM) such as Stacked LSTM, Bi-directional LSTM and Convolutional LSTM are used to design the proposed methodology and forecast the Covid-19 cases for one month ahead. Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries. Upward/downward trend of forecasted Covid-19 cases are also visualized graphically, which would be helpful for researchers and policy makers to mitigate the mortality and morbidity rate by streaming the Covid-19 into right direction.

Keywords: Recurrent neural networks; Time series; Covid-19; LSTM; Forecasting; Deep learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306238

DOI: 10.1016/j.chaos.2020.110227

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