Title: Outbreak trends of fatality rate into coronavirus disease-2019 using deep learning

Authors: Robin Singh Bhadoria; Yash Gupta; Ivan Perl

Addresses: Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India ' Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT) Nagpur, Maharashtra, India ' Department of Software Engineering and Computer Technologies, ITMO University, St. Petersburg, Russia

Abstract: The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11 March 2020. It was known to originate from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19), which is highly impulsive. This paper provides intrinsic algorithms namely - linear regression and long short-term memory (LSTM) using deep learning for time series-based prediction. It also uses the ReLU activation function and Adam optimiser. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provides an extensive model that shows a brief prediction about the number of cases and time for recovered, active and deaths rate till January 2021.

Keywords: pandemic analysis; coronavirus disease-2019; COVID-19; linear regression; time series forecasting; long short-term memory; LSTM; deep learning.

DOI: 10.1504/IJMEI.2023.127256

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.1, pp.70 - 83

Received: 28 Nov 2020
Accepted: 17 Feb 2021
Published online: 30 Nov 2022 *