Open Access

ARTICLE

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

by Siddhant Banyal¹, Rinky Dwivedi², Koyel Datta Gupta², Deepak Kumar Sharma^3,*, Fadi Al-Turjman⁴, Leonardo Mostarda⁵

1 Department of Instrumentation and Control, Netaji Subhas University of Technology, New Delhi, 110078, India
2 Department of Computer Science & Engineering, Maharaja Surajmal Institute of Technology, New Delhi, 110058, India
3 Department of Information Technology, Netaji Subhas University of Technology, New Delhi, 110078, India
4 Research Center for AI and IoT, Near East University, Nicosia, Mersin, 10, Turkey
5 Computer Science Department, Camerino University, Camerino, 62032, Italy

* Corresponding Author: Deepak Kumar Sharma. Email:

(This article belongs to the Special Issue: COVID-19 impacts on Software Engineering industry and research community)

Computers, Materials & Continua 2021, 67(2), 1679-1696. https://doi.org/10.32604/cmc.2021.014387

Received 17 September 2020; Accepted 23 October 2020; Issue published 05 February 2021

Abstract

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.

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