Prediction of Covid-19 trends in Europe using generalized regression neural network optimized by flower pollination algorithm. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Gupta, Koyel Datta; Dwivedi, Rinky; Sharma, Deepak Kumar
• Is Part Of: Journal of interdisciplinary mathematics. Volume 24:Issue 1(2021)
• Page Start: 33
• Page End: 51
• Publication Date: 2021-01-02
• Subjects: 68T05 -- 68Q32; COVID-19 -- GRNN -- FPA -- Smoothing function -- Outbreak prediction; Mathematics -- Periodicals; Mathematics; Periodicals; 510.5
• DOI: 10.1080/09720502.2020.1833447
• Languages: English
• ISSNs: 0972-0502

Abstract

Abstract: Corona virus disease (COVID-19) is a contagious infection caused by a novel virus. It causes severe flu-like respiratory illness with cough, fever, and in some extreme cases may cause acute respiratory distress. The corona virus disease 2019 (COVID-19) which started in the city of Wuhan in Hubei province of China, soon spread to other countries of the world. The rapid spread of this Corona virus disease throughout the world lead it to be declared a pandemic by WHO (World Health Organization) on March 11, 2020. This study focused on the outbreak of the disease in the Europe continent and tries to predict its trends. As on 7th May, 2020, Spain has been identified to have the maximum number of confirmed COVID-19 cases in Europe and second in the world following United States of America. The COVID-19 dataset for Europe (European Centre for Disease Prevention and Control) is compiled and a generalized regression neural network model optimized with flower pollination algorithm (FPA-GRNN) is constructed to predict the epidemic pattern.