Analysing deaths and confirmed cases of COVID-19 pandemic by analytical approaches

Abstract

In this work, the time series of growth rates regarding confirmed cases and deaths of COVID-19 for several sampled countries are investigated via an introduction of an orthonormal basis. This basis, which is served as the feature benchmark, reveals the hidden features of COVID-19 via the magnitude of Fourier coefficients. These coefficients are ranked in the form of ranking vectors for all the sampled countries. Based on these and Manhattan metric, we then perform spectral clustering to categorise the countries. Unlike the classical cosine similarity analysis which, relatively speaking, is a composite index and hard to identify the features of the categorised countries, spectral analysis delves into the internal structures or dynamical trend of the time series. This research shows there is no single feature that dominates the trend of the growth rates. It also reveals that results from the spectral analysis are different from the ones of cosine similarity. In the end, some approximated values of the confirmed cases and deaths are also calculated by the spectral analysis.

Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control

“…The study also reveals increasing complexity of lung infections among older people in comparison with younger people based on their X-ray images. The rate of increase in infection and death due to COVID-19, introducing the orthonormal basis, is investigated by Chen [32]. The study based on an orthonormal basis reveals several unknown features of the pandemic via Fourier coefficients.…”

“…This special issue is a compilation of original research articles that address the dynamics and applications of COVID-19 through nonlinear dynamics. The articles are organized in five sections, comprising mathematical modeling and epidemics [1][2][3][4][5][6][7], the dynamics of several waves and transmission [8][9][10][11][12][13][14][15][16][17], neural network and deep learning related to COVID-19 [18][19][20][21][22][23][24], predictions and estimations related to COVID-19 [25][26][27][28][29][30], and detailed analysis on the pandemic and its applications [31][32][33][34][35].…”

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds

“…Natiq and Saha implemented a non-linear dynamical model by combining of susceptible infected and recovered (SIR) model and Lotka-Volterra model to identify the virus transmission between the various species [58]. The researchers are also focused on mathematical models, non-linear dynamic models, and prediction models to determine the seriousness of the virus, how the vaccination is distributed to the people, how vaccination gives immunity to the human body, and the death rate in various countries [59][60][61][62][63].…”

“…: V,-vol 2/COVID-19 is spreading between different species. The researchers are also focused on mathematical models, non-linear dynamic models, and prediction models to identify the seriousness of the virus, how the vaccination is distributed to the people, how vaccination gives immunity to our body, and the death rate in various countries [59][60][61][62][63].…”