An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the data set of the number of deaths per day and the number of infected cases per day of the most affected countries, the USA, Brazil, Russia, India, and the UK, have been considered in the first and second waves. The correlation fractal dimension has been estimated for the prescribed data sets of COVID-19, and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance, has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to visualize the transmission rate in the both times.

Omicron: a mysterious variant of concern

“…The reader is encouraged to refer [ 1 – 3 ] for more information on Omicron. Recently, for analyzing and predicting the nature as well as the successive waves of COVID-19, fractal-based methods have been used, see, for instance, [ 4 – 7 ].…”

COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds

“…Biomedical researchers and scientists have proposed various Deep-Learning (DL), Signal-Processing (SP), and Machine-Learning (ML) approaches to diagnosis various diseases from human respiratory-generated sounds [7][8][9]. Recent research focused on prognostic models diagnostic models, and pertained ensemble models for the first wave, second wave, and third waves in India to identify SARS-CoV-2/COVID-19 disease symptoms [10][11][12][13].…”

COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis

“…Since 2018, increase in the number of dengue cases in at risk regions to arbovirus outbreaks such as the Reunion Island has been highlighted [1]. The 2019 Coronavirus disease (COVID- 19), now a global pandemic, is a respiratory disease caused by the severe acute 2 The model Consider a homogeneously mixed population, i.e., individuals in the population have equal probability of contact with each other. Using a deterministic compartmental modeling approach to describe the disease transmission dynamics, at any time t, the total population N h is subdivided into several epidemiological states depending on individuals health status: susceptible humans S h , infectious individuals with dengue I hd , individuals who have recovered from dengue R hd , infectious individuals with COVID-19 I hc , individuals who have recovered from COVID-19 R hd , infectious individuals with co-infected with dengue and COVID-19 I dc .…”