Development of Epidemiological Modeling RD-Covid-19 of Coronavirus Infectious Disease and Its Numerical Simulation

Abstract

AbstractCoronavirus disease (Covid-19) occurred first in Wuhan city of Hubei province of China in December 2019. The World Health Organization (WHO) declared the spread or the transmission of this virus as a global pandemic. The virus was named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses on February 11, 2020. Disease due to this novel-coronavirus is infectious. Therefore, modeling such an infectious disease is essential to understand the method of its transmission, spread, and epidemic. Several researchers have found that the transfer of the virus occurs through human contact via their pathogens, such as coughing, sneezing, and breathing. With all sorts of preventive measures (social distancing, wearing mask and lockdown), there is a need to develop a dynamic model of epidemiology for infectious disease. In this article, we have developed a new epidemiological dynamical model named RD_Covid-19 (version 1.0) model. The traditional epidemiological model of an infectious disease known as susceptible-exposed-infected-recovered-dead (SEIRD) is modified to develop this new model. RD_Covid-19 is a networked epidemiological model in which a data-driven logistic model, traditional epidemiological models such as SIR (Susceptible, Infected, Recovered), SEIR and SEIQRDP are interlinked. The model forecasts the spread of the Covid-19. Nonlinear least-squares optimization technique is applied for fitting the model to estimate its parameters. The realistic data is taken from John Hopkins University and WHO dashboard. The outcome of the numerical simulation of the model generates the temporal profile of infected, recovered, and death cases. The severity of the model is measured by computing the basic reproduction number (R0). The model executed to explore the corona outbreak in China, India, Brazil, and Russia. The estimated value of basic reproduction number, R0 is well in agreement with that obtained from the outcome of traditional models SIR and SEIR. The verification and validation (V & V) process of our model is carried out by comparing its results with an analogical logistic model.KeywordsCovid-19Epidemiological modelOptimizationNumerical OptimizationNonlinear least squareBasic reproduction number