Modified SIR model for COVID-19 transmission dynamics: Simulation with case study of UK, US and India.

Corona virus disease 2019 (COVID-19) is an infectious disease and has spread over more than 200 countries since its outbreak in December 2019. This pandemic has posed the greatest threat to global public health and seems to have changing characteristics with altering variants, hence various epidemiological and statistical models are getting developed to predict the infection spread, mortality rate and calibrating various impacting factors. But the aysmptomatic patient counts and demographical factors needs to be considered in model evaluation. Here we have proposed a new seven compartmental model, Susceptible- Exposed- Infected-Asymptomatic-Quarantined-Fatal-Recovered (SEIAQFR) which is based on classical Susceptible-Infected-Recovered (SIR) model dynamic of infectious disease, and considered factors like asymptomatic transmission and quarantine of patients. We have taken UK, US and India as a case study for model evaluation purpose. In our analysis, it is found that the Reproductive Rate ( R 0 ) of the disease is dynamic over a long period and provides better results in model performance ( > 0 . 98 R-square score) when model is fitted across smaller time period. On an average 40 % - 50 % cases are asymptomatic and have contributed to model accuracy. The model is employed to show accuracy in correspondence with different geographic data in both wave of disease spread. Different disease spreading factors like infection rate, recovery rate and mortality rate are well analyzed with best fit of real world data. Performance evaluation of this model has achieved good R-Square score which is 0 . 95 - 0 . 99 for infection prediction and 0 . 90 - 0 . 99 for death prediction and an average 1 % - 5 % MAPE in different wave of the disease in UK, US and India.

Mass transfer effects on mucus fluid in the presence of chemical reaction

The mucus fluid vehicle is impacted by the synthetic response that changes the physical science of liquid due to the thickness of the bodily fluid. Additionally, various issues in the respiratory system might happen because of bodily fluid adequacy. A central point of transportation of immunizations to forestall COVID-19 is the concentration level expected during movement, stockpiling, and dispersion. The current review stated that mucus fluid transportation is restrained through magnetic force originating due to heat variation. Permeable channel over respiratory disease and chemicals due to mass reaction–diffusion variation. The bodily fluid development is surveyed by the force, energy, and diffusion condition influence of body powers because of attractive field, source of heat cause of thermal conduction, resistance due to disease chemical reaction cause of concentration profile. The nonlinear arrangement of incomplete differential conditions is addressed by the Laplace transform technique, and MATLAB programming outcomes are initiated for momentum, temperature, and diffusion fields and inferred that the bodily fluid stream decelerates due to magnetic force. The skin friction, Nusselt number, Sherwood number, and the microorganism’s thickness are assessed and explained exhaustively. Furthermore, microorganisms are occupied in different elements to survey the mucus fluid mechanism.

Free and Forced Convective Flow in Pleural Fluid with Effect of Injection between Different Permeable Regions

Pleural effusion is an interruption of a pleural cavity in the lung wall. The lung and chest wall reversal process leads to pleural fluid aggregation in the pleural space. The parietal lymphatic expansion occurs because of increased pleural fluid. This model has been developed to obtain new results of respiratory tract infections, and also investigated the reaction of injection on an unstable free and forced convection flow of visceral pleural fluid transports in two different vertical porous regions. Finally, the model gives an impact of COVID-19 in the human respiratory tract, as it helps to anticipate early summary of establishing current pandemic infection. Results are computed analytically and plotted graphically for various physical parameters. The main highlights of this paper are mixed convection has been investigated mathematically in porous media, the effect of temperature and velocity field of pleural fluid was analyzed based on human lung mechanism, heat exchange associates with mucus layer and pleural fluid layer corresponding to thermal radiation and heat absorption, contribution of injection parameter over the region’s mucus and pleural phase, it has shown high sensitivity flow in diagnosis of COVID-19 due to pleural effusion.

New Procedures of a Fractional Order Model of Novel Coronavirus (COVID-19) Outbreak via Wavelets Method

Coronaviruses are a group of RNA (ribonucleic acid) viruses with the capacity for rapid mutation and recombination. Coronaviruses are known to cause respiratory or intestinal infections in humans and animals. In this paper, a biologically compatible set of nonlinear fractional differential equations governing the outbreak of the novel coronavirus is suggested based on a model previously proposed in the literature. Then, this set is numerically solved utilizing two new methods employing sine–cosine and Bernoulli wavelets and their operational matrices. Moreover, the convergence of the solution is experimentally studied. Furthermore, the accuracy of the solution is proved via comparing the results with those obtained in previous research for the primary model. Furthermore, the computational costs are compared by measuring the CPU running time. Finally, the effects of the fractional orders on the outbreak of the COVID-19 are investigated.

A Novel Technique to Control the Accuracy of a Nonlinear Fractional Order Model of COVID-19: Application of the CESTAC Method and the CADNA Library

In this paper, a nonlinear fractional order model of COVID-19 is approximated. For this aim, at first we apply the Caputo–Fabrizio fractional derivative to model the usual form of the phenomenon. In order to show the existence of a solution, the Banach fixed point theorem and the Picard–Lindelof approach are used. Additionally, the stability analysis is discussed using the fixed point theorem. The model is approximated based on Indian data and using the homotopy analysis transform method (HATM), which is among the most famous, flexible and applicable semi-analytical methods. After that, the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method and the CADNA (Control of Accuracy and Debugging for Numerical Applications) library, which are based on discrete stochastic arithmetic (DSA), are applied to validate the numerical results of the HATM. Additionally, the stopping condition in the numerical algorithm is based on two successive approximations and the main theorem of the CESTAC method can aid us analytically to apply the new terminations criterion instead of the usual absolute error that we use in the floating-point arithmetic (FPA). Finding the optimal approximations and the optimal iteration of the HATM to solve the nonlinear fractional order model of COVID-19 are the main novelties of this study.