Numerical analysis of fractional coronavirus model with Atangana-Baleanu derivative in Liouville-Caputo sense.

The novel coronavirus which emerged at the end of the year 2019 has made a huge impact on the population in all parts of the world. The causes of the outbreak of this deadliest virus in human beings are not yet known to the full extent. In this paper, an investigation is carried out for a new convergent solution of the time-fractional coronavirus model and a reliable homotopy perturbation transform method (HPTM) is used to explore the possible solution. In the presented model, the Atangana-Baleanu derivative in the Liouville-Caputo sense is used. The variations of the susceptible, the exposed, the infected, the quarantined susceptible (isolated and exposed), the hospitalized and the recovered population with time are presented through figures and are further discussed. The effects of selected parameters on the population with the time are also shown through figures. The convergence of solution by the HPTM is shown through tables. The results reveal that the HPTM is efficient, systematic, very effective, and easy to use in getting a solution to this new time-fractional mathematical model of coronavirus disease.

Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents,which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The researchworkwas designed via a particularmethod of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model's dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis. © 2023 Tech Science Press. All rights reserved.

How SARS-CoV-2 and Comparable Pathogens Can Be Defeated in a Single Day: Description and Mathematical Model of the Carrier Separation Plan (CSP).

A simple, common-sense, three-component procedure-the Carrier Separation Plan (CSP)-can immediately halt the transmission of SARS-CoV-2 or a comparable pathogen, allow the safe reopening of an entire economy without the need for social distancing, and quickly eradicate the pathogen from the population (assuming the pathogen can be killed by the immune systems of the carriers). The three components are (a) nearly simultaneous self-testing for the pathogen by an entire population, followed rapidly by (b) nearly simultaneous self-isolation of carriers, and (c) secondary screening at entrances to facilities where people congregate. After a period of preparation lasting roughly 5-10 weeks, these steps could and probably should be taken in a single day. The power of this methodology has already been demonstrated in varying degrees with groups ranging in size from 1,000 to 11 million. Although this plan might seem daunting, its costs are minimal compared to the losses we have incurred by relying on half measures, and the US and other countries have the technological, logistical, and industrial capacities to implement this plan in a matter of weeks. With proper messaging during the weeks leading up to the testing, compliance in such a program is likely to be high given the potential benefits, and because participation is voluntary and testing is noninvasive, the legal and ethical issues associated with such a program are minimal - trivial, in fact, compared to those associated with imposing a months-long lockdown on an entire population. A SIRD/CSP model suggests that the single-day testing and separation procedure will substantially lower the number of infections, even if compliance with the procedure is modest. Modeling also suggests that when long-term secondary screening is added to the 1-day procedure, over time, the pathogen is eradicated from the population. This can occur even when compliance with secondary screening is itself relatively low.

Design of nonstandard computational method for stochastic susceptible-infected-treated-recovered dynamics of coronavirus model.

The current effort is devoted to investigating and exploring the stochastic nonlinear mathematical pandemic model to describe the dynamics of the novel coronavirus. The model adopts the form of a nonlinear stochastic susceptible-infected-treated-recovered system, and we investigate the stochastic reproduction dynamics, both analytically and numerically. We applied different standard and nonstandard computational numerical methods for the solution of the stochastic system. The design of a nonstandard computation method for the stochastic system is innovative. Unfortunately, standard computation numerical methods are time-dependent and violate the structure properties of models, such as positivity, boundedness, and dynamical consistency of the stochastic system. To that end, convergence analysis of nonstandard computational methods and simulation with a comparison of standard computational methods are presented.