Narrow Results

The aim of this paper is to model corona-virus (COVID-19) taking into account random perturbations. The suggested model is composed of four different classes i.e. the susceptible population, the smart lockdown class, the infectious population, and the recovered population. We investigate the proposed problem for the derivation of at least one unique solution in the positive feasible region of nonlocal solution. For one stationary ergodic distribution, the necessary result of existence is developed by applying the Lyapunov function and the condition for the extinction of the disease is also established. The obtained results show that the effect of Brownian motion and noise terms on the transmission of the epidemic is very high. If the noise is large the infection may decrease or vanish. For validation of our obtained scheme, the results for all the classes of the problem have been simulated numerically.

Smoking has many harmful effects due to its toxic chemicals, which cause serious diseases. Our major interest in this research is to formulate a spatiotemporal mathematical model that predicts the evolution of smoking in society using a mathematical model that includes the large mobility of individuals. Indeed, we are concerned to study the global asymptotic stability of the unique positive steady state related to this model. The principal objective of this paper is to show that the smoking has no threshold dynamics as it has been shown for a large sample of epidemic models, and the consumption of cigarettes is always persistent, and based on the assumption of the parameters that verify the Lipschitz condition. We proved that the investigated model is always persistent and the unique positive equilibrium state is always globally stable. The mathematical finding is supported numerically using numerical simulations.

In this paper, we study a nonlinear mathematical model which addresses the transmission dynamics of COVID-19. The considered model consists of susceptible ($S$), exposed ($E$), infected ($I$), and recovered ($R$) individuals. For simplicity, the model is abbreviated as $SEIR$. Immigration rates of two kinds are involved in susceptible and infected individuals. First of all, the model is formulated. Then via classical analysis, we investigate its local and global stability by using the Jacobian matrix and Lyapunov function method. Further, the fundamental reproduction number ${\mathcal{ℛ}}_0$ is computed for the said model. Then, we simulate the model through the Runge–Kutta method of order two abbreviated as RK2. Finally, we switch over to the fractional order model and investigate its numerical simulations corresponding to different fractional orders by using the fractional order version of the aforementioned numerical method. Finally, graphical presentations are given for the approximate solution of various compartments of the proposed model. Also, a comparison with real data has been shown.

Deterministic and fractional properties of an epidemic model for the dynamics of the Middle Eastern respiratory syndrome coronavirus (MERS-COV) with various infection stages are proposed in this study whose aim is to show via a mathematical model the transmission of MERS-COV between humans and camels, which are suspected to be the primary source of infection. The mathematical aspects together with biological feasibility of MERS-COV model are provided. The basic reproduction number $R_0$ has been calculated by using the next-generation matrix. With the help of $R_0$, we show the local and global stability analysis of the proposed model. Analysis of sensitivity for the threshold number is performed to understand the most sensitive parameter. Moreover, the concepts of strength number and second-order derivative of the Lyapunov function have been utilized for the waves detection. By using the concept of fixed point approach for the model, concerning if it really exists or not, we prove the existence and uniqueness results for the proposed model. The numerical solutions are obtained with the help of well-known fractional Adams–Bashforth technique. For the approximate solution, with the help of Runge–Kutta technique of order four, we accomplish the numerical simulations to support our analytical outcomes which are believed to have an effective impact on developing preventive measures for MERS-CoV, including disease control as well as prevention of spread and transmission in related populations.