(R1412) Stability and Bifurcation of a Cholera Epidemic Model with Saturated Recovery Rate

In this paper, a Cholera epidemic model is proposed and studied analytically as well as numerically. It is assumed that the disease is transmitted by contact with Vibrio cholerae and infected person according to dose-response function. However, the saturated treatment function is used to describe the recovery process. Moreover, the vaccine against the disease is assumed to be utterly ineffective. The existence, uniqueness and boundedness of the solution of the proposed model are discussed. All possible equilibrium points and the basic reproduction number are determined. The local stability and persistence conditions are established. Lyapunov method and the second additive compound matrix are used to study the global stability of the system. The conditions that guarantee the occurrence of local bifurcation and backward bifurcation are determined. Finally, numerical simulation is used to investigate the global dynamical behavior of the Cholera epidemic model and understand the effects of parameters on evolution of the disease in the environment. It is observed that the solution of the model is very sensitive to varying in parameters values and different types of bifurcations are obtained including backward bifurcation

The Dynamics of Stage Structured Prey-Predator Model Involving Parasitic Infectious Disease

In this paper a prey-predator model involving parasitic infectious disease is proposed and analyzed. It is assumed that the life cycle of predator species is divided into two stages immature and mature. The analysis of local and global stability of all possible subsystems is carried out. The dynamical behaviors of the model system around biologically feasible equilibria are studied. The global dynamics of the model are investigated with the help of Suitable Lyapunov functions. Conditions for which the model persists are established. Finally, to nationalize our analytical results, numerical simulations are worked out for a hypothetical set of parameter values