A reliable numerical analysis for stochastic gonorrhea epidemic model with treatment effect

The phenomena of disease spread are unpredictable in nature due to random mixing of individuals in a population.It is of more significance to include this randomness while modeling infectious diseases.Modeling epidemics including tlieir stochastic behavior could be a more realistic approach in many situations.In thus paper,a stochastic gon orrhea epidemic model with treatment effect has been analyzed numerically.Numerical solution of stochastic model is presented in comparison with its deterministic part.The dynamics of the gonorrhea disease is governed by a threshold quantity Ai called basic reproductive number.If.A1<1.then disease eventually dies out while A1>1 shows the persistence of disease in population.The standard numerical schemes like Euler Maruyaina.stochastic Euler and stochastic Rurige Kutta are highly dependent on step size and do not behave well in certain scenarios.A competitive non-standard finite dif ference numerical scheme in stochastic setting is proposed,which is independent of step size and remains consistent with the corresponding deterministic model.

A Numerical Efficient Technique for the Solution of Susceptible Infected Recovered Epidemic Model

The essential features of the nonlinear stochastic models are positivity,dynamical consistency and boundedness.These features have a significant role in different fields of computational biology and many more.The aim of our paper,to achieve the comparison analysis of the stochastic susceptible,infected recovered epidemic model.The stochastic modelling is a realistic way to study the dynamics of compartmental modelling as compared to deterministic modelling.The effect of reproduction number has also observed in the stochastic susceptible,infected recovered epidemic model.For comparison analysis,we developed some explicit stochastic techniques,but they are the time-dependent techniques.The implicitly driven explicit technique has developed for the stochastic susceptible,infected recovered epidemic model.In the support,some theorems and graphical illustration has presented.Also,the time efficiency of this method makes it easy to find the solution of the stochastic system.The comparison with other techniques shows the efficacy and reliability of the designed technique.