A Discrete Study on Stochastic Epidemic Models with the Optimal Control Policies and Its Analysis
The article aims to assess the stochastic model's control variant and study the effect of time on the epidemic's behavior. A single population must have strong immunity as it recovers from the epidemic. Lower the infected and susceptible individuals and maximize the absolute amount of recovered individuals by using possible minimal control variables. We have demonstrated whether we should implement a treatment policy to minimize the number of death cases by examining three different examples from various perspectives. We have also proved that the best policy in the event of a fatality is to avoid it. The epidemic model will be a bang policy with only one switch if the cost function depicted in the illustration is utilized. Furthermore, a switch can only be activated if criteria are satisfied. As a result, we investigate the approaches for preventing the HBV model. Finally, we will develop a more realistic model-building strategy that integrates the emergence of treatment effect on the infectives during their incubation period for a fatal epidemic. A fatal epidemic is expected to be more severe than a general epidemic, and more realistic model-building approaches are developed. The same mathematical methods and conclusions may be used nearly immediately to a wide range of spreading processes, which should be stressed.
Keywords: discrete study, stochastic epidemic model, optimal control.
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