To deal with the dynamics model of spread of viruses, Sun et al. [41] proposed the disseminated FMD with an incubation duration fixed and non-localized infected to look for efficient control strategies. This is why the use of a delay in time to examine the effects that these phenomena cause is frequently employed for computer-based networks as well as biology [24-25] within numerous areas.1 Kouidere et al. [42] have proposed an optimal control method using delays in the control and state variables. Zheng et al. [27suggested a two-strain delayed model that calculated its threshold as well as the point at which the model reaches equilibrium.

Some measures have been suggested in the literature on ways to stop the current spreading of COVID-19.1 Wu [28] has developed a distributed and nonlinear latent delay model based on SIR and examined the traveling waves that occur at the equilibrium of the model. In addition masks, vaccination and wearing masks are proven to be effective. Based on COVID-19 as a background, Khan et al. [29] devised models that include random perturbations, as well as delay times and discovered the necessary conditions for the end of this virus.1 Based on the application in the New York City policy, Ma and co. [43] created an active model that includes mask coverage effective to study the effect of using masks during the COVID-19 outbreak. In the same way, Rihan [30] proposed an SIAQR delay model, and focused on the spread of the virus among populations.1

Ruhomally and co. [44] created an automated cellular system (CA) which describes the dynamics of COVID-19. Xia et al. [31] examined the effect of a slow recovery and inconsistent spreading on the transmission of disease in organized populations. They also examined the effects of contact tracing and vaccine on the reproduction rate of two species.1 Chen et al. [32] have developed an improved model for spreading rumors by analyzing the effect of delay in an interconnected system. Economic efficiency and cost were thought of as factors to control and prevent the spread of COVID-19.

Through the development of methods that are correlated the study may be able to reduce the spread of rumor.1 Asamoah et al. [45] proposed an autonomous model of nonlinear deterministic models to analyze COVID-19’s management to evaluate the economic health and cost results of the model autonomously developed by Saudi Arabia. Zhang et al. [33] developed a time delay model in which public opinion changed and studied the effect of delay on equilibrium point.1 Kingdom of Saudi Arabia.

Hu [34] studied the spread of rumors of reaction by using time delay as with their variations using complex networks. The outbreak isn’t going away because of the mutation in COVID-19. She also examined the spread of rumors around equilibrium points and it’s Turing bifurcation.1 To determine the future course of COVID-19, Massard et al. [46] created an algorithm to study the effects of three distinct SARS-CoV-2 variations on the spread of COVID-19 in France from January through May 2021 (before vaccination was made available to all of the population).

Emergencies cause the spread of panic and uncertainty and the government needs to implement decisive measures, for example, the release of official information or repressing the use of the use of force.1 The models of time-delay-related viral and time-delay rumors as well as the optimal control models were examined in the above paragraphs. Implementation of such steps can be described as the optimal control dilemma. However, the model for spreading panic in time-delayed emergency situations was not discussed.1 The objective is to employ the least cost to manage crises. In reality emotions influence on the behavior of people and, in particular, anxiety. Bolzoni et al. [35] examined the issue of controlling time for an outbreak model, and also analyzed how the best strategy to lower the risk of transmission.1

In addition emotions possess three distinct characteristics which include holistic, process and individual variability, of which individual variation is the most prominent characteristic. Grandits [36] looked into a stochastic model of epidemic control and employed an HJB equation to determine optimal strategies for controlling.1 Individual differences in emotional expression are determined by the personality of the individual.

Dai [37] analyzed the semigroup theory as well as minimizing sequences to demonstrate existence and some estimations of the exclusive powerful solution and the optimal pair of optimal control issues as well as.1 Different people have different emotions perception capabilities. Hang et al. [38] developed an optimal control of the avian flu model using delay and analysed the results with the help of Pontryagin’s maxima principle. Most of the time, those who are anxious have a tendency to be emotionally affected and irrational however, calm and rational people are more rational.1 Bashier [39] proposed an optimal control strategy using delay differential equations based upon the SIR epidemic model, and then investigated the sensitivity of two methods to time delays.

So, it is essential to be aware of the different personalities of individuals personalities in spreading anxiety in the face of emergencies.1 Wu [40] examined problems of nonlinear optimal control that require multiple delays employing gradient-based optimization techniques. This is a great way to simulate the process of emotional expression in actual life. To deal with the dynamics model of spread of viruses, Sun et al. [41] proposed the disseminated FMD with an incubation duration fixed and non-localized infected to look for efficient control strategies.1

Thus, it is of major theoretical and practical value to investigate the effects of delay in time on the spreading of panic. Kouidere et al. [42] have proposed an optimal control method using delays in the control and state variables. The remainder of this research is structured in the following manner in Section 2.1 an algorithm for time-delayed spreading of panic is discussed. Some measures have been suggested in the literature on ways to stop the current spreading of COVID-19.

In Section 3., the local and general stability of the two equilibriums are studied through mathematical analysis. In addition masks, vaccination and wearing masks are proven to be effective.1 We formulate the optimal control model, and we solve crucial conditions for an optimally designed solution using the maxima principle of Pontryagin in Section 4. Based on the application in the New York City policy, Ma and co. [43] created an active model that includes mask coverage effective to study the effect of using masks during the COVID-19 outbreak.1

In Section 4, the theoretical outcomes of analysis using numerical simulation are described in Section 5. Ruhomally and co. [44] created an automated cellular system (CA) which describes the dynamics of COVID-19. A brief summary is presented in Section 6. They also examined the effects of contact tracing and vaccine on the reproduction rate of two species.1