The epidemic’s unexpected emergence had a tremendous influence on society and people’s daily lives. As a result, epidemic prevention and control have become a top priority for all governments. Therefore, how and why the disease has spread so quickly should be investigated. As a result, for a study, researchers provided an epidemic evolutionary game model with non-anxiety sensitive intervention, an undifferentiated population, and government disease control units.
Modeling of complicated networks and differential equations are used. First, the game matrix is generated based on the real circumstances, and the replication factor dynamic equation of the game matrix is examined. The network evolution model is then created for comparison analysis and scenario simulation using the game matrix and complicated network evolution model. Finally, sensitivity analysis and parameter optimization are described. The game’s assumptions and preconditions are referred to as parameter values. The study used the emotional regulation approach questionnaire developed by Gross and John, which includes two dimensions of expression inhibition and cognitive reappraisal and a total of ten items, to better examine the association between residents’ well-being and scenario simulation (4 questions of expression inhibition and 6 questions of cognitive reappraisal). The questionnaire has a seven-point grading system. In this measurement, the composite reliability of the expression inhibition dimension is 0.83, with a 95% CI estimated by the delta method of [0.81, 0.85], and the composite reliability of the cognitive reappraisal dimension is 0.83, with a 95% CI of [0.81, 0.85].
The final evolutionary stability strategy was calculated as “initiative & indulgence,” meaning that the number of active individuals reached a certain value or the social & economic cost was low, or as “mobility, prevention & control,” meaning that the government unit had strong control ability and could control the epidemic within a certain range. Then, using simulation, the negative impact of complex networks and the requirement of prevention and control were derived, and the favorable impact of fixed cost investment on epidemic prevention and control was discussed in detail. The replicating dynamic equations evolutionary stability technique was incompatible with the evolutionary game results of complex networks. In complicated networks, the outcome of an evolutionary game was not completely stable. The first and second-step regression coefficients are substantial. After re-evaluation of intermediary factors in the third phase, the influence of social support on teenagers’ anxiety levels remained considerable. As a result, it was demonstrated that re-evaluation played a partial intermediate function in the association between social support and anxiety level in teenagers, with an effective value of 0.07, accounting for 0.05-0.22 / – 0.05 = 22% of the entire impact. The intermediate effect analysis used the degree of teenage anxiety as the dependent variable, social support as the independent variable, and re-evaluation as the intermediary variable. About 5,000 was the bootstrap self-sampling number. The results demonstrated that the 95% CI for the re-evaluated mediating impact was [-0.376, -0.058], and 0 does not fall within the upper and lower bounds, showing that the anxiety level of the online game plays a mediating function.
The model can be used to improve the effectiveness of epidemic prevention and control. The evolutionary game model was used in the study to examine the game connection between the populace and government agencies during the pandemic. The model integrates the complex network with the epidemic evolutionary game and considers the individual’s strategy choice during the evolutionary process. The study discovered that whether or not people get infected was directly tied to their consciousness or mobility methods. The susceptibility, infection, recovery, or death (SIR) chamber model introduced in 1926, on the other hand, split the population into three categories. People are viewed as a whole in the evolutionary game. They will, however, make different decisions in various states. The subsequent study will be enhanced.
Reference: academic.oup.com/ijnp/article/25/Supplement_1/A1/6633402
