Xinghua Chang

Infectious diseases not only pose a threat to human health, but also have an impact on social order and economic stability. Given the complexity and dynamism of the transmission process of infectious diseases, single dimensional information is difficult to provide comprehensive and accurate understanding of infectious diseases when characterizing transmission patterns and evolutionary characteristics. Therefore, it is crucial to delve into the interaction mechanism of multiple types of information, including positive, negative, and policy regulatory information, in the process of infectious disease transmission. Based on this, this paper innovatively constructs an infectious disease transmission model based on multidimensional media information interaction, which fully considers the heterogeneity characteristics of networks blocks and the co-evolution between information transmission and disease transmission. First, a two-layer infectious disease networks model based on multidimensional media information influence and regional heterogeneity was constructed. The basic reproduction number [ ] of the model was derived by using methods such as differential equation dynamics and matrix analysis. And with the help of Lyapunov function stability theory and Lasalle invariance principle, the dynamic properties of disease-free equilibrium points and endemic equilibrium points were rigorously proved. Second, in order to achieve effective intervention and optimized control of the transmission process of infectious diseases, the optimal control equation for infectious disease transmission was designed with the objective function of minimizing the number of infected people and control costs. The numerical solution of the optimal control strategy was obtained by introducing the Pontryagin maximum principle and using numerical algorithms. Finally, a series of numerical simulation experiments were conducted to verify the effectiveness and accuracy of the theoretical analysis of the model. Therefore, it is of great significance to reasonably guide and regulate the dissemination characteristics and content of multidimensional media information in order to achieve effective prevention and control of infectious diseases.

The spread of infectious diseases poses significant threats to public health, the economy, and society as a whole. Despite governmental control measures over individual behavior, the public might still be influenced by factors such as costs, expected benefits, and the behavior of others, leading to incomplete adherence to disease control measures. Therefore, this paper proposes a behavioral game theory based infectious disease model on two-layer networks. First, considering the dynamic interaction between individual awareness behavior and disease spreading, a two-layer coupled network of individual behavioral awareness and disease spreading is established. Second, game theory is used to describe the impact of relevant factors on individual awareness behavior. The first layer represents the individual protective behavior game layer, while the second layer represents the disease spreading layer. Government intervention in individual behavior is also considered in the two-layer network model, according to the situation of infectious disease spreading, a threshold model is introduced to control individual protective behavior. Finally, MMCA is used to analyze the disease spreading threshold, and the proportion of the final population state and the spreading threshold under different model parameters are analyzed. The results show that by reducing personal protective costs, increasing individual attention to disease information, and enhancing governmental adjustments to disease control measures, the outbreak threshold of disease spreading can be effectively increased.

Media coverage plays an important role in prevention and control the spread of COVID-19 during the pandemic. In this paper, an SIHRS model of COVID-19 pandemic with impulse and time delay under media coverage is established. The positive and negative emotions of public are considered by the impact of confirmed cases and medical resources. In order to restrain the negative information of public, the factor of policies and regulations with impulse and time delay is introduced. Furthermore, the system model is simulated and verified by the reported data of COVID-19 pandemic in Wuhan. The main results are as follows: (1) When the implementation rate of the negative information generated by the confirmed cases gradually reduced to 0.4 times, the cumulative confirmed cases will be significantly reduced to about 37000, indicating that the popularization of pandemic related media information should be broad; (2) When the implementation rate affected by the amount of policies and regulations information gradually increases to 3 times, the cumulative confirmed cases will be significantly reduced to about 28000, indicating that the policies and regulations information should be continuously and incrementally reported; (3) When the inhibition rate of policies and regulation information on negative information gradually increases to 3 times, the cumulative confirmed cases will also be significantly reduced to about 27000 cases, indicating that the targeted policies and regulations information has a significant impact on inhibiting the corresponding negative emotions.