A model for predicting the spread patterns of human and computational epidemics on complex temporal networks

In this paper, a novel model for predicting the spread patterns of human and computer epidemics on the basis of complex temporal networks is presented. This research addresses existing challenges in forecasting behaviors and crisis escalation by analyzing the dissemination of viruses and malware via cumulative data and related behavioral patterns. The proposed model employs the analysis of historical data and temporal dependencies to simulate various interactions between networks and temporal influences, thereby predicting crisis scenarios. The proposed model, which considers various interactions between networks and temporal influences, is capable of better simulating nonlinear and complex behaviors. This innovation can assist researchers and decision-makers in developing more effective strategies for managing health and security crises. Ultimately, this research provides a deeper understanding of the processes underlying the spread of diseases and malware and can serve as a foundation for future studies in this field.