Hybrid agent-based and markov chain simulation approach for modeling epidemiological dynamics

This study introduces an innovative approach for modeling infectious diseases through hybrid simulations, combining agent-based models and Markov chains. These simulations allow for the accurate evaluation of disease spread and intervention strategies in an accessible manner. The hybrid model incorporates key parameters such as infection and recovery rates, along with transitions between health states. By varying these parameters, the results can mirror those of classic models like SIS, SIR, SEAIR and VSEIQR. Through our simulations, we analyzed how changing these parameters impacts disease transmission, demonstrating the stochastic nature of epidemics. Infectious diseases remain a significant cause of global mortality, influenced by pathogen characteristics and public health interventions. The findings highlight that hybrid simulations are a powerful tool for understanding and predicting epidemiological behaviors, offering valuable insights into epidemic control and providing an accessible framework for educational and research purposes.