Infectious diseases cause global human and economic losses, exacerbated by intertwined physical-cyber information spread. In this paper, we refine a propagation dynamics model that integrates the coupled spread of infectious diseases and information in both physical and cyber domains. Specifically, we account for the influence of individual heterogeneity at the network level on epidemic thresholds. The proposed model is validated using real-world epidemiological data from an outbreak in the United States, demonstrating its applicability and accuracy in capturing disease transmission dynamics. This study examines how individual differences in response to information influence the effectiveness of infectious disease prevention. The findings reveal that when negative aware toward vaccination are prevalent, the coupling of cyber and physical information propagation leads to a lower recovery rate, resulting in the epidemic peak earlier. By incorporating individual-specific vaccination intentions, our two-layer model enhances infectious disease prediction in data-driven contexts. It further provides critical insights for outbreak risk management and prevention.