Traditional epidemiological models often assume homogeneous mixing of populations, which can oversimplify real-world dynamics. Network science provides a more nuanced approach by accounting for the heterogeneity of social networks. This can lead to better predictions of infection rates and more targeted intervention strategies. By analyzing network structures, such as scale-free networks or small-world networks, scientists can identify critical nodes or "super-spreaders" that disproportionately affect disease spread.
