A great number of infectious diseases of humans spread directly from one person to another person, and early work on the spread of such diseases has been based on the assumption that every infected individual is equally likely to transmit the disease to any susceptible individual in a population. One of the most important consequences of incorporating network structure into epidemic models was the demonstration that heterogeneity in the number of contacts (degree) can strongly affect how R 0 is calculated [12, 13, 34] . Thus, the same disease can exhibit markedly different epidemic patterns simply due to differences in the degree distribution. Our results extend this finding and show that even in networks with the same degree distribution, fundamentally different epidemic dynamics are expected to be observed due to different levels of community structure. This finding is important for various reasons: first, community structure has been shown to be a crucial feature of social networks [15, 16, 17, 19] , and its effect on disease spread is thus relevant to infectious disease dynamics. Furthermore, it corroborates earlier suggestions that community structure affects the spread of disease, and is the first to clearly isolate this effect from effects due to variance in the degree distribution [43] . Second, and consequently, data on the degree distribution of contact networks will not be sufficient to predict epidemic dynamics. Third, the design of control strategies benefits from taking community structure into account.