We find that hotspots are expected to influence disease dynamics significantly, even when infected groups do not travel to the hotspot at all. However, in this case, the hotspot effect strongly decreases as the distance between disease introduction and the hotspot increases. The reduction of mobility in infected groups also generates an unexpected spatio-temporal pattern: groups ranging at intermediate distance from the hotspot have the highest risk of infection, even if the disease is introduced immediately next to the hotspot. This counterintuitive result highlights the importance of understanding the behavioral effects Figure 2 . Influence of multiple model parameters on attack rate, when infected groups do not travel (Sick-stay model). The fraction of groups infected increases with the hotspot radius of attraction, but varies with the traveler-resident transmission probability P T (four lines in each graph), within-group transmission probability P w (three different columns of graphs), and between-neighbour transmission probability P B (four different rows of graphs). Each value is based on 1000 simulations in which disease was introduced randomly in one of the eigth groups adjacent to the hotspot. doi:10.1371/journal.pone.0031290.g002 of disease in wild animal populations. For example, as in humans, predicting the impact of hotspots on disease dynamics will strongly depend on understanding whether infectious individuals still travel to hotspots because disease symptoms appear after an infectious state (e.g., influenza H1N1 [6, 19] ), or whether infectious individuals do not visit hotspots because disease symptoms appear before the infectious state (e.g., SARS [34, 35] ). Furthermore, our results suggest that when transmission does not directly occur at hotspots, disease control measures targeting groups residing around the hotspot might not necessarily be the most efficient ones. Further simulation work is needed to identify optimal disease control measures. The habitat of wild animal populations often includes more than one hotspot. For example, the habitat of terrestrial mammals can include a small number of high-value hotspots attracting dozens of groups (e.g., salt licks or forest clearings) and more numerous low-value hotspots attracting only a few groups (e.g., fruiting trees). Our model reveals that, when groups are assumed to travel to their nearest hotspot, the impact of disease outbreaks is a bell-shaped function of the number of hotspots (Fig. 5) . This result challenges the hypothesis that the number of hotspots and disease prevalence will correlate positively [8] , and could be used to optimize strategies for controlling disease in wild animal populations. Thus, wildlife managers may consider increasing, rather than decreasing [36] , the number of water holes in order to reduce the number of highly-connected individuals or social groups, and hence the impact of an outbreak. However, additional studies are needed to determine if our result still holds when each animal visits more than one hotspot.