The analysis of the gradient of infection of the SIS model has the natural advantage of showing the effect of adaptive networks in a one-dimensional simplex (the fraction of infected). Yet, an analogous result holds for the SIR model. The gradient of infection now also depends on the number of recovered (r) individuals in the population and, once again, allows us to identify when disease expansion will be favored or not. Figure 2 gives a complete picture of the gradient of infection, using the appropriate simplex structure in which all points satisfy the relation i+r+s = N. The dashed red line indicates where G i,r ð Þ~0 in case individuals do not have any information about the health status of their contacts, i.e., links that involve infected individuals disappear at the same rate as those that do not (b I~bH ). Disease expansion is more likely than disease contraction (G i,r ð Þw0) when the population is in a configuration above the line, and less likely otherwise. Similarly, the solid blue line indicates where G i,r ð Þ~0 whenever individuals share information about their health status, and use it to avoid contact with infected. Once again, the availability of information modifies the disease dynamics, inhibiting disease progression for a broad range of configurations.