As computational resources modernize, physicists have become increasingly interested in understanding the dynamics of disease spread. The problem is essentially one of statistical physics, where the theory of phase transitions and critical phenomena and the tools of numerical simulations enable physicists to predict how epidemic outbreaks will evolve in time [1].

Most recent progress has considered diseases that die out quickly, but when persistent infections occur in a population, individuals may be infected but not infectious (i.e., “latently” infected) for long periods of time—even a lifetime. From a technical point of view, this seemingly minor aspect implies the system is open, since newborns enter the dynamics and some individuals die for causes not directly related to the disease. Now, in a paper appearing in *Physical Review E*, Joaquín Sanz, L. Mario Floría Peralta, and Yamir Moreno Vega at the Universidad de Zaragoza, Spain, report on the threshold for an epidemic to occur for persistent diseases. In particular, they consider one of the most threatening cases, tuberculosis.

Using the tools of network theory to describe the way individuals interact and infect one another, Sanz *et al.* show that the epidemic threshold depends on the distribution in the connectivity of the network nodes; namely, the ratio between the mean and standard deviation of this distribution. Although this result is similar to what occurs in diseases without latent periods, the techniques and ideas Sanz *et al.* have introduced represent an important step towards the modeling of persistent infections that will be useful in more general contexts. – *Alex Arenas*

[1] I. B. Schwartz and L. B. Shaw, Physics 3, 17 (2010).