Despite its place at the forefront of mathematical epidemiology, the concept of R 0 is deeply flawed. Defining R 0 proves to be significantly more difficult than it appears. Few epidemics are ever observed at the moment an infected individual enters a susceptible population, so calculating the value of R 0 for a specific disease relies on secondary methods. There are many methods to calculate R 0 from mathematical models, few of which agree with each other and few of which produce the average number of secondary infections. Methods to calculate R 0 from theoretical models include the survival function, the next-generation method, the eigenvalues of the Jacobian matrix, the existence of the endemic equilibrium, and the constant term of the characteristic polynomial. R 0 can also be estimated from epidemiological data via the number of susceptibles at endemic equilibrium, the average age at infection, the final size equation and calculation from the intrinsic growth rate. For an overview, see Heffernan et al. [2] .