Recently the problem of real time monitoring of (emerging) communicable diseases has gained growing attention, with a few new methods proposed to estimate R. One method proposes the analysis of the distribution of the sizes of case clusters to provide indications of changes in R. Specifically, increases in R(,1) translate on average into larger case cluster sizes [21, [27] [28] [29] . Another approach [30] relies on the inference of probable chains of transmission among observed cases from knowledge of the statistical distribution of the infectious period. From an ensemble of such chains and their associate compounded probability, R can be estimated. This method has recently been applied to ''real time'' monitoring of SARS [31, 32] , via a Bayesian inference scheme. The strength of this class of methods is that they allow insights into heterogeneities in the population. This demands the consideration of all pairs of possible transmissions, which may become computationally intense as case numbers rise and can be sensitive to under reporting, competing risk and to the details of the distribution of infectious periods. Moreover those studies considered the efficacy of control measures for a disease with an initial R.1 and no new cases introduced during the epidemic, whereas it is typical of emerging communicable diseases that adaptation of the pathogen's tropism to the host population is the result of numerous such introductions [22] .