We estimated the initial growth rate of the epidemic (r) by fitting an exponential curve to incidence data using a generalized linear model. We compared Akaike's Information Criterion values to determine the appropriate error structure (Poisson or negative binomial). The choice of which part of the epidemic curve the model should be fit to was subjective, therefore the model was fit to all possible sections of the epidemic curve (using a minimum of nine consecutive months) and the median, the 2.5th and the 97.5th percentile of the R 0 estimates are presented in Table 1 . Figure 3B (inset) shows that the estimate of R 0 was robust to the interval chosen for fitting the curve. We used the same method to estimate R 0 from data that we had compiled on outbreaks of canine rabies from elsewhere in the world. For these time series, we fitted exponential curves to the intervals between the first recorded case and the month (or week) with highest rabies incidence (Table 2 ) and converted the estimated growth rates to estimates of R 0 using the serial interval distribution data gathered by contact tracing in Tanzania. For partly vaccinated populations, we corrected our R 0 estimates by dividing by the fraction of dogs which were vaccinated prior to the outbreak [12] . For all the outbreaks considered, including those in Tanzania, some localized and individual control measures may have been instituted (such as tying up or killing infected animals), and therefore our R 0 estimates should be regarded as lower bounds. However simulations also revealed that for very low values of R 0 (,1.2), estimates from the epidemic trajectory can be slightly biased upwards ( Figure S2 ). This is probably because at very low levels of R 0 , most introductions do not initiate further cases and therefore a small number of individuals with higher than average biting behaviour are needed to trigger epidemics, thus biasing trajectories. The effective reproductive number R. The effective reproductive number R measures the average number of secondary cases per primary infection once an epidemic is underway. R changes through space and time depending upon the implementation of control measures, the depletion of susceptibles and the build-up of local correlations in the spatial distribution of infected and susceptible individuals. Numbers of secondary cases per rabid dog (inferred from the epidemic tree reconstructions) were calculated monthly and averaged across bootstrapped trees to give a time-varying estimate of R ( Figure 3C ). Although R declined through time in both districts, there was no apparent temporal trend in the biting behaviour of rabid dogs ( Figure S3 ), suggesting that domestic dog vaccination was the main factor responsible for reducing transmission.