Analyses of the contact-tracing data generated robust estimates of epidemiological parameters that have important implications for rabies control (Table 1 , Figures 2 and 3 , and Figure S1 ) and provide insight into how infectious disease transmission scales from individual behaviour to populationlevel dynamics. We estimated R 0 for rabies in Serengeti and Ngorongoro districts directly from infectious histories, from reconstructed epidemic trees based on the spatiotemporal proximity of cases, and from the exponential rate of increase in cases at the beginning of an epidemic. Biting behaviour of rabid dogs during the course of infectious periods was highly variable (mean bites per rabid dog ¼ 2.15, 95% confidence interval (CI) from fitting a negative binomial distribution: 1.95-2.37; variance ¼ 5.61, CI: 4.63-6.92; shape parameter k ¼ 1.33; CI: 1.23-1.42) ( Figure 3A ). The probability that an unvaccinated dog developed rabies after being bitten by an infectious animal was high (P rabiesjbite ¼ 0.49, CI: 0.45-0.52) ( Table 1) if the bitten dog was not vaccinated or killed immediately after exposure. Multiplying the average number of dogs bitten per rabid dog by the probability of developing rabies following exposure gave an R 0 estimate of 1.05 (CI: 0.96-1.14) ( Figure 3A and Table 1 ). These estimates should be regarded as lower bounds, because not all transmission events were observed (this calculation excludes rabid dogs that were killed before biting other animals or that disappeared and likely corresponded to unknown or unobserved rabid dogs in other areas; see Materials and Methods). Detailed data on the timing and location of transmission events and infections allowed us to estimate the spatial infection kernel and generation interval (distances and times between source cases and their resulting infections, respectively) ( Figure 2) and 