Inferring pandemic growth rates from sequence data

de Silva, Eric; Ferguson, Neil M.; Fraser, Christophe

Recently, Stack et al. [16] have performed a similar exercise for seasonally fluctuating influenza epidemics, and showed how different sampling protocols can influence inferred transmission dynamics in BSPs. In the case of seasonal epidemics, they point out the importance of temporal sampling, especially where populations undergo bottlenecks and the number of lineages are substantially reduced from one season to the next. BSPs inferred from samples taken after a bottleneck are unable to recover transmission dynamics prior to the bottleneck. Their analysis indicates that single-generation sampling or sampling randomly about a target generation as the epidemic begins to slow down is the most effective strategy. Also of interest is their observation that joining together successive BSPs inferred over shorter timescales was more representative of changes in population size than a single BSP over a longer time period, which they suggest may be owing to poorly specified prior probabilities in BEAST. Certainly for longer-term population dynamics strongly influenced by selection from the immune system or vaccination, the use of coalescent methods becomes questionable. For the work presented here, we adopted a branching process model to simulate the early stages of an epidemic. The model is parametrized by the basic reproduction number, R, which determines the rate of (exponential) growth of the epidemic, a dispersion parameter, k, which quantifies heterogeneity in infectiousness between individuals, and the generation time. We incorporate a realistic mutational model so that starting from a sequence of one of the early isolates, we simulate sequences collected over a specified number of generations of spread in the epidemic. We then compare these to assess how well coalescent methods are able to reliably estimate the true dynamics of the simulated epidemic.