Inferring pandemic growth rates from sequence data

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

Significantly, repeating all of the above, but this time only sampling sequences (still randomly and log-proportionally) from generations in which there are more than 100 sequences (figure 5b) results in much improved parametric and non-parametric estimates of growth rate than when sampling all generations. This is because a key assumption of the coalescent is broken when the number of infected individuals is small. Kingman's formulation of the coalescent assumes a maximum of one coalescent event in the sample per generation, meaning that the sample has to be a small proportion of the total population. During the early generations, a small number of infected individuals (or sequences in the population) are simulated. Here, the population is small, and therefore the sampling density high, explaining the poorer estimates in figure 5a.