Inferring pandemic growth rates from sequence data

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

The branching process model that we used to simulate epidemic data is deliberately simple, and lacks a number of important evolutionary and epidemiological features-notably population structure and selection. However, early in a pandemic when there is exponential growth of incidence (i.e. when numbers of infected are a very small proportion of total population size), we would not expect selection to play a major role. In addition, molecular analysis of the substitutions seen in the H1N1 virus in the first weeks of the epidemic give little support to the hypothesis that there were significant phenotypic changes that may have affected fitness. This is our major justification for assuming neutrality in the work presented here --but it should be noted that such an assumption would be expected to have more dubious validity later in a pandemic, when immunity, vaccine and antiviral use influence viral evolution. Of course, the presence of selection would also limit the applicability of coalescent methods to infer population dynamics. Population structure would also not be expected to have a major impact very early in an epidemic, even in earliest affected communities, incidence by the end of May would not have been expected to have grown to the extent that epidemic growth rates would have slowed in some geographical areas but not others. Microstructure (e.g. households and local contact networks) is crudely represented in our model by allowing for highly over-dispersed offspring distributions.