We determined the date a country reported its first WHOconfirmed 2009 A/H1N1 case through May 8 th , 2009. We chose this date in order to limit the analysis, as much as possible, to initial spread from Mexico, because it served as a natural breakpoint in the distributions of reporting dates, as well as being the date our initial analysis. We performed a survival analysis using R [30] , and used an accelerated life time model using a log-logistic distribution. We also examined using a scale-free exponential distribution, as opposed to a log-logistic distribution, which requires a scale parameter, but these models did not fit nearly as well, as measured by AIC. We followed Burnham and Anderson [31] , in using Akaike Information Criterion (AIC) to choose the model that best explains the data (i.e., the one with the lowest AIC, or equivalently DAIC, score). Additionally we provided the Akaike weights, which estimate the likelihood that a specific model is the true model, assuming that the true model is in the set of examined models [14] . Using this methodology, we choose to evaluate 22 models that made mechanistic sense including a null model for a reference. We did not include any models with only the indirect flight data, and without the direct flight data, because we feel that this does not make mechanistic sense. To reduce multicollinearity we included at most two socio-economic indicators.