  We study how international flights can facilitate the spread of an epidemic
to a worldwide scale. We combine an infrastructure network of flight
connections with a population density dataset to derive the mobility network,
and then we define an epidemic framework to model the spread of the disease.
Our approach combines a compartmental SEIRS model with a graph diffusion model
to capture the clusteredness of the distribution of the population. The
resulting model is characterised by the dynamics of a metapopulation SEIRS,
with amplification or reduction of the infection rate which is determined also
by the mobility of individuals. We use simulations to characterise and study a
variety of realistic scenarios that resemble the recent spread of COVID-19.
Crucially, we define a formal framework that can be used to design epidemic
mitigation strategies: we propose an optimisation approach based on genetic
algorithms that can be used to identify an optimal airport closure strategy,
and that can be employed to aid decision making for the mitigation of the
epidemic, in a timely manner.
