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Massive growth in human mobility has dramatically increased the risk and rateof pandemic spread. Macro-level descriptors of the topology of the WorldAirline Network (WAN) explains middle and late stage dynamics of pandemicspread mediated by this network, but necessarily regard early stage variationas stochastic. We propose that much of early stage variation can be explainedby appropriately characterizing the local topology surrounding the debutlocation of an outbreak. We measure for each airport the expected force ofinfection (AEF) which a pandemic originating at that airport would generate. Weobserve, for a subset of world airports, the minimum transmission rate at whicha disease becomes pandemically competent at each airport. We also observe, fora larger subset, the time until a pandemically competent outbreak achievespandemic status given its debut location. Observations are generated using ahighly sophisticated metapopulation reaction-diffusion simulator under adisease model known to well replicate the 2009 influenza pandemic. Therobustness of the AEF measure to model misspecification is examined bydegrading the network model. AEF powerfully explains pandemic risk, showingcorrelation of 0.90 to the transmission level needed to give a disease pandemiccompetence, and correlation of 0.85 to the delay until an outbreak becomes apandemic. The AEF is robust to model misspecification. For 97% of airports,removing 15% of airports from the model changes their AEF metric by less than1%. Appropriately summarizing the size, shape, and diversity of an airport'slocal neighborhood in the WAN accurately explains much of the macro-levelstochasticity in pandemic outcomes.

Stochasticity in pandemic spread over the World Airline Network explained by local flight connections