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Measuring the Potential of Individual Airports for Pandemic Spread over the World Airline Network

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44895

Lawyer,  Glenn
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Lawyer, G. (2016). Measuring the Potential of Individual Airports for Pandemic Spread over the World Airline Network. BMC Infectious Diseases, 16(1): 70. doi:10.1186/s12879-016-1350-4.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0029-C693-A
Zusammenfassung
ABSTRACT: BACKGROUND: Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of this early stage variation can be explained by appropriately characterizing the local network topology surrounding an outbreak’s debut location. METHODS: Based on a model of the WAN derived from public data, we measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate, assuming an epidemic process which transmits from airport to airport via scheduled commercial flights. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the underlying model WAN. RESULTS: AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97 % of airports, removing 15 % of airports from the model changes their AEF metric by less than 1 %. CONCLUSIONS: Appropriately summarizing the size, shape, and diversity of an airport’s local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.