2015/04/22 by Glenn Lawyer
Computer Science · Economics, Econometrics and Finance · Mathematics · Medicine · Physics and Astronomy · #Aviation Industry Analysis and Trends #Biology #COVID-19 epidemiological studies #Complex Network Analysis Techniques #Computer network #Computer science #Coronavirus disease 2019 (COVID-19) #Demography #Disease #Econometrics #Economics #Epidemic model #Geography #Infectious disease (medical specialty) #Influenza pandemic #Mathematics #Medicine #Metapopulation #Metric (unit) #Network topology #Operations management #Operations research #Outbreak #Pandemic #Population #Robustness (evolution) #Telecommunications #Transmission (telecommunications) #cs.SI #physics.soc-ph
paper · pdf · doi:10.1186/s12879-016-1350-4
article text: 6 pages, 5 figures, 28 references
arxiv created 2015/04/22 · openalex publication_date 2015/12/01 · arxiv updated 2016/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.