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Bayesian reconstruction of a spatially heterogeneous epidemic: Characterising the geographic spread of pandemic infection in England
Paul Birrell (Public Health England & MRC Biostatistics Unit, University of Cambridge)
Understanding how the geographic distribution of, and movements within, a population influence the spatial spread of infections is crucial for the design of interventions to curb transmission. Existing knowledge is typically based on results from simulation studies whereas analyses of real data remain sparse. The main difficulty in quantifying the spatial pattern of disease spread is the paucity of available data together with the challenge of incorporating the limited information into models of disease transmission. To address this challenge, the role of routine migration on the spatial pattern of infection during a pandemic outbreak in England is investigated here through two modelling approaches: parallel-region models, where epidemics in different regions are assumed to occur in isolation with shared characteristics; and meta-region models where inter-region transmission is expressed as a function of the commuter flux between regions. Results when applied to 2009 A/H1N1 \"swine flu\" data highlight that the significantly less computationally demanding parallel-region approach is sufficiently flexible to capture the underlying dynamics. This suggests that inter-region movement is either inaccurately characterized by the available commuting data or insignificant once its initial impact on transmission has subsided.