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Computer Sciences, Epidemiology
Socioeconomic status can have an important effect on health. In this paper we: (i) propose using house price data as a publicly available proxy for socioeconomic status to examine neighbourhood socioeconomic status at a more fine grained resolution than is available in Irish Central Statistics Office data; (ii) use a dissimilarity index to demonstrate and measure the existence of socioeconomic clustering at a neighbourhood level; (iii) demonstrate that using a standard ABM initialisation process based on CSO small area data results in ABMs systematically underestimating the socioeconomic clustering in Irish neighbourhoods; (iv) demonstrate that ABM models are better calibrated towards socioeconomic clustering after a segregation models has been run for a burn-in period after initial model setup; and (v) that running a socieconomic segregation model during the initiation of an ABM epidemiology model can have an effect on the outbreak patterns of the model. Our results support the use of segregation models as useful additions to the initiation process of ABM for epidemiology.
Hunter, Elizabeth, Mac Namee, Brian and Kelleher, John D. (2018) 'Using a Socioeconomic Segregation Burn-in Model to Initialise an Agent-Based Model for Infectious Diseases' Journal of Artificial Societies and Social Simulation 21 (4) 9 . doi: 10.18564/jasss.3870