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Editors contains: "Pacheco, Diego"

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  1. ; ; ; (, GoodIT 2022: {ACM} International Conference on Information Technology for Social Good, Limassol, Cyprus, September 7 - 9, 2022)
    Mourlas, cotas; Pacheco, Diego; Pandi, Catia (Ed.)
    We present an individual-centric agent-based model and a flexible tool, GeoSpread, for studying and predicting the spread of viruses and diseases in urban settings. Using COVID-19 data collected by the Korean Center for Disease Control & Prevention (KCDC), we analyze patient and route data of infected people from January 20, 2020, to May 31, 2020, and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of population mobility and is used to parameterize GeoSpread to capture the spread of the disease. We validate simulation predictions from GeoSpread with ground truth and we evaluate different what-if counter-measure scenarios to illustrate the usefulness and flexibility of the tool for epidemic modeling. 
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