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http://dx.doi.org/10.11108/kagis.2018.21.2.094

A Study on the Agent Based Infection Prediction Model Using Space Big Data -focusing on MERS-CoV incident in Seoul-  

JEON, Sang-Eun (Smart Urban Research Department, Jungdo UIT)
SHIN, Dong-Bin (Dept. of Urban Information Engineering, Anyang University)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.21, no.2, 2018 , pp. 94-106 More about this Journal
Abstract
The epidemiological model is useful for creating simulation and associated preventive measures for disease spread, and provides a detailed understanding of the spread of disease space through contact with individuals. In this study, propose an agent-based spatial model(ABM) integrated with spatial big data to simulate the spread of MERS-CoV infections in real time as a result of the interaction between individuals in space. The model described direct contact between individuals and hospitals, taking into account three factors : population, time, and space. The dynamic relationship of the population was based on the MERS-CoV case in Seoul Metropolitan Government in 2015. The model was used to predict the occurrence of MERS, compare the actual spread of MERS with the results of this model by time series, and verify the validity of the model by applying various scenarios. Testing various preventive measures using the measures proposed to select a quarantine strategy in the event of MERS-CoV outbreaks is expected to play an important role in controlling the spread of MERS-CoV.
Keywords
Geospatial Big Data; MERSC; Community Spread; Agent Based Modeling(ABM);
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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