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Use of GIS and agent-based modeling to simulate the spread of influenza

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5 Author(s)
Jiasheng Wang ; Sch. of Tourism & Geogr. Sci., Yunnan Normal Univ., Kunming, China ; Jianhong Xiong ; Kun Yang ; Shuangyun Peng
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The propagation of influenza through a population is an inherent spatial-temporal process of great importance for modern society. So a spatial explicit epidemiologic model of flu is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. The objective of this study is to develop an agent-based modeling (ABM) approach that integrates geographic information system (GIS) to simulate the spread of influenza in an urban environment, as a result of individuals' interactions in a geospatial context. The methodology for simulating spatiotemporal dynamics of influenza propagation is presented. Based on Repast Simphony platform, and applying Java and GIS tools, this paper designs and develops a prototype system to carry out agent-based simulation of influenza transmission and control in a geographic environment. The model is implemented using the H1N1 outbreak in Kunming, Yunnan province, southwest of china in 2009 as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. In the model, influenza is described with the mathematic relation between transmission probability, the distance of two individuals, latent period, the time of infected to die, cure rate and other parameters. User can change the simulation results through adjusting the value of time to hospital and so on. The results show that the GIS-agent based model designed for this study can be easily customized to study the spread dynamics of any other influenza by simply adjusting the disease parameters. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention a- - nd control of an influenza outbreak.

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Geoinformatics, 2010 18th International Conference on

Date of Conference: 18-20 June 2010

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