Multi-agent Model Analysis of the Containment Strategy for Avian Influenza (AI) in South Korea
Taehyong Kim; Woochang Hwang; Aidong Zhang; Sen, S.; Ramanathan, M.
Bioinformatics and Biomedicine, 2008. BIBM apos;08. IEEE International Conference on
Volume , Issue , 3-5 Nov. 2008 Page(s):335 - 338
Digital Object Identifier 10.1109/BIBM.2008.31
Summary:This research presents a multi agent model to estimate and predict the spread of Avian Influenza (AI) in various attributes and environments in a given population. AI can be transmitted by air and is a critical hazard to birds, especially chickens and ducks. The virus occurs naturally in birds and is capable of being transmitted from an infected bird to another. It is conceivable that AI could be a major threat to human health if the virus becomes capable of transmission to human beings. The next outbreak of AI could lead to millions of deaths unless a feasible strategy for AI containment can be developed.This paper focuses the flexibility that a multi agent system offers. Agent-based models can closely mimic the situations that exist in real system where several autonomous components may be interacting with each other. The modeling approach offers the advantage of examining the interactions between the agents. This research studies the interactions of three critical factors that characterize AI outbreaks. These properties are quarantine range, incubation period and infection probability. The multi agent model investigates the nature of spreading of AI by incorporating these three properties. We illustrate the potential benefits of multi agent modeling in containing the spreading of AI by presenting how efficiently the virus can be contained. Our work exploits data on the 2008 outbreak of AI in South Korea.
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