A Computational Model of Mitigating Disease Spread in Spatial Networks

Authors:
Taehyong Kim;Kang Li;Aidong Zhang;Surajit Sen;Murali Ramanathan
Affiliations:
State University of New York at Buffalo, USA;State University of New York at Buffalo, USA;State University of New York at Buffalo, USA;State University of New York at Buffalo, USA;State University of New York at Buffalo, USA
Venue:
International Journal of Artificial Life Research
Year:
2011

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Abstract

This study examines the problem of disease spreading and containment in spatial networks, where the computational model is capable of detecting disease progression to initiate processes mitigating infection spreads. This paper focuses on disease spread from a central point in a 1 x 1 unit square spatial network, and makes the model respond by trying to selectively decimate the network and thereby contain disease spread. Attention is directed on the kinematics of disease spreading with respect to how damage is controlled by the model. In addition, the authors analyze both the sensitivity of disease progression on various parameter settings and the correlation of parameters of the model. As the result, this study suggests that the radius of containment process is the most critical parameter and its best values with the computational model would be a great help to reduce damages from disease spread of a future pandemic. The study can be applied to controlling other virus spread problems in spatial networks such as disease spread in a geographical network and virus spread in a brain cell network.