[Predict SARS infection with the small world network model].

Guoji Lin; Xun Jia; Qi Ouyang

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PubMed · 12914222

[Predict SARS infection with the small world network model].

Guoji Lin·Xun Jia·Qi Ouyang

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Abstract

We report of our numerical simulation of SARS infection dynamics with the small world network model. The negative feedback mechanism and the effect of information flow are added in the model. The simulation fits well with the observed data. The main results of our simulation are that the feedback mechanism can effectively slow down the SARS infecting rate, but it may cause sustained oscillations in number of infection cases. Moreover, keeping the transparency of information is a key factor to resist SARS in the society.

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Computer Simulation Humans Models, Theoretical Neural Networks, Computer Severe Acute Respiratory Syndrome Journal Article

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Guoji Lin, Xun Jia, Qi Ouyang. 2003-05-31. [Predict SARS infection with the small world network model].. https://pubmed.ncbi.nlm.nih.gov/12914222/

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