Predict SARS infection with the small world network model

Guoji Lin, Xun Jia, Q. Ouyang

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)66-69
Number of pages4
JournalBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
Volume35 Suppl
StatePublished - 2003

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Predict SARS infection with the small world network model. / Lin, Guoji; Jia, Xun; Ouyang, Q.

In: Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences, Vol. 35 Suppl, 2003, p. 66-69.

Research output: Contribution to journalArticle

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