title: [Predict SARS infection with the small world network model].
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.
authors.affiliation: Laboratory of Nonlinear Science, School of Physics, Center for Theoretical Biology, Peking University, Beijing 100871, China.
authors.forename: Guoji
authors.initials: G
authors.lastname: Lin
authors.forename: Xun
authors.initials: X
authors.lastname: Jia
authors.forename: Qi
authors.initials: Q
authors.lastname: Ouyang
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D045169:Severe Acute Respiratory Syndrome / Q000635:transmission*