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Pengliang Shi

Publications and source records attributed to Pengliang Shi.

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Plausible models for propagation of the SARS virus

Using daily infection data for Hong Kong we explore the validity of a variety of models of disease propagation when applied to the SARS epidemic. Surrogate data methods show that simple random models are insufficient and that the standard epidemic susceptible-infected-removed model does not fully account for the underlying variability in the observed data. As an alternative, we consider a more complex small world network model and show that such a structure can be applied to reliably produce simulations quantitative similar to the true data. The small world network model not only captures the apparently random fluctuation in the reported data, but can also reproduces mini-outbreaks such as those caused by so-called ``super-spreaders'' and in the Hong Kong housing estate Amoy Gardens.

q-bio.PE

Modelling of SARS for Hong Kong

A simplified susceptible-infected-recovered (SIR) epidemic model and a small-world model are applied to analyse the spread and control of Severe Acute Respiratory Syndrome (SARS) for Hong Kong in early 2003. From data available in mid April 2003, we predict that SARS would be controlled by June and nearly 1700 persons would be infected based on the SIR model. This is consistent with the known data. A simple way to evaluate the development and efficacy of control is described and shown to provide a useful measure for the future evolution of an epidemic. This may contribute to improve strategic response from the government. The evaluation process here is universal and therefore applicable to many similar homogeneous epidemic diseases within a fixed population. A novel model consisting of map systems involving the Small-World network principle is also described. We find that this model reproduces qualitative features of the random disease propagation observed in the true data. Unlike traditional deterministic models, scale-free phenomena are observed in the epidemic network. The numerical simulations provide theoretical support for current strategies and achieve more efficient control of some epidemic diseases, including SARS.

q-bio.PE