以小世界社會網路為基礎的流行病模擬模型

Abstract

本研究提出一個新的小世界模型—具有分身點概念的細胞自動機，以進行流行病模擬。利用分身點的概念，可以直觀地描述在真實社會中，個體藉著交通工具長距離移動與每天在固定地點活動的行為，例如：家庭、工作場合、捷運站、或餐廳。本研究從社會學與流行病學兩個層面依次說明如何將分身點的概念應用在傳統細胞自動機上。然後以實驗分析證明本模型具有社會特質，也就是能夠表達出小世界的特性（低分隔度與高群聚度）。之後，與傳統流行病模型所推導出的R0再傳染參數做比較，說明本模型亦能夠展現R0參數的特性，證明本模型可以正確地套用在流行病學的模擬上。最後以2003年在全世界爆發的SARS為例證，證明本模型適合可以用來做流行病模擬。

The author validates a new small world model consisting of cellular automata with mirror identities of daily-contact social networks for purposes of epidemiological simulations. The mirror identity concept was established to integrate human long-distance movement and daily visits to fixed locations into the model. After showing that the model is capable of displaying small-world effects (i.e., low degree of separation and relatively high degree of clustering) on a societal level, we offer proof of its ability to display R0 properties, which are considered central to epidemiological studies. A simulation of the 2003 SARS outbreak serves as our primary example of how the proposed model functions.