The authors propose a small-world network model that combines cellular automata with the social mirror identities of daily-contact networks for purposes of performing epidemiological simulations. The social mirror identity concept was established to integrate human long-distance movement and daily visits to fixed locations. After showing that the model is capable of displaying such small-world effects as low degree of separation and relatively high degree of clustering on a societal level, the authors offer proof of its ability to display R 0 properties—considered central to all epidemiological studies. To test their model, they simulated the 2003 severe acute respiratory syndrome (SARS) outbreak.
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