Modelling SARS data using threshold geometric process
During the outbreak of an epidemic disease, for example, the severe acute respiratory
syndrome (SARS), the number of daily infected cases often exhibit multiple trends:
monotone increasing during the growing stage, stationary during the stabilized stage and
then decreasing during the declining stage. Lam first proposed modelling a monotone trend
by a geometric process (GP){Xi, i= 1, 2,…} directly such that {ai− 1Xi, i= 1, 2,…} forms a
renewal process for some ratio a> 0 which measures the direction and strength of the trend …
syndrome (SARS), the number of daily infected cases often exhibit multiple trends:
monotone increasing during the growing stage, stationary during the stabilized stage and
then decreasing during the declining stage. Lam first proposed modelling a monotone trend
by a geometric process (GP){Xi, i= 1, 2,…} directly such that {ai− 1Xi, i= 1, 2,…} forms a
renewal process for some ratio a> 0 which measures the direction and strength of the trend …
Abstract
During the outbreak of an epidemic disease, for example, the severe acute respiratory syndrome (SARS), the number of daily infected cases often exhibit multiple trends: monotone increasing during the growing stage, stationary during the stabilized stage and then decreasing during the declining stage. Lam first proposed modelling a monotone trend by a geometric process (GP) {Xi, i=1,2,…} directly such that {ai−1Xi, i=1,2,…} forms a renewal process for some ratio a>0 which measures the direction and strength of the trend. Parameters can be conveniently estimated using the LSE methods. Previous GP models limit to data with only a single trend. For data with multiple trends, we propose a moving window technique to locate the turning point(s). The threshold GP model is fitted to the SARS data from four regions in 2003. Copyright © 2005 John Wiley & Sons, Ltd.
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