title: Modelling SARS data using threshold geometric process.
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) [X(i), i=1,2,...] directly such that [a(i-1)X(i), 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.
authors.affiliation: Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong. jchan@hkustasc.hku.hk
authors.forename: Jennifer S K
authors.initials: JS
authors.lastname: Chan
authors.forename: Philip L H
authors.initials: PL
authors.lastname: Yu
authors.forename: Yeh
authors.initials: Y
authors.lastname: Lam
authors.forename: Alvin P K
authors.initials: AP
authors.lastname: Ho
mesh_terms: D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D009864:Ontario / Q000453:epidemiology; D011790:Quarantine; D045473:Severe acute respiratory syndrome-related coronavirus / Q000254:growth & development*; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology*; D012846:Singapore / Q000453:epidemiology; D013269:Stochastic Processes; D013624:Taiwan / Q000453:epidemiology