Transmission Dynamics of the Etiological Agent of SARS in Hong Kong: Impact of Public Health Interventions

See allHide authors and affiliations

Science  20 Jun 2003:
Vol. 300, Issue 5627, pp. 1961-1966
DOI: 10.1126/science.1086478
  • Fig. 1.

    Input data and model structure. (A) Map of Hong Kong showing the location of each district used in the model. More than 70% of cases reported through 16 April 2003 are from the three colored districts: Kwung Tong (red), Tai Po (green), and Sha Tin (blue). (B) Weekly incidence (by time of hospital admission) with the color coding used in (A). (C) Schematic flow diagram of transmission model used. Each district in the metapopulation model uses the structure shown. Susceptible individuals (S) become infected and enter a latent class (L). They then progress to a short asymptomatic and potentially infectious stage, I (see text), before the onset of symptoms and progression to class Y. It is assumed that every infected individual eventually enters hospital and either recovers (HR) or dies (HD). Color code: red, incubation period; blue, time from symptoms to hospitalization; magenta, time from hospitalization to death; green, time from hospitalization to recovery. (D) Distributions [of gamma form; see (10)] for the waiting times of the compartments of the stochastic model shown in (C). Colors match those used in (C). Distributions shown are estimates for the start of the epidemic. The onset-to-hospitalization distribution changes during the epidemic as a result of more rapid hospital admission.

  • Fig. 2.

    (A) Estimated reproduction number in the absence of SSEs, RtXSS, for the period of the epidemic for which data are available. Confidence intervals are also shown. The self-sustaining threshold RtXSS = 1 is shown as a solid line. (B) Model estimates of weekly case incidence (average of 1000 model realizations) for the three districts with the most cases, by date of hospital admission (to be compared with Fig. 1B). (C) Model estimate of case incidence (average of 1000 model realizations at the best-fit parameter set with 95% prediction intervals). Prediction intervals are generated from extreme realizations at the boundaries of the multivariate confidence intervals. The upper and lower bounds of the data are also shown.

  • Fig. 3.

    (A) Weekly admissions stratified by SSE cluster or type of exposure, as determined by contact tracing: 125 cases (green line) were attributed to direct exposure to the Hong Kong index patient on or shortly after his admission to the Prince of Wales Hospital on 3 March 2003 [only first-generation cases are shown (11)]; 331 cases (blue line) were attributed to the Amoy Gardens outbreak. Of the remaining 1056 cases, 201 were attributed to exposure occurring in a hospital or clinic (purple line) and 855 were attributed to other exposures or had no contact tracing available (ochre line). (B) Best fit model predictions stratified by exposure, matching definitions in (A). The predicted Amoy Gardens case time series has been further stratified into first-generation cases only (dashed line) and first-generation plus all additional cases (solid line). The correspondence between (B) and (A) is reasonable, although the discrepancy between the data and model predictions for Amoy Gardens suggests that only first-generation cases were successfully linked to the Amoy cluster by contact tracing. The narrow width of the observed Amoy cluster peak shown in (A) suggests that cases in this cluster had a narrower onset-to-hospitalization distribution than other cases. Comparing model predictions and data for hospital exposure–related cases also indicates that hospital transmission has decreased faster in recent weeks than is captured by the model. (C) Best fit model predictions for the total prevalence of symptomatic SARS patients, stratified according to whether they have been hospitalized.

  • Fig. 4.

    Effect of alternative control scenarios on progression of a hypothetical epidemic with Hong Kong–like characteristics. No SSEs other than an initial seeding event of 50 infections on day 0 are modeled. Five scenarios are shown: (A) No control measures or change in population behavior, giving rise to a catastrophic epidemic. (B) No change in behavior but a reduction in mean onset-to-hospitalization time of 2 days achieved on day 30, the effect of which is to temporarily increase admission numbers but to reduce transmission by 19%. (C) As B, but with complete cessation of movement between districts imposed on day 45 (the effect of which is to reduce transmission by 76%). These control measures are seen to be sufficient to control the epidemic even in the absence of population behavior change. (D) As B, but with a 50% drop in population contact rates and hospital infections from day 45—just sufficient to prevent epidemic growth. (E) As D, but with a 70% reduction in hospital transmission from day 55—sufficient to rapidly control the epidemic. For all scenarios, Hong Kong demographic parameters were used and averages of 1000 model realizations are shown.

Additional Files

  • Abstract
    Full Text
    Transmission Dynamics of the Etiological Agent of SARS in Hong Kong: Impact of Public Health Interventions
    Steven Riley, Christophe Fraser, Christl A. Donnelly, Azra C. Ghani, Laith J. Abu-Raddad, Anthony J. Hedley, Gabriel M. Leung, Lai-Ming Ho, Tai-Hing Lam, Thuan Q. Thach, Patsy Chau, King-Pan Chan, Su-Vui Lo, Pak-Yin Leung, Thomas Tsang, William Ho, Koon-Hung Lee, Edith M. C. Lau, Neil M. Ferguson, Roy M. Anderson

    Supporting Online Material

    This file is in Adobe Acrobat PDF format. If you have not installed and configured the Adobe Acrobat Reader on your system, please see Help with Printing for instructions.

    This supplement contains:

    SOM Text

    Download supplement

Stay Connected to Science

Navigate This Article