Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong

Abstract

The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (+/-0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.

Figure 1. Compartmental Model for SARS Transmission in Hong Kong.

Superscript letters denote social categories: , general population; , healthcare workers; , hospitalized patients. Disease states are: , susceptible; , exposed (infected but not yet infectious); , infectious not hospitalized; , infectious hospitalized, and , removed (recovered or dead).

Figure 2. Daily Incidence of SARS Symptom Onset (Observed and 5×10⁴ Simulated Epidemics), Hong Kong, 2003.

Cases were defined as: nosocomial when patients had been hospitalized for ≥5 days before symptom onset ; healthcare workers when they were indeed healthcare workers and not nosocomial ; and general population, otherwise . The grey cloud surrounding the observed epidemic curve corresponds to simulated epidemic curves.

Figure 3. Proportion of Infectious Period Spent in the Community before Hospitalization as a Function of Week of Symptom Onset.

Figure 4. Mean Effective SARS Contact Rates (solid line) and 95% Credible Intervals (dashed line) in the Community and Hospitals as a Function of Time and Dates of Important Events.