title: Bayesian analysis of severe acute respiratory syndrome: the 2003 Hong Kong epidemic.
abstract: This paper analyzes data arising from a Severe Acute Respiratory Syndrome (SARS) epidemic in Hong Kong in 2003 involving 1755 cases. A discrete time stochastic model that uses a back-projection approach is proposed. Markov Chain Monte Carlo (MCMC) methods are developed for estimation of model parameters. The algorithm is further extended to integrate numerically over unobserved variables of the model. Applying the method to SARS data from Hong Kong, a value of 3.88 with a posterior standard deviation of 0.09 was estimated for the basic reproduction number. An estimate of the transmission parameter at the beginning of the epidemic was also obtained as 0.149 with a posterior standard deviation of 0.003. A reduction in the transmission parameter during the course of the epidemic forced the effective reproduction number to cross the threshold value of one, seven days after control interventions were introduced. At the end of the epidemic, the effective reproduction number was as low as 0.001 suggesting that the epidemic was brought under control by the intervention measures introduced.
authors.affiliation: Mathematics Department, University of Botswana, Private Bag 0022, Gaborone, Botswana. lekonepe@mopipi.ub.bw
authors.forename: Phenyo E
authors.initials: PE
authors.lastname: Lekone
mesh_terms: D000465:Algorithms; D050936:Basic Reproduction Number; D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015995:Prevalence; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology* / Q000635:transmission; D013269:Stochastic Processes