Moser C.B., Gupta M., Archer B.N., White L.F.
Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, MA, United States; National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa
Moser, C.B., Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, MA, United States; Gupta, M., Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, MA, United States; Archer, B.N., National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa; White, L.F., Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, MA, United States
The basic reproductive number (R0) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R0 and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R0 and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R 0 = 1.36 -1.46,μ = 2.0-2.7,μ = mean of the SI). The estimates of R0 and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample. © 2015 Moser et al.
2009 H1N1 influenza; Article; basic reproduction number; Bayes theorem; contact examination; disease transmission; epidemic; Hong Kong; household; human; sample size; serial interval; severe acute respiratory syndrome; Singapore; South Africa; statistical model; statistical parameters; Bayes theorem; computer simulation; confidence interval; epidemic; Influenza A virus (H1N1); Influenza, Human; severe acute respiratory syndrome; statistics and numerical data; transmission; virology; Bayes Theorem; Computer Simulation; Confidence Intervals; Contact Tracing; Disease Outbreaks; Hong Kong; Humans; Influenza A Virus, H1N1 Subtype; Influenza, Human; Severe Acute Respiratory Syndrome; Singapore; South Africa