Predicting case numbers during infectious diseaseoutbreaks when some cases are undiagnosed
We describe a method for calculating 95 per cent bounds for the current number of hidden cases and the future number of diagnosed cases during an outbreak of an infectious disease. A Bayesian Markov chain Monte Carlo approach is used to fit a model of infectious disease transmission that takes account of undiagnosed cases. Assessing this method on simulated data, we find that it provides conservative 95 per cent bounds for the number of undiagnosed cases and future case numbers, and that these...[Show more]
|Collections||ANU Research Publications|
|Source:||Statistics in Medicine|
|01_Glass_Predicting_case_numbers_during_2007.pdf||134.18 kB||Adobe PDF|
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