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Monitoring epidemiologic surveillance data using hidden Markov models

✍ Scribed by Yann Le Strat; Fabrice Carrat

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 173 KB
Volume: 18
Category: Article
ISSN: 0277-6715
DOI: 10.1002/(sici)1097-0258(19991230)18:24<3463::aid-sim409>3.0.co;2-i

No coin nor oath required. For personal study only.

✦ Synopsis

The analysis of routinely collected surveillance data is an important challenge in public health practice. We present a method based on a hidden Markov model for monitoring such time series. The model characterizes the sequence of measurements by assuming that its probability density function depends on the state of an underlying Markov chain. The parameter vector includes distribution parameters and transition probabilities between the states. Maximum likelihood estimates are obtained with a modi"ed EM algorithm. Extensions are provided to take into account trend and seasonality in the data. The method is demonstrated on two examples: the "rst seeks to characterize in#uenza-like illness incidence rates with a mixture of Gaussian distributions, and the other, poliomyelitis counts with mixture of Poisson distributions. The results justify a wider use of this method for analysing surveillance data.

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