Monitoring epidemiologic surveillance data using hidden Markov models.
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 modified EM algorithm. Extensions are provided to take into account trend and seasonality in the data. The method is demonstrated on two examples: the first seeks to characterize influenza-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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Unité de Recherches 'Epidémiologie et Sciences de l'Information', INSERM, U444 Institut Fédératif Saint-Antoine de Recherche sur la Santé, 27, rue Chaligny, 75571 Paris, Cedex 12, France. lestraty@necker.fr
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D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005602:France / Q000453:epidemiology; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D016013:Likelihood Functions; D008390:Markov Chains; D016011:Normal Distribution; D016012:Poisson Distribution; D011051:Poliomyelitis / Q000453:epidemiology*; D011159:Population Surveillance; D014481:United States / Q000453:epidemiology