Robust outbreak surveillance of epidemics in Sweden

Statistical Research Unit, Department of Economics, University of Gothenburg, and Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, SE 40530, Gothenburg, Sweden.
Statistics in Medicine (Impact Factor: 2.04). 02/2009; 28(3):476-93. DOI: 10.1002/sim.3483
Source: PubMed

ABSTRACT Outbreak detection is of interest in connection with several diseases and syndromes. The aim is to detect the progressive increase in the incidence as soon as possible after the onset of the outbreak. A semiparametric method is applied to Swedish data on tularaemia and influenza. The method is constructed to detect a change from a constant level to a monotonically increasing incidence. If seasonal effects are present, the residuals from a model incorporating these can be used. The properties of the method are evaluated by application to Swedish data on tularaemia and influenza and by simulations. The suggested method is compared with subjective judgments as well as with other algorithms. The conclusion is that the method works well. A user-friendly computer program is described.

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    01/2012; 3(1). DOI:10.4172/2157-7420.1000108
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    ABSTRACT: Public health surveillance aims at lessening disease burden, e.g., in case of infectious diseases by timely recognizing emerging outbreaks. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionality for the visualization, mod-elling and monitoring of surveillance time series. With respect to modelling we focus on univariate time series modelling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. This ranges from illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modelling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdisper-sion using beta-binomial or Dirichlet-Multinomial modelling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihood-ratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Al-together, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.
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