Robust outbreak surveillance of epidemics in Sweden.
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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ABSTRACT: In this article, we discuss statistical methods for curve-estimation under the assumption of unimodality for variables with distributions belonging to the two-parameter exponential family with known or constant dispersion parameter. An important special case is a one-parameter distribution. We suggest a nonparametric method based on monotonicity properties. The method is applied to Swedish data on laboratory verified diagnoses of influenza and data on inflation from an episode of hyperinflation in Bulgaria.Communication in Statistics- Theory and Methods 05/2009; 38(9):1526-1538. · 0.28 Impact Factor
Article: Multivariate outbreak detection[Show abstract] [Hide abstract]
ABSTRACT: Online monitoring is needed to detect outbreaks of diseases such as influenza. Surveillance is also needed for other kinds of outbreaks, in the sense of an increasing expected value after a constant period. Information on spatial location or other variables might be available and may be utilized. We adapted a robust method for outbreak detection to a multivariate case. The relation between the times of the onsets of the outbreaks at different locations (or some other variable) was used to determine the sufficient statistic for surveillance. The derived maximum-likelihood estimator of the outbreak regression was semi-parametric in the sense that the baseline and the slope were non-parametric while the distribution belonged to the one-parameter exponential family. The estimator was used in a generalized-likelihood ratio surveillance method. The method was evaluated with respect to robustness and efficiency in a simulation study and applied to spatial data for detection of influenza outbreaks in Sweden.Journal of Applied Statistics 02/2012; 39(2):223-242. · 0.45 Impact Factor
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ABSTRACT: Spatial surveillance is a special case of multivariate surveillance. Thus, in this review of spatial outbreak methods, the relation to general multivariate surveillance approaches is discussed. Different outbreak models are needed for different public health applications. We will discuss methods for the detection of: 1) Spatial clusters of increased incidence, 2) Increased incidence at only one (unknown) location, 3) Simultaneous increase at all locations, 4) Outbreaks with a time lag between the onsets in different regions. Spatial outbreaks are characterized by the relation between the times of the onsets of the outbreaks at different locations. The sufficient reduction plays an important role in finding a likelihood ratio method. The change at the outbreak may be a step change from the non-epidemic period to an increased incidence level. However, errors in the estimation of the baseline have great influence and nonparametric methods are of interest. For the seasonal influenza in Sweden the outbreak was characterized by a monotonic increase following the constant non-epidemic level. A semiparametric generalized likelihood ratio surveillance method was used. Appropriate evaluation metrics are important since they should agree with the aim of the application. Evaluation in spatial and other multivariate surveillance requires special concern.IIE Transactions 01/2012; 46(8). · 1.06 Impact Factor