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. · 0.30 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: Information about the spatial spread of epidemics can be useful for many purposes. The spatial aspect of Swedish influenza data was analyzed with the main aim of finding patterns that could be useful for statistical surveillance of the outbreak, i.e. for detecting an increase in incidence as soon as possible. In Sweden, two types of data are collected during the influenza season: laboratory diagnosed cases (LDI), collected by a number of laboratories, and cases of influenza-like illness (ILI), c... merollected by a number of selected physicians. Quality problems were found for both types of data but were most severe for ILI. No evidence for a geographical pattern was found. Instead, it was found that the influenza outbreak starts at about the same time in the major cities and then occurs in the rest of the country. The data were divided into two groups, a metropolitan group representing the major cities and a locality group representing the rest of the country. The properties of the metropolitan group and the locality group were studied and it was found that the time difference in the onset of the outbreak was about one week. Both parametric and nonparametric regression models were suggested.01/2010;