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: This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina.Statistics in Medicine 09/2011; 30(26):3095-116. · 2.04 Impact Factor
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ABSTRACT: Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced. Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks. This growth in interest has given rise to much new methodological work, ranging across the spectrum of statistical methods. The paper presents a comprehensive review of the statistical approaches that have been proposed. Applications to both laboratory and syndromic surveillance data are provided to illustrate the various methods.Journal of the Royal Statistical Society Series A (Statistics in Society) 12/2011; 175(1):49 - 82. · 1.36 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