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Prediction of infectious diseases:an exception reporting system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper prediction methods are discussed in the context of developing an exception reporting system for laboratory reports. The detection of outbreaks and longer term trends is briefly addressed, before a consideration of data types and availability to be used in evaluating the prediction methods. Four general prediction methods are outlined and the selection of data to which they are applied is examined. Both real and simulated data are used to evaluate the prediction methods and a strategy for an exception reporting system is proposed.
Rocznik
Tom
Strony
MI67--74
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
autor
  • Department of Statistics and Modelling Science, University of Strathclyde, Livingstone Tower, 26 Richmond Street, Glasgow, G1 1XH, U.K.
autor
autor
autor
Bibliografia
  • [1] LAST J.M., ed. A dictionary of epidemiology, 3rd ed, Oxford University Press, 1995.
  • [2] COSTAGLIOLA D., When is the epidemic warning cut-off point exceeded?, European Journal of Epidemiology, Vol. 10, No. 4, pp.475-476, 1994.
  • [3] WATIER L., RICHARDSON S., HUBERT B., A time-series construction of an alert threshold with application to S-bovismorbificans in France, Statistics in Medicine, Vol. 10, No. 10, pp.1493-1509, 1991.
  • [4] TOUBIANA L., FLAHAULT A., A space-time criterion for early detection of epidemics of influenza-like-illness, European Journal of Epidemiology, Vol. 14, No. 5, pp.465-470, 1998.
  • [5] FARRINGTON C.P., ANDREWS N.J., BEALE A.D., CATCHPOLE M.A., A statistical algorithm for the early detection of outbreaks of infectious disease, Journal of the Royal Statistical Society Series A - Statistics in Society, Vol. 159, No. 3, pp.547-563, 1996.
  • [6] STROUP D.F., WHARTON M., KAFADAR K., DEAN A.G., Evaluation of a method for detecting aberrations in public-health surveillance data, American Journal of Epidemiology, Vol. 137, No. 3, pp.373-380, 1993.
  • [7] STERN L., LIGHTFOOT D., Automated outbreak detection: A quantitative retrospective analysis, Epidemiology and Infection, Vol. 122, No. 1, pp.103-110, 1999.
  • [8] NGO L., TAGER I.B., HADLEY D., Application of exponential smoothing for nosocomial infection surveillance, American Journal of Epidemiology, Vol. 143, No. 6, pp.637-647, 1996.
  • [9] CHATFIELD C., The analysis of time series - An introduction, 5th ed, Chapman & Hall/CRC, 1996.
  • [10] STROUP D.F., WILLIAMSON G.D., HERNDON J.L., Detection of aberrations in the occurrence of notifiable diseases surveillance data, Statistics in Medicine, Vol. 8, No. 3, pp.323-329, 1989.
  • [11] XIE M., HE B., GOH T.N., Zero-inflated Poisson model in statistical process control, Computational Statistics & Data Analysis, Vol. 38, No. 2, pp.191-201, 2001.
  • [12] LAMBERT D., Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics, Vol. 34, No. 1, pp.1-14, 1992.
  • [13] RIDOUT M., DEMETRIO C.G.B., HINDE J., Models for count data with many zeros, Proc. 19th International Biometric Conference, pp.179-192, Cape Town, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0019-0011
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