PREDICTION OF INFECTIOUS DISEASES:AN EXCEPTION REPORTING SYSTEM

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.

[1]  Chris Chatfield,et al.  The Analysis of Time Series: An Introduction , 1981 .

[2]  D F Stroup,et al.  Evaluation of a method for detecting aberrations in public health surveillance data. , 1993, American journal of epidemiology.

[3]  I. Tager,et al.  Application of exponential smoothing for nosocomial infection surveillance. , 1996, American journal of epidemiology.

[4]  Diane Lambert,et al.  Zero-inflacted Poisson regression, with an application to defects in manufacturing , 1992 .

[5]  D. Costagliola,et al.  When is the epidemic warning cut-off point exceeded? , 1994, European Journal of Epidemiology.

[6]  L. Stern,et al.  Automated outbreak detection: a quantitative retrospective analysis , 1999, Epidemiology and Infection.

[7]  Thong Ngee Goh,et al.  Zero-inflated Poisson model in statistical process control , 2002 .

[8]  Laurent Toubiana,et al.  A space-time criterion for early detection of epidemics of influenza-like-illness , 1998, European Journal of Epidemiology.

[9]  John Hinde,et al.  Models for count data with many zeros , 1998 .

[10]  Nick Andrews,et al.  A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease , 1996 .

[11]  L Watier,et al.  A time series construction of an alert threshold with application to S. bovismorbificans in France. , 1991, Statistics in medicine.

[12]  D F Stroup,et al.  Detection of aberrations in the occurrence of notifiable diseases surveillance data. , 1989, Statistics in medicine.