Epidemic intelligence statistical modelling for biosurveillance

Abstract

The last two decades, the emergence of new infectious diseases and the occasional rapid increase of their cases worldwide, the intense concern about bioterrorism, pandemic influenza or/and other Public Health threats, and the increasing volumes of epidemiological data, are all key factors that made necessary the development of advanced biosurveillance systems. Additionally, these factors have resulted in the awakening of the scientific community for introducing new and more efficient epidemic outbreak detection methods. As seen from above, the biosurveillance is a dynamic scientific activity which progresses and requires systematic monitoring of developments in the field of health sciences and biostatistics. This paper deals with the development of statistical regression modelling techniques in order to provide guidelines for the selection of the optimal periodic regression model for early and accurate outbreak detection in an epidemiological surveillance system, as well as for its proper use and implementation.