Citation

Fienberg SE, Shmueli G. Stat. Med. 2005; 24(4): 513-529.

Affiliation

Department of Statistics, Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Copyright

(Copyright © 2005, John Wiley and Sons)

DOI

10.1002/sim.2032

PMID

15678405

Abstract

The traditional focus for detecting outbreaks of an epidemic or bio-terrorist attack has been on the collection and analysis of medical and public health data. Although such data are the most direct indicators of symptoms, they tend to be collected, delivered, and analysed days, weeks, and even months after the outbreak. By the time this information reaches decision makers it is often too late to treat the infected population or to react in some other way. In this paper, we explore different sources of data, traditional and non-traditional, that can be used for detecting a bio-terrorist attack in a timely manner. We set our discussion in the context of state-of-the-art syndromic surveillance systems and we focus on statistical issues and challenges associated with non-traditional data sources and the timely integration of multiple data sources for detection purposes.

Language: en