High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems.

INTRODUCTION When public health surveillance systems are evaluated, CDC recommends that the expected sensitivity, specificity, and timeliness of surveillance systems be characterized for outbreaks of different sizes, etiologies, and geographic or demographic scopes. High-Fidelity Injection Detectability Experiments (HiFIDE) is a tool that health departments can use to compute these metrics for detection algorithms and surveillance data that they are using in their surveillance system. OBJECTIVE The objective of this study is to develop a tool that allows health departments to estimate the expected sensitivity, specificity, and timeliness of outbreak detection. METHODS HiFIDE extends existing semisynthetic injection methods by replacing geometrically shaped injects with injects derived from surveillance data collected during real outbreaks. These injects maintain the known relation between outbreak size and effect on surveillance data, which allows inferences to be made regarding the smallest outbreak that can be expected to be detectable. RESULTS An example illustrates the use of HiFIDE to analyze detectability of a waterborne Cryptosporidium outbreak in Washington, DC. CONCLUSION HiFIDE enables public health departments to perform system validations recommended by CDC. HiFIDE can be obtained for no charge for noncommercial use (http://www.hifide.org).

[1]  Tom Fawcett,et al.  Activity monitoring: noticing interesting changes in behavior , 1999, KDD '99.

[2]  J. Aramini,et al.  Waterborne cryptosporidiosis outbreak, North Battleford, Saskatchewan, Spring 2001. , 2001, Canada communicable disease report = Releve des maladies transmissibles au Canada.

[3]  Galit Shmueli,et al.  Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Marcello Pagano,et al.  Using temporal context to improve biosurveillance , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Fu-Chiang Tsui,et al.  Application of Information Technology: Design of a National Retail Data Monitor for Public Health Surveillance , 2003, J. Am. Medical Informatics Assoc..

[6]  Kenneth D. Mandl,et al.  Time series modeling for syndromic surveillance , 2003, BMC Medical Informatics Decis. Mak..

[7]  S. Blount,et al.  Lead Visual Information Specialist , 2003 .