Michael M. Wagner

RODS Laboratory, Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania


Biosurveillance is a process that detects disease in people, plants, or animals. It detects and characterizes outbreaks of disease. It monitors the environment for bacteria, viruses, and other biological agents that cause disease. The biosurveillance process systematically collects and analyzes data for the purpose of detecting cases of disease, outbreaks of disease, and environmental conditions that predispose to disease.

Detection of disease outbreaks is of particular importance. Unlike explosions, disease outbreaks are silent. Disease outbreaks sicken or kill individuals before they are detected. Disease outbreaks can inflict this damage quickly, and they can also spread quickly. The window of opportunity to limit this damage can be as brief as a few days in the worst case (Wagner et al., 2001).

The United States spends billions of dollars per year on various forms of biosurveillance. The major expenditures are for hospital infection control, public health surveillance, surveillance of the air and water, training, improvement of the information technology infrastructure for public health, and research.1 Biosurveillance is also a rapidly growing scientific field at the intersection of epidemiology, artificial intelligence, microbiology, computer science, statistics, system engineering, medicine, and veterinary medicine.


The biosurveillance process is a continuous one (Figure 1.1). An organization conducting biosurveillance collects and analyzes surveillance data continuously. The organization also faces decisions continuously about whether to act based on the results of these analyses.

The biosurveillance process involves a positive feedback loop: when the continuous collection and analysis of surveillance data identifies an anomalous number of sick individuals (or a single case of a dangerous disease), investigators collect additional information that feeds back into the analytic process, resulting in better characterization of the event. The improved understanding of the event may lead to more questions, which drive further collection of data and additional analyses. Concurrent with these cycles of data collection and analysis, the organization may initiate response actions such as vaccinations and quarantine to control the outbreak. The net effect of this process, when viewed over time, is a series of actions that lead to characterization and control of the outbreak.

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