Introduction

Decision making is part of the daily routine of biosurveillance. When a healthcare provider or a citizen notifies a health department about a case or an unusual cluster of illness, the staff at the health department must decide whether and how to further investigate. Hospital infection control practitioners, veterinarians, or nursing home directors face similar decisions when they receive results of testing or discover anomalous numbers of cases of disease. During an outbreak, these individuals face many decisions that they must make quickly and often under uncertainty and resource limitations.

Recently, the number of routine decisions faced by biosurveillance staff has increased. Now, not only must staff decide whether to investigate reports of confirmed cases from physicians and laboratories, and the less frequent reports of clusters of illnesses, but they must also decide whether to react to surveillance data collected from schools, 911 call centers, doctor's offices, pharmacies, and emergency departments. These data are associated with higher degrees of uncertainty than are reportable disease data, which increases the difficulty of the decisions.

Biosurveillance organizations also make decisions. They decide how to set the alarm thresholds of detection algorithms. These decisions are closely related to the above decisions about how to react to anomalies in biosurveillance data. A biosurveillance organization also decides how to invest finite resources in biosurveillance systems.

In this chapter, we focus on the decisions elicited by anomalies in biosurveillance data. We review the science of decision making, which includes decision theory and decision analysis. Decision analysis is a technique that people and organizations can use to improve the quality of their decisions. Organizations may use decision analysis to develop policies to guide frontline personnel or even incorporate the mathematical models created by these techniques directly into biosurveillance systems.

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