Decision making is ubiquitous in biosurveillance. Decisions link biosurveillance systems to response systems. Decision makers must be skilled in both interpretation of biosurveillance data and in weighing the costs and benefits of their decisions. They, ideally, must have access to probabilistic interpretations of biosurveillance data as well as objective information about the costs and benefits of their actions. At present, the assistance available to frontline personnel in taking decisions is the training that they receive in school, on the job, experts on whom they can call, and policies established by their organizations about these decisions. The biosurveillance systems that they use at present support rapid collection and analysis of patient data and epidemiological information, but with rare exception, they do not provide probabilistic output needed for decision making (the exceptions are BARD and PANDA described in Chapters 19 and 18, respectively).7 To date, there have been few economic analyses (see Kaufmann et al., 1997; Meltzer et al., 1999) and only one decision analysis of a biosurveillance problem (Wagner et al., 2005). Potentially, such analyses will be helpful for all major threats. Achieving this level of analysis is a long-term goal.
Decision analysis will be of increasing importance in biosurveillance as a result of the new requirement to detect outbreaks as early as possible. Biosurveillance organizations can use the techniques of decision analysis to build models of decision problems and use these models to determine policy. Computational decision analysis can provide direct support to frontline personnel as well as incident managers.
The next chapter discusses how to wrap existing biosurveillance algorithms in a Bayesian wrapper so that they can output the probabilities required by modelers (and decision makers). Chapter 31 discusses the state of the art in cost-benefit analysis.
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