Atmospheric dispersion models describe how wind disperses biological agents from the source of an aerosol release. They have several important uses in biosurveillance, including configuration of biosurveillance systems before an outbreak, detection and characterization of an outbreak, and focused surveillance in areas suspected to have been exposed but where individuals are not yet ill after outbreak detection.

For atmospheric dispersion models to be applied to outbreak detection, they must be inverted. Most research on the use of inverted atmospheric dispersion models for outbreak detection has used Bayesian methods. Inverted models for outbreak detection have the potential to narrow down the differential diagnosis of outbreaks by inferring that outbreaks are windborne (which limits the possible organisms) early in the course of an outbreak. They can also estimate the location, quantity, and timing of the release of biological agent, information critical to directing response efforts.

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