Because the atmospheric dispersion of a biological agent that is released into the air will determine in large part the spatial distribution of cases, it is logical to construct an outbreak detection algorithm that employs atmospheric dispersion modeling. The additional information provided by the model and weather data may enable earlier and more specific detection of a windborne outbreak. The potential increase in specificity results from the ability to distinguish between a disease pattern that is consistent with known weather conditions and the knowledge of how biological agents disperse in the atmosphere that is embodied in an atmospheric dispersion model. Such algorithms may be less prone to false alarms due to non-windborne outbreaks because other modes of transmission of disease produce different spatial patterns in data.
To incorporate an atmospheric dispersion model into a detection algorithm, we must invert it. An atmospheric dispersion model takes as input a known release of biologic agent and projects its downwind effects.To detect an unknown release, we must take observations about downwind effects (e.g., sick people or environmental samples such as the number of spores estimated from a BioWatch or other environmental monitor) and work backwards to infer the location, amount, and time of the release of the biologic agent (Figures 19.4 and 19.5). We now discuss this inversion problem and examples of algorithms and the methods they use to solve it.
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