1.5 Airborne dispersion modelling

Data requirements: Case Data , Potential outbreak source locations, Meteorological data

Description: In essence a plume model attempts to track the concentration, or dose, of a contaminant through space and time following its release into the atmosphere. A simple Gaussian plume model makes the simplifying assumption that the wind is of a fixed speed and direction for the duration of the release and whilst having an inspiration in turbulent fluid flow theory the form of the Gaussian plume is dependent on empirical estimates of downwind and vertical dispersion. The effects of buildings and vehicles in changing the flow of material are not included in such a model. Releases of legionnella tend to be over a comparatively long period of time (weeks or months and so the weather is likely to have changed over this time) and tend to be in urban environments.

Numerous software packages exist (SCIPUFF, ADMS) that allow for the modelling of more realistic environments, for example with changing meteorological conditions or within an urban environment, but these require careful parameterisation and expert users/interpretation.

Dispersion models serve two major functions, one to estimate the exposed population following a potential release, the other to show potential release sites from the pattern of observed infections. For the former insufficient evidence has been compiled to suggest an infectious dose of Legionella in humans (or the probability of infection following inhalation of a dose), or the atmospheric survival of the bacteria. As such converting the contours from a plume model into potential infections is difficult without additional strong assumptions being made. The latter use for a dispersion model is challenging in the majority of outbreaks as the uncertainty regarding the time of infection means that the location of infection is unclear. We are also unsure of the total 'at risk' population in time and space. We are thus often left with stating that the pattern of infection is consistent with a release rather than making stronger statements.

Outputs generated from plume models, despite the uncertainty involved, can be used within similar analyses as described in section 1.3.1 Buffer and overlay analysis. By replacing the buffer with the modelled plume(s), case movement data can be analysed to identify those cases that have moved within the area covered by the 'plume(s)'.

Software required: Plume modelling software such as ADMS.

Examples from the literature:

Nguyen et. al (2006) http, incorporated hourly meteorological data including temperature, humidity, nebulosity, wind speed and wind direction into a Gaussian dispersion model to simulate the dispersion of aerosols from a suspected cooling tower over largely open countryside in their investigation into an Legionnaires' disease outbreak in Pas-de-Calais, France. Similarly Nygard et. al (2005) http made use of hourly meteorological data within a Gaussian Puff model to describe the dispersal of aerosols from a number of potential sources and found the plume modelled for the source identified as being responsible for the outbreak had the best fit with the distribution of cases.