Introduction

The motivation for using models of atmospheric dispersion in the analysis of biosurveillance data is the basic physics of an aerosol release. When a biological agent is released outdoors into the atmosphere as an aerosol cloud, wind and other weather conditions determine the locations to which the cloud travels and its density when it reaches a particular location. These factors in turn influence the spatial pattern of disease in a community.

An atmospheric dispersion model is a mathematical description of how wind and other weather conditions determine the spread of substances such as biological agents through the air. An aerosol release of a biological agent into the atmosphere is the most feared route of a biological attack. Windborne distribution of a biological agent is an efficient means of (nearly) simultaneously exposing as many as hundreds of thousands to millions of people to an agent. In addition to the terrorist threat, wind can also spread several naturally occurring diseases of concern to biosurveillance over distances as large as 100 km.

One naturally occurring disease for which the wind is a route of transmission is foot and mouth disease (FMD), a highly contagious infectious disease of cloven-hoofed animals such as cattle, pigs, and sheep. Researchers have studied the windborne spread of FMD extensively because FMD has significant economic consequences to nations that are currently free of the disease (through trade embargoes that disease-free nations may impose). Additionally, experts consider FMD as the most likely terrorist threat to agribusiness (Breeze, 2004, Cupp et al., 2004). When managing an outbreak of FMD, many governments use atmospheric dispersion models specialized for FMD to identify farms at risk of acquiring FMD from infected farms and to make decisions about restrictions on movement of animals and vaccination strategies.

Besides the FMD virus, biological agents of concern for aerosol dissemination include, but are not limited to, Bacillus anthracis (anthrax), Francisella tularensis (tularemia), Yersinia pestis (plague), the Smallpox virus, and botulinum toxin (see Table 3.2 in Chapter 3). Outbreaks of other naturally occurring diseases known or suspected to be the result of windborne spread include Q fever in humans, due to windborne spread of Coxiella burnetii spores generated by sheep husbandry (Hawker et al., 1998); bluetongue in sheep, due to windborne spread of the Culicoides midge that is the insect vector of the disease (Sellers et al., 1978, Sellers et al., 1979, Sellers and Pedgley, 1985); and Western equine encephalitis in horses, due to windborne spread of its mosquito vector (Sellers and Maarouf, 1988). Windborne spread of plant pathogens is also common (Davis, 1987).

We begin with an example of how researchers have used atmospheric dispersion models and weather data in the past to identify that an outbreak resulted from windborne dissemination of a biological agent. We then explore the basics of atmospheric dispersion modeling and contrast several specific atmospheric dispersion models. We then discuss more generally the role of atmospheric dispersion models in the analysis of biosurveillance data. We discuss additional uses of atmospheric dispersion models and weather data—a key input parameter of an atmospheric dispersion model—at the end of the chapter.

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