Background: Early detection of outdoor aerosol releases of anthrax is an important problem. The Bayesian Aerosol Release Detector (BARD) is a system for detecting releases of aerosolized anthrax and characterizing them in terms of location, time and quantity. Modelling a population's exposure to aerosolized anthrax poses a number of challenges. A major difficulty is to accurately estimate the exposure level--the number of inhaled anthrax spores--of each individual in the exposed region. Partly, this difficulty stems from the lack of fine-grained data about the population under surveillance. To cope with this challenge, nearly all anthrax biosurveillance systems, including BARD, ignore the mobility of the population and assume that exposure to anthrax would occur at one's home administrative unit--an assumption that limits the fidelity of the model.
Methods: We employed commuting data provided by the U.S. Census Bureau to parameterize a commuting model. Then, we developed methods for integrating commuting into BARD's simulation and detection algorithms and conducted two studies to measure the effect. The first study (simulation study) was designed to assess how BARD's detection and characterization performance are impacted by incorporation of commuting in BARD's outbreak-simulation algorithm. The second study (detection study) was designed to measure the effect of incorporating commuting in BARD's outbreak-detection algorithm.
Results: We found that failing to account for commuting in detection (when commuting is present in simulation) leads to a deterioration in BARD's detection and characterization performance that is both statistically and practically significant. We found that a simplified approach to accounting for commuting in detection--simplified to maintain tractability of inference--nearly fully restored both detection and characterization performance of BARD detector.
Conclusion: We conclude that it is important to account for commuting (and mobility in general) in BARD's simulation algorithm. Further, the proposed method for incorporating commuting in BARD's detection algorithm can successfully perform the necessary correction in the detection algorithm, while preserving BARD's practicality. In our future work, we intend to further study the problem of the trade-off between running time and accuracy of the computation in BARD's version that includes commuting and ultimately find the best such trade-off.