PNNL

Abstract

Chemical and biological forensic programs rely heavily on laboratory measurements to determine how a threat agent may have been produced. In addition to laboratory analyses, it may also be useful to identify institutions where the same threat agent has been produced by the same or a very similar process, since the producer of the agent may have learned methods at a university or similar institution. We developed a Bayesian network framework that combines the results of laboratory measurements with evidence from scientific literature to probabilistically rank institutions that have published papers on the agent of interest. As an example, we consider a network of three laboratory assays that are used to estimate the probabilities that a forensic sample of Yersinia pestis was produced using one of three culture media. We then apply techniques from multi-attribute decision science to assess and compare the performance of the various implementations of the Bayesian network in terms of three attributes: fidelity, document curation intensity, and consumption of the forensic sample. The mathematical approach we use to compare the various implementations is generalizable to the evaluation of other signature systems.