Applied forensic epidemiology: the Bayesian evaluation of forensic evidence in vehicular homicide investigation

The comparative weighting of evidence in a criminal case can be a complicated task when the relevance or meaning of the evidence is disputed. An example of this complexity in seen in vehicular homicide investigations in which the identity of the driver (and thus the guilty party) is not clear. The discipline of Forensic Epidemiology, including the appropriate application of Bayes' Theorem (Bayes' Law) provides a systematic framework to bring clarity to the evaluation of such matters. Bayes' is a useful tool for the conditioning and quantification of probabilities associated with evidence in a vehicular homicide investigation. The authors present a case study in the application of Bayes' Theorem to the facts in a vehicular homicide investigation. An initial analysis of the crash dynamics in comparison with the injury pattern and ejection status of the surviving occupant versus that of the decedent suggested that the survivor was the driver. The results of the analysis were used as tests for guilt, with estimated true and false positive rates, which then formed the basis for a Bayesian calculation of the posterior probability of the survivor's guilt given the evidence. As a result of the Bayesian analysis described herein, it was determined that the survivor was 19 times more likely to have been the driver, in comparison with the decedent. This ratio far exceeded the suggested threshold of 10:1 for establishing the guilt of the survivor beyond a reasonable doubt. When used properly, Bayes' Theorem can offer definitive insight in the investigation and prosecution of vehicular homicide cases.