Highlights
- •Bayesian networks (BNs) are used to evaluate a pair of activity level propositions.
- •Probabilities on the persistence of non-self DNA on hands over time are obtained.
- •Delays in deposition decreased the detectability of the transferred contribution.
- •Empirical estimations of the likelihood of direct or indirect deposition are obtained.
- •Impacts of changes to various parameter values on the LR are observed.
Abstract
Questions relating to how DNA from an individual got to where it was recovered from
and the activities associated with its pickup, retention and deposition are increasingly
relevant to criminal investigations and judicial considerations. To address activity
level propositions, investigators are typically required to assess the likelihood
that DNA was transferred indirectly and not deposited through direct contact with
an item or surface. By constructing a series of Bayesian networks, we demonstrate
their use in assessing activity level propositions derived from a recent legal case
involving the alleged secondary transfer of DNA to a surface following a handshaking
event.
In the absence of data required to perform the assessment, a set of handshaking simulations
were performed to obtain probabilities on the persistence of non-self DNA on the hands
following a 40 min, 5 h or 8 h delay between the handshake and contact with the final surface (an axe handle).
Variables such as time elapsed, and the activities performed and objects contacted
between the handshake and contact with the axe handle, were also considered when assessing
the DNA results.
DNA from a known contributor was transferred to the right hand of an opposing hand-shaker
(as a depositor), and could be subsequently transferred to, and detected on, a surface
contacted by the depositor 40 min to 5 h post-handshake. No non-self DNA from the known contributor was detected in deposits
made 8 h post-handshake. DNA from the depositor was generally detected as the major or only
contributor in the profiles generated. Contributions from the known contributor were
minor, decreasing in presence and in the strength of support for inclusion as the
time between the handshake and transfer event increased.
The construction of a series of Bayesian networks based on the case circumstances
provided empirical estimations of the likelihood of direct or indirect deposition.
The analyses and conclusions presented demonstrate both the complexity of activity
level assessments concerning DNA evidence, and the power of Bayesian networks to visualise
and explore the issues of interest for a given case.
Keywords
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Article info
Publication history
Published online: November 28, 2017
Accepted:
November 26,
2017
Received in revised form:
October 21,
2017
Received:
August 18,
2017
Identification
Copyright
© 2017 Elsevier B.V. All rights reserved.
