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 40min, 5h or 8h 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 40min to 5h post-handshake. No non-self DNA from the known contributor was detected in deposits made 8h 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.