Likelihood ratios given activity-level propositions for DNA transfer evidence: Practical implementation and simulation studies using the HaloGen engine (Part II)

The quantitative interpretation of low-template DNA findings given activity-level propositions requires models that can accommodate inter-laboratory variability, uncertainty in transfer and recovery, and case-specific assumptions. This paper presents the practical implementation of HaloGen, an open-source hierarchical Bayesian framework for calculating activity-level likelihood ratios (LRs) from DNA quantity data. We compare three modelling approaches derived from the framework: a Group model, which combines data across laboratories, a hierarchically informed Lab–Bayes model, and a standalone, laboratory specific Lab–Vague model. Through simulation studies, we show that evidential strength is sensitive not only to DNA quantity but also to case context, particularly the assumed number of relevant actors ( N S ), the treatment of specified unknown contributors, and the choice of laboratory calibration. Inter-laboratory differences in DNA recovery and non-detection can lead to materially different LRs when these data are used within the HaloGen framework, so pooled or external data should not be used uncritically. To address practical implementation, we propose a minimum-effort calibration pathway for laboratories wishing to use HaloGen for quantitative activity-level LR reporting. The results indicate that a limited number of local direct/secondary transfer experiments can improve relevance compared with exclusive reliance on a pooled population model, although the adequacy of any dataset remains case- and proposition-dependent. The findings clarify how contextual assumptions enter mathematically into activity-level inference and underscore the importance of transparent specification of propositions, data relevance, model assumptions, and remaining expert judgement.