Estimation of temperature-dependent growth profiles for the assessment of time of hatching in forensic entomology.

This article focuses on a novel functional data analysis (FDA) method for estimating the hatching time of blow fly larvae in forensic entomology, using temperature-dependent larval growth data. The approach integrates experimental larval length measurements at constant temperatures with dynamic temperature profiles from crime scenes via a differential equation model, enabling reconstruction of growth curves under varying temperatures. The method estimates the hatching time by minimizing the discrepancy between observed larval lengths at the scene and predicted lengths from the reconstructed growth curve, allowing incorporation of expert knowledge and uncertainty quantification in both frequentist and Bayesian frameworks. Empirical validation through simulations and two anonymized UK case studies demonstrates that this method provides comparable or improved estimates over the traditional accumulated degree hours (ADH) model, with added benefits in uncertainty assessment and model diagnostics. The authors note that while the method enhances growth curve estimation, it does not alone determine the post-mortem interval due to biological and environmental complexities, and suggest future extensions to include additional developmental stages and uncertainty sources.