A Memory-Based Quantitative Framework for Postmortem Interval Estimation with Uncertainty Bounds

Accurate estimation of the postmortem interval (PMI) is one of the most critical and least reliable applications in forensic science. Methods based on the cooling of the body’s tissues rely on the memoryless property of heat transfer in a constant ambient temperature environment, which can introduce biases into the PMI estimation process. We introduce a memory-based fractional model for PMI estimation that accounts for these biases and provides uncertainty quantification in the model’s predictions. Our model accounts for the effects of body mass, clothing insulation, humidity, ambient airflow, and ground contact in a Monte Carlo process to calculate PMI with uncertainty. A sensitivity analysis helps identify the most significant sources of uncertainty in the model’s estimates of PMI. A case study based on a real forensic investigation indicates the potential value of the model in a forensic investigation while remaining transparent to the courts that oversee those investigations.