A Reduced Mechanistic Model for Aquatic Decomposition of a Human Body
- 1University of South Florida
Abstract
Estimating the postmortem submersion interval (PMSI) remains challenging due to complex biological and environmental interactions during aquatic decomposition. This study proposes a reduced mechanistic ordinary differential equation (ODE) model linking tissue degradation, microbial activity, and dissolved oxygen dynamics. Calibrated against the total aquatic decomposition score (TADS) trajectory for Northern Adriatic Sea cases, the model preserves a strictly monotone TADS--time relationship, ensuring unique numerical inversion from observed TADS to PMSI. Synthetic observation experiments evaluated how data selection influences parameter recovery, practical identifiability, and robustness under measurement uncertainty. Microbial measurements improved parameter recovery for microbial dynamics, whereas dissolved oxygen measurements constrained oxygen-consumption and renewal processes. Combining both types of observation optimized the recovery of coupled parameters. Trajectory reconstruction, profile likelihood analysis, boundary-hit frequencies, and Monte Carlo simulations confirmed these findings. Importantly, while all observation strategies reproduced TADS with comparable accuracy, they differed substantially in recovering underlying biological parameters and latent-state dynamics. Thus, accurately reproducing decomposition scores alone is insufficient for reliable parameter inference. This framework provides a transparent and biologically interpretable foundation for the mechanistic estimation of PMSI. Future applications to case-level and multi-site datasets may enable site-specific recalibration and improved predictive evaluation across diverse aquatic environments.
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BibTeX
@misc{akin2026reduced,
title = {{A Reduced Mechanistic Model for Aquatic Decomposition of a Human Body}},
author = {U. Akin and O. F. Aykir and N. N. PELEN and M. Kaplan},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.755382},
url = {https://www.biorxiv.org/content/10.64898/2026.09.29.755382v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A Reduced Mechanistic Model for Aquatic Decomposition of a Human Body
AU - U. Akin
AU - O. F. Aykir
AU - N. N. PELEN
AU - M. Kaplan
PY - 2026
DA - 2026/09/29
PB - bioRxiv
DO - 10.64898/2026.09.29.755382
UR - https://www.biorxiv.org/content/10.64898/2026.09.29.755382v1
AB - Estimating the postmortem submersion interval (PMSI) remains challenging due to complex biological and environmental interactions during aquatic decomposition. This study proposes a reduced mechanistic ordinary differential equation (ODE) model linking tissue degradation, microbial activity, and dissolved oxygen dynamics. Calibrated against the total aquatic decomposition score (TADS) trajectory for Northern Adriatic Sea cases, the model preserves a strictly monotone TADS--time relationship, ensuring unique numerical inversion from observed TADS to PMSI. Synthetic observation experiments evaluated how data selection influences parameter recovery, practical identifiability, and robustness under measurement uncertainty. Microbial measurements improved parameter recovery for microbial dynamics, whereas dissolved oxygen measurements constrained oxygen-consumption and renewal processes. Combining both types of observation optimized the recovery of coupled parameters. Trajectory reconstruction, profile likelihood analysis, boundary-hit frequencies, and Monte Carlo simulations confirmed these findings. Importantly, while all observation strategies reproduced TADS with comparable accuracy, they differed substantially in recovering underlying biological parameters and latent-state dynamics. Thus, accurately reproducing decomposition scores alone is insufficient for reliable parameter inference. This framework provides a transparent and biologically interpretable foundation for the mechanistic estimation of PMSI. Future applications to case-level and multi-site datasets may enable site-specific recalibration and improved predictive evaluation across diverse aquatic environments.
ER -