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(How) Should Risk Assessors Think with Models?
Citation:
Etterson, M. (How) Should Risk Assessors Think with Models? Presentation to University of Minnesota Duluth Department of Mathematics and Statistics Colloquium, Duluth, MN, December 02, 2021. https://doi.org/10.23645/epacomptox.18703580
Impact/Purpose:
Presentation to University of Minnesota Duluth Department of Mathematics and Statistics Colloquium December 2021. This presentation is intended for a very general academic audience. It describes some of the major challenges with model-based inference in environmental toxicology. It is intended for education purposes only and is not a presentation of scientific results.
Description:
Despite over 50 years of research on the use of population models in chemical risk assessment, their practical utility has remained elusive. A novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parameterization, and verification. ELMs capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness. Two measures of fitness are proposed as natural model predictions in the context of chemical risk assessment, lifetime reproductive success, and the expected annual propagation of genetic descendants, including self (intrinsic fitness). Throughout, the focus is on development of robust qualitative model predictions that depend as little as possible on specific parameter values. Thus, ELMs sacrifice precision to optimize generality in understanding the effects of chemicals across the diversity of avian lifecycles. Notably, the ELM approach integrates naturally with the adverse outcome pathway framework; this integration can be employed as a mid-tier risk assessment tool when lower tier analyses suggest potential risk. This abstract does not reflect EPA policy.
URLs/Downloads:
DOI: (How) Should Risk Assessors Think with Models?
ETTERSON.UMDMATH.2021B.PDF (PDF, NA pp, 2583.71 KB, about PDF)