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Using Agent-Based Approaches to Characterize Exposure Related Behavior
Citation:
Tornero-Velez, R., K. Isaacs, AND P. Price. Using Agent-Based Approaches to Characterize Exposure Related Behavior. ISES 2015 Annual Meeting, Henderson, NV, October 18 - 22, 2015.
Impact/Purpose:
This poster will communicate the goals and initial efforts in building models of human behavior relevant to assessing exposures to chemicals in consumer products.
Description:
The Tox21 initiative is generating data on biological activity, toxicity, and chemical properties for over 8,000 substances. One of the goals for EPA’s National Exposure Research Lab (NERL) is to assess the magnitude and variability in the public’s exposures to these chemicals. Because of the number of substances and the complexity of human exposures, novel approaches are required to achieve this goal. One approach that is being investigated at NERL is the creation of simulated individuals (agents) and the modeling of their interactions with multiple sources of exposures (agent-based modeling or ABM). The advent of diverse ABM platforms as well compelling applications in economics, ecology and sociology suggests that ABM is a promising computational architecture for modeling exposure-related behaviors. ABM can be used to study exposures that occur as the result of passive exposures to environmental sources, exposures that occur as the result of the agent’s behaviors (use of commercial products), and exposures that occur from other agents’ behaviors (bystander exposures). The investigation builds on the existing work to simulate interindividual variation in exposure-related characteristics in the US population contained in exposure programs such as LifeLine™, CARES, and SHEDS-Multimedia. Based on this work, models will be produced where the agents’ exposure-related behaviors are simulated using both first principles and existing data (e.g., the Consolidated Human Activity Database, NHANES, and the U.S. Census). The agent’s behaviors will be used to support exposure models of dose and to make testable predictions of behavior (activity patterns, product usage rates, etc.). If successful, ABM could support high-throughput exposure modeling of longitudinal, aggregate and cumulative exposures. These data in turn could support high throughput assessments of substances’ acute andchronic effects.
URLs/Downloads:
https://intlexposurescience.org/Public/Annual_Meeting/PastMeetings/2015_Meeting.aspx