Calibrating an agent-based model of longitudinal human activity patterns using the Consolidated Human Activity Database.

Patterns of human behavior over extended periods of time are important for characterizing human exposure to hazardous chemicals. Because longitudinal behavior patterns for an individual are difficult to obtain, exposure-assessors have characterized such patterns by linking daily records from multiple individuals. In an earlier publication, we developed an alternative strategy that was based on agent-based simulation modeling.

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence.

Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior.