Simulating patterns of life: More representative time-activity patterns that account for context
Background: Complex contributions of environment to health are intimately connected to human behavior. Modeling of human behaviors helps inform important policy decisions related to critical environmental and public health challenges. There are many approaches to human behavior modeling, all of which rely ultimately on portraying the time-activity patterns of individual humans--also known as their patterns of life. Current behavior models, however, rarely incorporate the context that surrounds individuals’ truly broad scope of activities and influences on those activities.
Objectives: We describe in detail a range of elements involved in modeling time-activity patterns and connect work in the social science field of behavior modeling with applications in exposure science and environmental health. We propose a framework for behavior modeling that considers the broad scope of activities required to generate more representative patterns of life and thus improve modeling that underlies understanding of environmental contributions to health and associated decisions to promote and protect public health.
Methods: We describe an agent-based modeling approach to generate a population’s schedules, filter the schedules, analyze the schedules, and interrogate results to inform action.
Discussion: We call for a centralized and standardized program to generate population schedules from which researchers can down-select for their specific studies.