Close

Behavior Model Calibration for Epidemic Simulations.

Abstract

Computational epidemiologists frequently employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. The agents used in such simulations rarely capture the real-world decision-making of human beings. An absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this paper, we address this problem by developing a methodology to create and calibrate an agent decision making model for a large multi-agent simulation, using survey data. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.

MIDAS Network Members

Citation:

Singh M, Marathe A, Marathe MV, Swarup S. (2018). Behavior Model Calibration for Epidemic Simulations. Proceedings of the ... International Joint Conference on Autonomous Agents and Multiagent Systems : AAMAS. International Joint Conference on Autonomous Agents and Multiagent Systems, (2018)