Work

Behavior Model Calibration for Epidemic Simulations

Year: 2018

Type: conference-paper

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.

Source: Adaptive Agents and Multi-Agents Systems

DOI: 10.65109/qiwe4800

Authors: Meghendra Singh, Achla Marathe, Madhav V. Marathe, Samarth Swarup

Institution: Virginia Tech

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Awards: Synthetic Information Systems for Better Informing Public Health Policymakers, Detection and characterization of critical under-immunized hotspots