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Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations

Ahmed, Abdulrahman A and Rahimian, M Amin and Roberts, Mark S Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations.

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Abstract

Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between ``digital twins''. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geo-spatial perspective using a synthetic population that is constructed based on the U.S. census data. In this paper, we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulation frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.


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Details

Item Type: Article
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Ahmed, Abdulrahman AABA173@pitt.eduABA1730000-0002-8736-8160
Rahimian, M AminRAHIMIAN@pitt.eduRAHIMIAN0000-0001-9384-1041
Roberts, Mark S
Event Type: Conference
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Refereed: No
Uncontrolled Keywords: cs.MA, cs.MA, cs.SI, stat.AP
Additional Information: To be presented in Winter Simulation Conference 2023, repository link: https://github.com/abdulrahmanfci/gpr-abm
Date Deposited: 04 Jun 2024 13:37
Last Modified: 06 Jun 2024 05:55
URI: http://d-scholarship.pitt.edu/id/eprint/46469

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