A scalable discrete event stochastic agent-based model of infectious disease propagation
Loading...
Authors
Sanchez, Paul J.
Sanchez, Susan M.
Subjects
Advisors
Date of Issue
2015-08
Date
Publisher
IEEE
Language
Abstract
We propose a newstochastic model of infectious disease propagation. This model tracks individual outcomes,
but does so without needing to create connectivity graphs for all members of the population. This makes
the model scalable to much larger populations than traditional agent-based models have been able to cope
with, while preserving the impact of variability during the critical early stages of an outbreak. This contrasts
favorably with aggregate deterministic models, which ignore variability, and negates the requirement to
assume “convenient” but potentially unrealistic distribution choices which aggregate stochastic models need
in order to be analytically tractable. Initial explorations with our new model show behaviors similar to
the observed course of Ebola outbreaks over the past 30+ years-while many outbreaks will fizzle out
relatively quickly, some appear to reach a critical mass threshold and can turn into widespread epidemics.
Type
Conference Paper
Description
The article of record as published may be found at http://dx.doi.org/10.1109/WSC.2015.7408160
Proceedings of the 2015 Winter Simulation Conference, L. Yilmaz, W. K V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, eds.
Proceedings of the 2015 Winter Simulation Conference, L. Yilmaz, W. K V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, eds.
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funding
Format
8 p.
Citation
Paul Sanchez, Susan Sanchez "A scalable discrete event stochastic agent-based model of infectious disease propagation," Proceedings of the 2015 Winter Simulation Conference, 2015 IEEE.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.