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Green Simulation of Pandemic Disease Propagation

1
Industrial Engineering & Operations Research Department, University of California at Berkeley, Berkeley, CA 94720-1777, USA
2
Mechanical and Industrial Engineering Department, Majmaah University, Majmaah 11952, Saudi Arabia
3
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA
*
Author to whom correspondence should be addressed.
All authors contributed equally to this work.
Symmetry 2019, 11(4), 580; https://doi.org/10.3390/sym11040580
Received: 21 March 2019 / Revised: 14 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
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Abstract

This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previous scenario. A set of Monte Carlo experiments illustrates the effectiveness of the procedure. We also propose various extensions of the method, including its use to estimate the sensitivity of propagation characteristics in response to small changes in the infection probability. View Full-Text
Keywords: stochastic simulation; infectious disease; SIR model; variance reduction stochastic simulation; infectious disease; SIR model; variance reduction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wilson, S.; Alabdulkarim, A.; Goldsman, D. Green Simulation of Pandemic Disease Propagation. Symmetry 2019, 11, 580.

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