Next Article in Journal
Fully Metallic Flat Lens Based on Locally Twist-Symmetric Array of Complementary Split-Ring Resonators
Previous Article in Journal
Experimental Investigation on the Cooling and Inerting Effects of Liquid Nitrogen Injected into a Confined Space
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Green Simulation of Pandemic Disease Propagation

Industrial Engineering & Operations Research Department, University of California at Berkeley, Berkeley, CA 94720-1777, USA
Mechanical and Industrial Engineering Department, Majmaah University, Majmaah 11952, Saudi Arabia
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;
Received: 21 March 2019 / Revised: 14 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
PDF [478 KB, uploaded 22 April 2019]


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Wilson, S.; Alabdulkarim, A.; Goldsman, D. Green Simulation of Pandemic Disease Propagation. Symmetry 2019, 11, 580.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top