Understanding the Impact of Face Mask Usage Through Epidemic Simulation of Large Social Networks

  • Susan M. MniszewskiEmail author
  • Sara Y. Del Valle
  • Reid Priedhorsky
  • James M. Hyman
  • Kyle S. Hickman
Part of the Intelligent Systems Reference Library book series (ISRL, volume 52)


Evidence from the 2003 SARS epidemic and 2009 H1N1 pandemic shows that face masks can be an effective non-pharmaceutical intervention in minimizing the spread of airborne viruses. Recent studies have shown that using face masks is correlated to an individual’s age and gender, where females and older adults are more likely to wear a mask than males or youths. There are only a few studies quantifying the impact of using face masks to slow the spread of an epidemic at the population level, and even fewer studies that model their impact in a population where the use of face masks depends upon the age and gender of the population. We use a state-of-the-art agent-based simulation to model the use of face masks and quantify their impact on three levels of an influenza epidemic and compare different mitigation scenarios. These scenarios involve changing the demographics of mask usage, the adoption of mask usage in relation to a perceived threat level, and the combination of masks with other non-pharmaceutical interventions such as hand washing and social distancing. Our results shows that face masks alone have limited impact on the spread of influenza. However, when face masks are combined with other interventions such as hand sanitizer, they can be more effective. We also observe that monitoring social internet systems can be a useful technique to measure compliance. We conclude that educating the public on the effectiveness of masks to increase compliance can reduce morbidity and mortality.


Social Distance Face Mask Epidemic Curve Surgical Mask Hand Sanitizer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to acknowledge the Institutional Computing Program at Los Alamos National Laboratory for use of their HPC cluster resources.We thank Aron Culotta for his assistance with the Twitter data analysis. We also thank Geoffrey Fairchild for providing some useful articles.This research has been supported at Los Alamos National Laboratory under the Department of Energy contract DE-AC52-06NA25396 and a grant from NIH/NIGMS in the Models of Infectious Disease Agent Study (MIDAS) program (U01-GM097658-01).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Susan M. Mniszewski
    • 1
    Email author
  • Sara Y. Del Valle
    • 1
  • Reid Priedhorsky
    • 1
  • James M. Hyman
    • 2
  • Kyle S. Hickman
    • 2
  1. 1.Los Alamos National LaboratoryLos AlamosUSA
  2. 2.Tulane UniversityNew OrleansUSA

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