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NISAC Tools: EpiSimS

EpiSimS: Epidemic Simulation System


EpiSimS is a stochastic, agent-based simulation engine that models the spread of disease in regions, allowing for the assessment of disease prevention, intervention, and response strategies. EpiSimS explicitly represents the daily movements and interactions of synthetic individuals in a city or region, including their interactions with others. It is used as an experimental test bed for analyzing the consequences, feasibility, and effectiveness of response options to disease outbreaks.

Risk-based assessment of alternatives.

EpiSimS-computed impact of school closures for a 1918-like pandemic influenza. As the schools re-open, new infection waves appear for B and C. (Click to enlarge)

Model Characteristics

EpiSimS models the United States as 15 regions, with each region comprised of about 20 million synthetic individuals. Because of its extremely high level of resolution, EpiSimS facilitates research into a wide variety of topics including:

  • Parameter values for within-host based on demographic or geographic characteristics
  • Targeted mitigation strategies by demographic or geospatial characteristics
  • Impact of disease on workforce absenteeism by more than 1,000 industry classification
  • Impact of reactive behavioral modifications such as fear-based home isolation
EpiSimS-computed attack rate by census tract for a 1918-like pandemic influenza in southern California.

EpiSimS-computed attack rate by census tract for a 1918-like pandemic influenza in southern California. The hotspots are highly correlated with the average household size. (Click to enlarge)

Where Is the Tool Applied?

NISAC has used EpiSimS to assess the population impacts of airborne infectious diseases such as pandemic influenza and smallpox and waterborne diseases such as cyclosporiasis. EpiSimS is also used to evaluate the feasibility and effectiveness of pharmaceutical and non-pharmaceutical interventions for short-term and long-term studies. In these applications, NISAC also used EpiSimS-generated rates for deaths and workforce absenteeism by industry to assess the potential economic impacts of a pandemic.

Confidence in the Model

EpiSimS is based on the activity generator developed for TRANSIMS (Barrett et al. 2000), a transportation simulation code used by the U.S. Department of Transportation to analyze traffic movement and derive the need for roads. The emergent person-to-person social network and mixing patterns are consistent with published survey studies (Del Valle et al. 2008). EpiSimS results have been published by several peer-review journals and presented to technical conferences and workshops.

EpiSimS-computed attack rate projections by state for a 1918-like pandemic influenza

EpiSimS-computed attack rate projections by state for a 1918-like pandemic influenza (Click to enlarge)

Model Documentation

  • Del Valle SY, Stroud PD, Smith JP, Mniszewski SM, Riese JM, Sydoriak SJ, Kubicek DA. EpiSimS: Epidemic Simulation System. 2006; Los Alamos Unlimited Release (LAUR) 06-06714.

Selected Validation References

  • Barrett CL, Beckman RJ, Berkbigler KP, Eubank SG, Henson KM, Kubicek DA, Romero PR, Smith JP, et al. TRANSIMS: Transportation Analysis Simulation. 2000; LAUR 00-1725.
  • Barrett CL, Eubank SG & Smith JP. If Smallpox Strikes Portland. Scientific American, 2005; 29: 54-61.
  • Eubank S, Goclu H, Kumar A, Marathe M, Srinivasan A, Totoczkal Z, Wang N. Modelling disease outbreaks in realistic urban social networks. Nature, 2004; 429:180-184.
  • Stroud PD, Sydoriak SJ, Riese JM, Smith JP, Mniszewski SM, Romero PR. Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing. Mathematical Bioscicences, 2006; 203: 301-318.
  • Stroud PD, Del Valle SY, Mniszewski SM, Riese JM, Sydoriak SJ. Pandemic Influenza Impact Analysis Report: Simulation of Disease Spread and Intervention Effectiveness. 2006; LAUR 06-706.
  • Del Valle S, Hyman JM, Hethcote HW, Eubank SG. Mixing Patterns Between Age Groups Using Social Networks. Social Networks , 2007; 29: 539-554.
  • Stroud P, Del Valle S, Sydoriak S, Mniszewski S, Riese J. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. Journal of Artificial Societies and Social Simulation 2007; 10 4 9.
  • Stroud P, Mniszewski S, Del Valle S, Sydoriak S, Riese J. Earlier and Faster Production of Vaccine for Pandemic Mitigation, 2007; LAUR 07-0534.
  • Mniszewski S, Del Valle S, Stroud P, Sydoriak S, Riese J. Effect of Home Transmission Reduction on Pandemic Influenza 2007; LAUR 07-1557.
  • Riese J, Stroud P, Mniszewski S, Del Valle S, Sydoriak S. Sensitivity Analysis of Antiviral Stockpile for Pandemic Influenza Mitigation. 2007; LAUR 07-1989.
  • Mniszewski S, Del Valle S, Stroud P, Riese J, Sydoriak S. Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available. Computational & Mathematical Organization Theory, 2008; 14:209-221.
  • Henson KM, Cuellar L, Kubicek DA, Tallman CD. Comparing travel patterns for local area using both a local and national travel survey for homeland security modeling. 2008; LAUR 08-04943.
  • Mniszewski S, Del Valle S, Stroud P, Riese J, Sydoriak S. EpiSimS Simulation of a Multi-component Strategy for Pandemic Influenza. Proceedings of the 2008 Spring Simulation Multiconference, 2008.
  • Del Valle S, Stroud P, Mniszewski S. Dynamic Contact Patterns and Social Structure in Realistic Social Networks, Social Networks: Development, Evaluation and Influence. Nova Science Publishers, 2008.
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