Particle filtering in a SEIRV simulation model of H1N1 influenza

Anahita Safarishahrbijari, Trisha Lawrence, Richard Lomotey, Juxin Liu, Cheryl Waldner, Nathaniel Osgood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Numerous studies have been conducted using simulation models to predict the epidemiological spread of H1N1 and understand intervention trade-offs. However, existing models are generally not very accurate in H1N1 model predictions. In this report, we examine the impact of using particle filtering in a compartmental SEIRV (susceptible, exposed, infected, recovered and vaccinated) model which considers the impact of vaccination on the outbreak in the province of Manitoba. For the purpose of evaluating the performance of the particle filtering method, this work further compares the ability of particle filtering and traditional calibration to anticipate the evolution of the outbreak. Preliminary simulated results indicate that the particle filtering approach outperforms the calibration method in terms of the discrepancy between empirical data and model data.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781467397438
StatePublished - Feb 16 2016
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


OtherWinter Simulation Conference, WSC 2015
Country/TerritoryUnited States
CityHuntington Beach

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


Dive into the research topics of 'Particle filtering in a SEIRV simulation model of H1N1 influenza'. Together they form a unique fingerprint.

Cite this