EpiDMS: Data management and analytics for decision-making from epidemic spread simulation ensembles

Sicong Liu, Silvestro Poccia, Kasim Candan, Gerardo Chowell, Maria Luisa Sapino

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Background. Carefully calibrated large-scale computational models of epidemic spread represent a powerful tool to support the decision-making process during epidemic emergencies. Epidemic models are being increasingly used for generating forecasts of the spatial-temporal progression of epidemics at different spatial scales and for assessing the likely impact of different intervention strategies. However, the management and analysis of simulation ensembles stemming from large-scale computational models pose challenges, particularly when dealing with multiple interdependent parameters, spanning multiple layers and geospatial frames, affected by complex dynamic processes operating at different resolutions. Methods. We describe and illustrate with examples a novel epidemic simulation data management system, epiDMS, that was developed to address the challenges that arise from the need to generate, search, visualize, and analyze, in a scalable manner, large volumes of epidemic simulation ensembles and observations during the progression of an epidemic. Results and conclusions. epiDMS is a publicly available system that facilitates management and analysis of large epidemic simulation ensembles. epiDMS aims to fill an important hole in decision-making during healthcare emergencies by enabling critical services with significant economic and health impact.

Original languageEnglish (US)
Pages (from-to)S427-S432
JournalJournal of Infectious Diseases
StatePublished - Dec 1 2016


  • Analytics
  • Big data
  • Data management
  • Epidemics
  • Public health decision-making
  • Simulation ensembles

ASJC Scopus subject areas

  • Immunology and Allergy
  • Infectious Diseases


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