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epiDMS: Data Management and Analytics for Decision-Making From Epidemic Spread Simulation Ensembles.

Sicong Liu1, Silvestro Poccia2, K Selçuk Candan1

  • 1School of Informatics, and Decision Systems Engineering, Arizona State University, Tempe.

The Journal of Infectious Diseases
|August 24, 2017
PubMed
Summary

A new data management system, epiDMS, helps analyze large epidemic simulation ensembles. This tool supports crucial decision-making during public health emergencies by improving the management and analysis of complex epidemic data.

Keywords:
analyticsbig datadata managementepidemicspublic health decision-makingsimulation ensembles

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Area of Science:

  • Computational epidemiology
  • Health informatics
  • Data science

Background:

  • Large-scale computational models are vital for epidemic forecasting and intervention assessment.
  • Managing and analyzing large simulation ensembles presents significant data challenges.
  • Interdependencies, multi-scale dynamics, and diverse data frames complicate epidemic modeling.

Purpose of the Study:

  • To introduce epiDMS, a novel data management system for epidemic simulations.
  • To address challenges in generating, searching, visualizing, and analyzing large simulation ensembles.
  • To provide a scalable solution for managing epidemic simulation data.

Main Methods:

  • Development of the epiDMS data management system.
  • Illustrative examples demonstrating system capabilities.
  • Focus on scalable data handling for simulation ensembles and observations.

Main Results:

  • epiDMS is a publicly available system.
  • The system facilitates the management and analysis of large epidemic simulation ensembles.
  • It addresses critical needs in decision-making during healthcare emergencies.

Conclusions:

  • epiDMS offers a solution for managing complex epidemic simulation data.
  • The system supports critical services with significant economic and health impact.
  • It enhances the utility of computational models in epidemic response.