Abstract

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
Volume214
DOIs
StatePublished - Dec 1 2016

Fingerprint

Decision Making
Emergencies
Information Systems
Economics
Delivery of Health Care
Health

Keywords

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

ASJC Scopus subject areas

  • Immunology and Allergy
  • Infectious Diseases

Cite this

EpiDMS : Data management and analytics for decision-making from epidemic spread simulation ensembles. / Liu, Sicong; Poccia, Silvestro; Candan, Kasim; Chowell, Gerardo; Sapino, Maria Luisa.

In: Journal of Infectious Diseases, Vol. 214, 01.12.2016, p. S427-S432.

Research output: Contribution to journalArticle

Liu, Sicong ; Poccia, Silvestro ; Candan, Kasim ; Chowell, Gerardo ; Sapino, Maria Luisa. / EpiDMS : Data management and analytics for decision-making from epidemic spread simulation ensembles. In: Journal of Infectious Diseases. 2016 ; Vol. 214. pp. S427-S432.
@article{a21522019edc427682b86160265ea344,
title = "EpiDMS: Data management and analytics for decision-making from epidemic spread simulation ensembles",
abstract = "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.",
keywords = "Analytics, Big data, Data management, Epidemics, Public health decision-making, Simulation ensembles",
author = "Sicong Liu and Silvestro Poccia and Kasim Candan and Gerardo Chowell and Sapino, {Maria Luisa}",
year = "2016",
month = "12",
day = "1",
doi = "10.1093/infdis/jiw305",
language = "English (US)",
volume = "214",
pages = "S427--S432",
journal = "Journal of Infectious Diseases",
issn = "0022-1899",
publisher = "Oxford University Press",

}

TY - JOUR

T1 - EpiDMS

T2 - Data management and analytics for decision-making from epidemic spread simulation ensembles

AU - Liu, Sicong

AU - Poccia, Silvestro

AU - Candan, Kasim

AU - Chowell, Gerardo

AU - Sapino, Maria Luisa

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

KW - Analytics

KW - Big data

KW - Data management

KW - Epidemics

KW - Public health decision-making

KW - Simulation ensembles

UR - http://www.scopus.com/inward/record.url?scp=85015875467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015875467&partnerID=8YFLogxK

U2 - 10.1093/infdis/jiw305

DO - 10.1093/infdis/jiw305

M3 - Article

VL - 214

SP - S427-S432

JO - Journal of Infectious Diseases

JF - Journal of Infectious Diseases

SN - 0022-1899

ER -