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

Abstract 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.
PMID
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Authors

Mayor MeshTerms

Computer Simulation

Decision Support Techniques

Epidemics

Keywords

analytics

big data

data management

epidemics

public health decision-making

simulation ensembles

Journal Title the journal of infectious diseases
Publication Year Start




PMID- 28830107
OWN - NLM
STAT- MEDLINE
DA  - 20170823
DCOM- 20170825
LR  - 20170825
IS  - 1537-6613 (Electronic)
IS  - 0022-1899 (Linking)
VI  - 214
IP  - suppl_4
DP  - 2016 Dec 01
TI  - epiDMS: Data Management and Analytics for Decision-Making From Epidemic Spread
      Simulation Ensembles.
PG  - S427-S432
LID - 10.1093/infdis/jiw305 [doi]
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.
FAU - Liu, Sicong
AU  - Liu S
AD  - School of Informatics, and Decision Systems Engineering, Arizona State
      University, Tempe.
FAU - Poccia, Silvestro
AU  - Poccia S
AD  - Computer Science Department, University of Torino, Italy.
FAU - Candan, K Selcuk
AU  - Candan KS
AD  - School of Informatics, and Decision Systems Engineering, Arizona State
      University, Tempe.
FAU - Chowell, Gerardo
AU  - Chowell G
AD  - School of Public Health, Georgia State University, Atlanta.
FAU - Sapino, Maria Luisa
AU  - Sapino ML
AD  - Computer Science Department, University of Torino, Italy.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - United States
TA  - J Infect Dis
JT  - The Journal of infectious diseases
JID - 0413675
SB  - AIM
SB  - IM
MH  - Communicable Diseases/*epidemiology/*transmission
MH  - *Computer Simulation
MH  - *Decision Support Techniques
MH  - *Epidemics
MH  - Humans
OTO - NOTNLM
OT  - analytics
OT  - big data
OT  - data management
OT  - epidemics
OT  - public health decision-making
OT  - simulation ensembles
EDAT- 2017/08/24 06:00
MHDA- 2017/08/26 06:00
CRDT- 2017/08/24 06:00
AID - 2527908 [pii]
AID - 10.1093/infdis/jiw305 [doi]
PST - ppublish
SO  - J Infect Dis. 2016 Dec 1;214(suppl_4):S427-S432. doi: 10.1093/infdis/jiw305.