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

2016; Oxford University Press; Volume: 214; Issue: suppl 4 Linguagem: Inglês

10.1093/infdis/jiw305

1537-6613

Sicong Liu, Silvestro Roberto Poccia, K. Selçuk Candan, Gerardo Chowell, Maria Luisa Sapino,

Data-Driven Disease Surveillance

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. 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. 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.