BDCE 2014 - IEEE Workshop ? Big Data in Computational Epidemiology

Workshop ‘Big Data in Computational Epidemiology’ at IEEE BigData 2014 should be submitted through the IEEE BigData submission system (http://bit.ly/1oSAkAk). Please refer to IEEE manuscript templates (http://www.ieee.org/conferences_events/conferences...) for submission guidelines.
Important dates:
July 1, 2014: Due date for full workshop papers submission
Sept 1, 2014: Notification of paper acceptance to authors
Sept 25, 2014: Camera-ready of accepted papers
October 27-30, 2014: Conference
BCDE 2014 -- IEEE Workshop ? Big Data in Computational Epidemiology
Computational epidemiology aims to understand the spread of diseases and efficient strategies to mitigate their outbreak. It studies dynamics in socio-technical systems, where disease spread co-evolves with public health interventions as well as individual behavior. It has evolved from ODE models to networked models which apply agent based modeling and simulation methodologies. Computation of such high resolution models involves processing data sets that are massive, disparate, heterogeneous, evolving (at an ever increasing rate), and potentially unstructured and of various quality.
The workshop brings together researchers from epidemiology, data science, computational science, and health IT domains to tap the potential of emerging technologies in data intensive computations and analytical processing to advance the state of art in computational epidemiology. The central theme of how to manage, integrate, analyze, and visualize vast array of datasets has wider applications in the bio- and physical- simulation and informatics based sciences such as immunology, high energy physics, and, medical informatics.
The workshop welcomes original research related to computational models and methodologies developed for handling big data and their application to epidemiology. Topics of interest include:
Collection and generation of large scale epidemiological datasets
Management, provenance, storage, and archival of surveillance, synthetic, and experiment data
Analytics of spatial, temporal, relational, and semi-structured data
Mining social media data and other online data for public health
Simulation driven statistical methods for knowledge discovery and forecasting
Cloud, streaming, and high performance data intensive science
Semantic web tools, informatics, inference, and integration of public health data
Privacy in the big data era
Agent mining, multi-agent systems, agent based modeling, and behavior modeling in epidemiology