TY - GEN
T1 - Integrating predictive analytics into a spatiotemporal epidemic simulation
AU - Bryan, Chris
AU - Wu, Xue
AU - Mniszewski, Susan
AU - Ma, Kwan Liu
N1 - Funding Information:
Acknowledgments: This research has been sponsored in part by the U.S. National Science Foundation via grant NSF IIS-1320229, the U.S. Department of Energy through grant DE-FC02-12ER260n, and the Los Alamos National Laboratory Information Science and Technology Institute.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/4
Y1 - 2015/12/4
N2 - The Epidemic Simulation System (EpiSimS) is a scalable, complex modeling tool for analyzing disease within the United States. Due to its high input dimensionality, time requirements, and resource constraints, simulating over the entire parameter space is unfeasible. One solution is to take a granular sampling of the input space and use simpler predictive models (emulators) in between. The quality of the implemented emulator depends on many factors: robustness, sophistication, configuration, and suitability to the input data. Visual analytics can be leveraged to provide guidance and understanding of these things to the user. In this paper, we have implemented a novel interface and workflow for emulator building and use. We introduce a workflow to build emulators, make predictions, and then analyze the results. Our prediction process first predicts temporal time series, and uses these to derive predicted spatial densities. Integrated into the EpiSimS framework, we target users who are non-experts at statistical modeling. This approach allows for a high level of analysis into the state of the built emulators and their resultant predictions. We present our workflow, models, the associated system, and evaluate the overall utility with feedback from EpiSimS scientists.
AB - The Epidemic Simulation System (EpiSimS) is a scalable, complex modeling tool for analyzing disease within the United States. Due to its high input dimensionality, time requirements, and resource constraints, simulating over the entire parameter space is unfeasible. One solution is to take a granular sampling of the input space and use simpler predictive models (emulators) in between. The quality of the implemented emulator depends on many factors: robustness, sophistication, configuration, and suitability to the input data. Visual analytics can be leveraged to provide guidance and understanding of these things to the user. In this paper, we have implemented a novel interface and workflow for emulator building and use. We introduce a workflow to build emulators, make predictions, and then analyze the results. Our prediction process first predicts temporal time series, and uses these to derive predicted spatial densities. Integrated into the EpiSimS framework, we target users who are non-experts at statistical modeling. This approach allows for a high level of analysis into the state of the built emulators and their resultant predictions. We present our workflow, models, the associated system, and evaluate the overall utility with feedback from EpiSimS scientists.
KW - Epidemic Visualization
KW - Predictive Modeling
KW - Spatial-Temporal Systems
KW - Visual Analytics
UR - http://www.scopus.com/inward/record.url?scp=84962788710&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962788710&partnerID=8YFLogxK
U2 - 10.1109/VAST.2015.7347626
DO - 10.1109/VAST.2015.7347626
M3 - Conference contribution
AN - SCOPUS:84962788710
T3 - 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
SP - 17
EP - 24
BT - 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
A2 - Chen, Min
A2 - Andrienko, Gennady
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015
Y2 - 25 October 2015 through 30 October 2015
ER -