Spatial data mining to support pandemic preparedness

Authors:
Chris Bailey-Kellogg;Naren Ramakrishnan;Madhav V. Marathe
Affiliations:
Dartmouth College, Hanover, NH;Virginia Tech, Blacksburg, VA;Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2006

Citing 1
Cited 2

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

An interaction-based approach to computational epidemiology

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Coordinated clustering algorithms to support charging infrastructure design for electric vehicles

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Effective detection of and response to pandemic disease outbreaks require significant advances in data mining. Contributions to the recently held SIAM DM 2006 Workshop on Spatial Data Mining highlighted key challenges, directions, and progress in this context. We summarize here the main themes presented at the workshop as well as promising research directions for the data mining community.