Paper Info
Title
Towards real time epidemiology: data assimilation, modeling and anomaly detection of health surveillance data streams
Abstract
An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes' theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.
Year
DOI
Venue
2007
10.1007/978-3-540-72608-1_8
BioSurveillance
Keywords
Field
DocType
health surveillance data stream,anomaly detection,present disease incidence data,dynamical probabilistic model,real-time data assimilation,disease evolution,disease dynamic,data assimilation,towards real time epidemiology,future outlook,future new case,data stream,real time,interactive visualization,bayesian inference
Data mining,Anomaly detection,Data stream mining,Public health surveillance,Bayesian inference,Computer science,Interactive visualization,Artificial intelligence,Data assimilation,Probabilistic logic,Machine learning,Bayes' theorem
Conference
Volume
ISSN
Citations 
4506
0302-9743
3
PageRank 
References 
Authors
0.66
2
5
Name
Order
Citations
PageRank
Luís M. A. Bettencourt1949.47
Ruy M. Ribeiro2234.99
G Chowell372.40
Tim Lant4142.75
Carlos Castillo-Chavez55294.79