Towards real time epidemiology: Data assimilation, modeling and anomaly detection of health surveillance data streams

Luís M A Bettencourt, Ruy M. Ribeiro, Gerardo Chowell, Timothy Lant, Carlos Castillo-Chavez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIntelligence and Security Informatics
Subtitle of host publicationBiosurveillance - Second NSF Workshop, BioSurveillance 2007, Proceedings
PublisherSpringer Verlag
Pages79-90
Number of pages12
ISBN (Print)9783540726074
DOIs
StatePublished - 2007
Event2nd NSF BioSurveillance Workshop, BioSurveillance 2007 - New Brunswick, NJ, United States
Duration: May 22 2007May 22 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd NSF BioSurveillance Workshop, BioSurveillance 2007
CountryUnited States
CityNew Brunswick, NJ
Period5/22/075/22/07

Keywords

  • Anomaly detection
  • Bayesian inference
  • Data assimilation
  • Interactive visualization
  • Real time epidemiology
  • Surveillance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Bettencourt, L. M. A., Ribeiro, R. M., Chowell, G., Lant, T., & Castillo-Chavez, C. (2007). Towards real time epidemiology: Data assimilation, modeling and anomaly detection of health surveillance data streams. In Intelligence and Security Informatics: Biosurveillance - Second NSF Workshop, BioSurveillance 2007, Proceedings (pp. 79-90). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4506 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-72608-1_8