Advertisement

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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4506)

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.

Keywords

real time epidemiology data assimilation Bayesian inference anomaly detection interactive visualization  surveillance 

References

  1. 1.
    Lawson, A.B., Kleinman, K. (eds.): Spatial and Syndromic Surveillance for Public Health. John Wiley & Sons, Chichester (2005)Google Scholar
  2. 2.
    Buehler, J.W., et al.: Framework for evaluating public health surveillance systems for early detection of outbreaks; recommendations from the CDC working group. MMWR CDC Surveill. Summ. 53, 1–16 (2004)Google Scholar
  3. 3.
    Proceedings of the 2002 National Syndromic Surveillance Conference, New York, USA, September 23-24, 2002. J. Urban Health 80 (2003)Google Scholar
  4. 4.
    Various: Abstracts from the 2005 Syndromic Surveillance Conference. Advances in Disease Surveillance 1 (2006)Google Scholar
  5. 5.
    Anderson, R.M., May, R.M.: Infectious Diseases of Humans. Oxford University Press, Oxford (1991)Google Scholar
  6. 6.
    Brauer, F., Castillo-Chavez, C.: Mathematical Models in Population Biology and Epidemiology. Springer, New York (2001)zbMATHGoogle Scholar
  7. 7.
    Diekmann, O., Heesterbeek, J.A.: Mathematical Epidemiology of Infectious Diseases: model building, analysis and interpretation. John Wiley & Sons, Chichester (2000)Google Scholar
  8. 8.
    Wallinga, J., Teunis, P.: Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol. 160, 509–516 (2004)CrossRefGoogle Scholar
  9. 9.
    Lipsitch, M., et al.: Transmission dynamics and control of severe acute respiratory syndrome. Science 300, 1966–1970 (2003)CrossRefGoogle Scholar
  10. 10.
    Riley, S., Fraser, C., Donnelly, C.A., et al.: Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science 300, 1961–1966 (2003)CrossRefGoogle Scholar
  11. 11.
    Chowell, G., et al.: Transmission Dynamics of the Great Influenza Pandemic of 1918 in Geneva, Switzerland: Assessing the Effects of Hypothetical Interventions. J. Theor. Biol. 241, 193–204 (2006)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Ferguson, N.M., Donnelly, C.A., Anderson, R.M.: Transmission dynamics and epidemiology of dengue: insights from age-stratified sero-prevalence surveys. Phil. Trans. Roy. Soc. Lond. B 354, 757–768 (1999)CrossRefGoogle Scholar
  13. 13.
    Koopman, J.S., et al.: Determinants and predictors of dengue infection in Mexico. Am. J. Epidem. 133, 1168–1178 (1991)Google Scholar
  14. 14.
    Farrington, C.P., Whitaker, H.J.: Estimation of effective reproduction numbers for infectious diseases using serological survey data. Biostatistics 4, 621–632 (2003)zbMATHCrossRefGoogle Scholar
  15. 15.
    Hethcote, H.W.: The Mathematics of Infectious Diseases. SIAM Rev. 42, 599–653 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Nishiura, H., et al.: Transmission potential of primary pneumonic plague: time inhomogeneous evaluation based on historical documents of the transmission network. J. Epidemiol. Community. Health 60, 640–645 (2006)CrossRefGoogle Scholar
  17. 17.
    Committee on Modeling Community Containment for Pandemic Influenza: Modeling Community Containment for Pandemic Influenza: A letter report. National Academies Press, Washington (2006)Google Scholar
  18. 18.
    Castillo-Chavez, C., Feng, Z., Huang, W.: On the computation R0 and its role on global stability. In: Castillo-Chavez, C., et al. (eds.) Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction. The IMA Volumes in Mathematics and its Applications, pp. 229–250. Springer, Berlin (2002)Google Scholar
  19. 19.
    Heffernan, J.M., Smith, R.J., Wahl, L.: Perspectives on the basic reproductive ratio. Journal of the Royal Society, Interface the Royal Society 2, 281–293 (2005)CrossRefGoogle Scholar
  20. 20.
    van den Driessche, P., Watmough, J.: Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180, 29–48 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Cauchemez, S., et al.: Real-time estimates in early detection of SARS. Emerg. Infect. Dis. 12, 110–113 (2006)Google Scholar
  22. 22.
    Cauchemez, S., et al.: Estimating in real time the efficacy of measures to control emerging communicable diseases. Am. J. Epidemiol. 164, 591–597 (2006)CrossRefGoogle Scholar
  23. 23.
    Bettencourt, L.M.A., Ribeiro, R.M.: Real time Bayesian estimation of the epidemic potential of emerging infectious diseases, submitted (2007)Google Scholar
  24. 24.
    Chowell, G., Nishiura, H., Bettencourt, L.M.A.: Comparative estimation of the reproduction number for pandemic influenza from daily case notification data. J. R. Soc. Interface 4, 155–166 (2007)CrossRefGoogle Scholar
  25. 25.
    Bettencourt, L.M.A., et al.: The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models. Physica A 364, 513–536 (2006)CrossRefGoogle Scholar
  26. 26.
    Petitti, D.B.: Meta Analysis, Decision Analysis and Cost-effectiveness Analysis: Methods for Quantitative Synthesis in Medicine. Oxford University Press, New York (2000)Google Scholar
  27. 27.
  28. 28.
  29. 29.
  30. 30.
  31. 31.

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Luís M. A. Bettencourt
    • 1
  • Ruy M. Ribeiro
    • 1
  • Gerardo Chowell
    • 1
  • Timothy Lant
    • 2
  • Carlos Castillo-Chavez
    • 3
  1. 1.Theoretical Division and Center for Non-Linear Studies, Los Alamos National Laboratory, MS B284, Los Alamos NM 87545USA
  2. 2.Decision Theater, Arizona State University, PO Box 878409, Tempe AZ, 85287-8409USA
  3. 3.Department of Mathematics and Statistics, Arizona State University, PO Box 871804, Tempe AZ, 85287-1804USA

Personalised recommendations