Addressing the problem of lack of representativeness on syndromic surveillance schemes

Isabel Natário; M. Lucília Carvalho

Discussiones Mathematicae Probability and Statistics (2009)

  • Volume: 29, Issue: 2, page 169-183
  • ISSN: 1509-9423

Abstract

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A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means. This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence of disease, and an observational process resulting from the monitoring. We then use resampling importance sampling estimation techniques, in a Bayesian framework, which enables us to estimate the population states and, thus, the corresponding disease incidence curves.

How to cite

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Isabel Natário, and M. Lucília Carvalho. "Addressing the problem of lack of representativeness on syndromic surveillance schemes." Discussiones Mathematicae Probability and Statistics 29.2 (2009): 169-183. <http://eudml.org/doc/277025>.

@article{IsabelNatário2009,
abstract = { A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means. This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence of disease, and an observational process resulting from the monitoring. We then use resampling importance sampling estimation techniques, in a Bayesian framework, which enables us to estimate the population states and, thus, the corresponding disease incidence curves. },
author = {Isabel Natário, M. Lucília Carvalho},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {syndromic surveillance; state space models; importance sampling},
language = {eng},
number = {2},
pages = {169-183},
title = {Addressing the problem of lack of representativeness on syndromic surveillance schemes},
url = {http://eudml.org/doc/277025},
volume = {29},
year = {2009},
}

TY - JOUR
AU - Isabel Natário
AU - M. Lucília Carvalho
TI - Addressing the problem of lack of representativeness on syndromic surveillance schemes
JO - Discussiones Mathematicae Probability and Statistics
PY - 2009
VL - 29
IS - 2
SP - 169
EP - 183
AB - A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means. This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence of disease, and an observational process resulting from the monitoring. We then use resampling importance sampling estimation techniques, in a Bayesian framework, which enables us to estimate the population states and, thus, the corresponding disease incidence curves.
LA - eng
KW - syndromic surveillance; state space models; importance sampling
UR - http://eudml.org/doc/277025
ER -

References

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  1. [1] D.L. Cooper, G.E. Smith, M. Regan, S. Large and P.P. Groenewegen, Tracking the spatial diffusion on influenza and norovirus using telehealth data: a spatiotemporal analysis of syndromic data, BMC Medicine (2008) 6:16. doi:10.1186/1741-7015-6-16. 
  2. [2] M.J. O'Connor, D. Buckeridge, M.K. Choy, M. Crubezy, Z. Pincus and M.A. Musen, BioSTORM: A System for Automated Surveillance of Diverse Data Sources, AMIA Annual Symposium Proceedings 2003. 
  3. [3] B.Y. Reis, C. Kirby, L.E. Hadden, K. Olson, A.J. McMurry, J.B. Daniel and K.D. Mandl, AEGIS: A robust and scalable real-time public health surveillance system, Journal of the American Medical Informatics Association 14 (2007), 581-588. 
  4. [4] J. Lombardo, H. Burkom, E. Elbert, S. Magruder, S.H. Lewis, W. Loschen, J. Sari, C. Sniegoski, R. Wojcik and J. Pavlin, A Systems Overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II), J Urban Health 80 (2 Suppl 1) (2003), i32-i42. 
  5. [5] J.S. Brownstein, C.C. Freifeld, B.Y. Reis and K.D. Mandl, Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project, PLoS Med 5 (7) (2008), e151. 
  6. [6] S.P. van Noort, M. Muehlen, H. Rebelo de Andrade, C. Koppeschaar, J.M. Lima Lourenço and M.G. Gomes, Gripenet: an internet-based system to monitor influenza-like illness uniformly across Europe, Euro Surveill. 12 (7) (2007), pii=722. 
  7. [7] M.A. Stoto, M. Schonlau and L.T. Mariano, Syndromic surveillance: it is worth the effort?, Chance 17 (2004), 19-24. 
  8. [8] S.T. Buckland, K.B. Newman, L. Thomas and N.B. Koesters, State-space models for the dynamics of wild animal populations, Ecological Modeling 171 (2004), 157-175. 
  9. [9] L. Thomas, S.T. Buckland, K.B. Newman and J. Harwood, A unified framework for modelling wild population dynamics, Australian New Zealand Journal Statistics 47 (2005), 19-34. Zbl1109.92060
  10. [10] K.B. Newman, S.T. Buckland, S.T. Lindley, L. Thomas and C. Fernández, Hidden process models for animal population dynamics, Ecological Applications 16 (2006), 74-86. 
  11. [11] J. Durbin and S.J. Koopman, Time Series Analysis by State Space Methods, Oxford University Press 2001. Zbl0995.62504
  12. [12] H. Caswell, Matrix Population Models - 2nd Edition, Sinauer Associates, Inc. Publishers 2001. 
  13. [13] M. West and J. Harrison, Bayesian forecasting and dynamic models - 2nd edition. Springer 1997. Zbl0871.62026
  14. [14] A. Doucet and A.M. Johansen, A Tutorial on Particle Filtering and Smoothing: Fifteen years Later, In Handbook of Nonlinear Filtering, eds D. Crisan, B. Rozovsky, Oxford University Press 2009. Zbl05919872
  15. [15] J. Liu and M. West, Combining parameter and state estimation in simulation-based filtering, In sequential Monte Carlo Methods in Practice, eds A Doucet, N Freitas, N Gordon, New-York: Springer-Verlag 2001. 
  16. [16] Departamento de Epidemiologia do INSA, Gripe 2007 - um estudo sobre comportamentos face à 'gripe' - relatório, Instituto Nacional de Saúde Dr. Ricardo Jorge 2007. 
  17. [17] Departamento de Epidemiologia do INSA, Médicos Sentinela, o que se fez em 2007 - relatório de actividades 21, Instituto Nacional de Saúde Dr. Ricardo Jorge 2009. 
  18. [18] http://www.gripenet.pt/ 

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