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.
@article{bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1113,
author = {Isabel Nat\'ario and M. Luc\'\i lia Carvalho},
title = {Addressing the problem of lack of representativeness on syndromic surveillance schemes},
journal = {Discussiones Mathematicae Probability and Statistics},
volume = {29},
year = {2009},
pages = {169-183},
zbl = {1208.62174},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1113}
}
Isabel Natário; M. Lucília Carvalho. Addressing the problem of lack of representativeness on syndromic surveillance schemes. Discussiones Mathematicae Probability and Statistics, Tome 29 (2009) pp. 169-183. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1113/
[000] [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.
[001] [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.
[002] [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.
[003] [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.
[004] [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.
[005] [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.
[006] [7] M.A. Stoto, M. Schonlau and L.T. Mariano, Syndromic surveillance: it is worth the effort?, Chance 17 (2004), 19-24.
[007] [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.
[008] [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. | Zbl 1109.92060
[009] [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.
[010] [11] J. Durbin and S.J. Koopman, Time Series Analysis by State Space Methods, Oxford University Press 2001. | Zbl 0995.62504
[011] [12] H. Caswell, Matrix Population Models - 2nd Edition, Sinauer Associates, Inc. Publishers 2001.
[012] [13] M. West and J. Harrison, Bayesian forecasting and dynamic models - 2nd edition. Springer 1997. | Zbl 0871.62026
[013] [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. | Zbl 05919872
[014] [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.
[015] [16] Departamento de Epidemiologia do INSA, Gripe 2007 - um estudo sobre comportamentos face à 'gripe' - relatório, Instituto Nacional de Saúde Dr. Ricardo Jorge 2007.
[016] [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.
[017] [18] http://www.gripenet.pt/