Detecting disease outbreaks using a combined Bayesian network and particle filter approach

Journal of Theoretical Biology

Volumn 370, Issue , 2015, Pages 171-183

  Dawson, Peter ^a   Gailis, Ralph ^a   Meehan, Alaster ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

Disease surveillance; Epidemics; Plague; Syndromic surveillance

Indexed keywords

BAYESIAN ANALYSIS; DATA MINING; DISEASE INCIDENCE; DISEASE SPREAD; EPIDEMIC; NUMERICAL MODEL; PROBABILITY;

ARTICLE; BAYES THEOREM; DATA MINING; DISEASE MODEL; ELECTRONIC MEDICAL RECORD; EPIDEMIC; HUMAN; MATHEMATICAL PHENOMENA; METHODOLOGY; PARTICLE FILTER APPROACH; PLAGUE; PRIORITY JOURNAL; PROBABILITY; STATISTICAL CONCEPTS; ADULT; ALGORITHM; BIOLOGICAL MODEL; CHILD; FEMALE; HOSPITAL; MALE; MARKOV CHAIN;

ADULT; ALGORITHMS; BAYES THEOREM; CHILD; DISEASE OUTBREAKS; FEMALE; HOSPITALS; HUMANS; MALE; MARKOV CHAINS; MODELS, BIOLOGICAL; PROBABILITY;

EID: 84923238201     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2015.01.023     Document Type: Article

References (24)

1. Asmussen S., Nerman O., Olsson M. Fitting phase-type distributions via the EM algorithm. Scand. J. Stat. 1996, 23(4):419-441.
2. Cooper, G.F., Dash, D.H., Levander, J.D., Wong, W.K., Hogan, W.R., Wagner, M.M., 2004. Bayesian biosurveillance of disease outbreaks. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, UAI'04. AUAI Press, Arlington, Virginia, United States. pp. 94-103. URL . http://dl.acm.org/citation.cfm?id=1036843.1036855.
3. Dawson, P., Meehan, A., Aubron, C., Cheng, A., Lau, W., Gailis, R., Pierce, C., 2015. Modelling symptoms of inhalational anthrax and influenza pneumonia for disease surveillance, in preparation.
4. Doucet, A., de Freitas, N., Murphy, K.P., Russell, S.J., 2000. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks, CoRR abs/1301.3853.
5. Gani R., Leach S. Epidemiologic determinants for modeling pneumonic plague outbreaks. Emerg. Infect. Dis. 2004, 10(4):608-614.
6. Gordon N., Salmond D., Smith A.F.M. Novel approach to nonlinear non-Gaussian Bayesian state estimation. IEE Proc. F: Radar Signal Process. 1993, 140(2):107-113.
7. Jaynes E.T. Probability Theory: The Logic of Science 2003, Cambridge University Press, Cambridge, UK.
8. Jiang X., Cooper G.F. A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection. Data Min. Knowl. Discov. 2010, 20(3):328-360. 10.1007/s10618-009-0151-4.
9. Jiang, X., Cooper, G., Neill, D., 2009. Generalized amoc curves for evaluation and improvement of event surveillance. In: AMIA Annual Symposium Proceedings, pp. 281-285.
10. Kalman R.E. A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 1960, 82(Series D):35-45. URL http://www.cs.unc.edu/~welch/kalman/media/pdf/Kalman1960.pdf.
11. Kaufmann A.F., Meltzer M.I., Schmid G.P. The economic impact of a bioterrorist attack. are prevention and postattack intervention programs justifiable?. Emerg. Infect. Dis. 1997, 3(2):83-94. URL http://view.ncbi.nlm.nih.gov/pubmed/9204289.
12. Kaydos-Daniels S.C., Rojas Smith L., Farris T.R. Biosurveillance in outbreak investigations. Biosecur. Bioterror. 2013, 11(1):20-28. using Smart Source Parsing Mar; Epub 2013 Feb 28. 10.1089/bsp.2011.0109.
13. Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Gr. Stat. 1996, 5(1):1-25. 10.2307/1390750.
14. Ksinchun Chen D.Z., Yan P. Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense 2010, Springer, New York, USA.
15. Murphy, K., 2002. Dynamic Bayesian Networks: Representation, Inference and Learning (Ph.D. thesis). University of California, Berkeley.
16. Nachimuthu, S.K., Wong, A., Haug, P.J., 2010. Modeling glucose homeostasis and insulin dosing in an intensive care unit using dynamic Bayesian networks. In: AMIA Annual Symposium Proceedings, pp. 532-536. URL . http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041323/.
17. Ristic B., Arulampalam S., Gordon N. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House radar library 2004, Artech House.
18. Roberts M., Heesterbeek J. Model-consistent estimation of the basic reproduction number from the incidence of an emerging infection. J. Math. Biol. 2007, 55(5-6):803-816. 10.1007/s00285-007-0112-8.
19. Sartwell P.E. The distribution of incubation periods of infectious disease. Am. J. Hyg. 1950, 51(3):310-318.
20. Shaman J., Karspeck A. Forecasting seasonal outbreaks of influenza. Proc. Natl. Acad. Sci. U.S.A. 2012, 109(50):20425-20430. URL http://www.biomedsearch.com/nih/Forecasting-seasonal-outbreaks-influenza/23184969.html.
21. Skvortsov A., Ristic B. Monitoring and prediction of an epidemic outbreak using syndromic observations. Math. Biosci. 2012, 240(1):12-19. 10.1016/j.mbs.2012.05.010.
22. Wagner M., Tsui F., Cooper G., Espino J., Harkema H., Levander J., Villamarin R., Voorhees R., Millett N., Keane C., Dey A., Razdan M., Hu Y., Tsai M., Brown S., Lee B., Gallagher A., Potter M. Probabilistic, decision-theoretic disease surveillance and control. Online J. Public Health Inform. 2011, 3:3. 10.5210/ojphi.v3i3.3798.
23. Wong, W.K., Moore, A., Cooper, G., Wagner, M., 2003. Bayesian network anomaly pattern detection for disease outbreaks. In: Proceedings of the Twentieth International Conference on Machine Learning. AAAI Press, pp. 808-815 Washington DC, USA.
24. Wong, W.K., Moore, A., Cooper, G., Wagner, M., WSARE Datasets. URL , 2003. http://www.autonlab.org/autonweb/15959.html.