Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany

Biometrical Journal

Volumn 55, Issue 4, 2013, Pages 509-526

  Manitz, Juliane ^a   Höhle, Michael ^b  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

^b ROBERT KOCH INSTITUTE   (Germany)

Author keywords

Bayesian model; Campylobacter enteritis; INLA; Outbreak detection; Surveillance

Indexed keywords

BAYESIAN NETWORKS; DECISION MAKING; TIME SERIES;

BAYESIAN; BAYESIAN MODELLING; CAMPYLOBACTER ENTERITIS; CAMPYLOBACTERIOSIS; CAMPYLOBACTERS; COVARIATES; DETECTION ALGORITHM; INLA; OUTBREAK DETECTION; SURVEILLANCE;

STATISTICAL PROCESS CONTROL;

ADOLESCENT; ADULT; AGED; ALGORITHM; ARTICLE; BAYES THEOREM; BAYESIAN MODEL; CAMPYLOBACTER; CAMPYLOBACTER ENTERITIS; CAMPYLOBACTERIOSIS; CHILD; EPIDEMIC; EPIDEMIOLOGICAL MONITORING; FEMALE; GERMANY; HUMAN; INFANT; INLA; ISOLATION AND PURIFICATION; LABORATORY DIAGNOSIS; MALE; MICROBIOLOGY; MIDDLE AGED; NEWBORN; OUTBREAK DETECTION; PRESCHOOL CHILD; PROBABILITY; SURVEILLANCE; VERY ELDERLY; YOUNG ADULT;

BAYESIAN MODEL; CAMPYLOBACTER ENTERITIS; INLA; OUTBREAK DETECTION; SURVEILLANCE;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; BAYES THEOREM; CAMPYLOBACTER; CAMPYLOBACTER INFECTIONS; CHILD; CHILD, PRESCHOOL; DISEASE OUTBREAKS; EPIDEMIOLOGICAL MONITORING; FALSE POSITIVE REACTIONS; FEMALE; GERMANY; HUMANS; INFANT; INFANT, NEWBORN; MALE; MARKOV CHAINS; MIDDLE AGED; YOUNG ADULT;

EID: 84880042358     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201200141     Document Type: Article

References (36)

1. Baron, M. and Tartakovsky, A. G. (2006). Asymptotic optimality of change-point detection schemes in general continuous-time models. Sequential Analysis 25, 257-296.
2. Buchholz, U., Bernard, H., Werber, D., Böhmer, M. M., Remschmidt, C., Wilking, H., Deleré, Y., an der Heiden, M., Adlhoch, C., Dreesman, J., Ehlers, J., Ethelberg, S., Faber, M., Frank, C., Fricke, G., Greiner, M., Höhle, M., Ivarsson, S., Jark, U., Kirchner, M., Koch, J., Krause, G., Luber, P., Rosner, B., Stark, K. and Kühne, M. (2011). German outbreak of Escherichia coli O104:H4 associated with sprouts. New England Journal of Medicine 365, 1763-1770.
3. Dengler, R. (1997). Dampf und Tropfen. Betrachtungen rund um die Luftfeuchtigkeit. PLUS LUCIS 3, 14-18.
4. Deutscher Wetterdienst (2012). Climate data of Germany. Available at http://www.dwd.de
5. Eilers, P. H. C., Gampe, J., Marx, B. D. and Rau, R. (2008). Modulation models for seasonal time series and incidence tables. Statistics in Medicine 27, 3430-3441.
6. Farrington, C. P., Andrews, N. J., Beale, A. D. and Catchpole, M. A. (1996). A statistical algorithm for the early detection of outbreaks of infectious disease. Journal of the Royal Statistical Society, Series A (Statistics in Society) 159, 547-563.
7. Frank, C., Werber, D., Cramer, J. P., Askar, M., Faber, M., An Der Heiden, M., Bernard, H., Fruth, A., Prager, R., Spode, A., Wadl, M., Zoufaly, A., Jordan, S., Kemper, M. J., Follin, P., Müller, L., King, L. A., Rosner, B., Buchholz, U., Stark, K. and Krause, G. (2011). Epidemic profile of Shiga-toxin producing Escherichia coli O104:H4 outbreak in Germany. New England Journal of Medicine 365, 1771-1780.
8. Heisterkamp, S. H., Dekkers, A. L. M. and Heijne, J. C. M. (2006). Automated detection of infectious disease outbreaks: hierarchical time series models. Statistics in Medicine 25, 4179-4196.
9. Held, L. and Rue, H. (2010). Conditional and intrinsic autoregressions. In: Gelfand, A., Diggle, P. J., Guttorp, P. and Fuentes, M. (Eds.), Handbook of Spatial Statistics. CRC Press, Boca Raton, pp. 201-216.
10. Höhle, M. (2007). an R package for the monitoring of infectious diseases. Computational Statistics 22, 571-582.
11. Höhle, M. and Mazick, A. (2010). Aberration detection in R illustrated by Danish mortality monitoring. In: Kass-Hout, T. and Xaiohui, Z. (Eds.), Biosurveillance: Methods and Case Studies. CRC Press, Boca Raton, pp. 215-238.
12. Höhle, M. and Paul, M. (2008). Count data regression charts for the monitoring of surveillance time series. Computational Statistics and Data Analysis 52, 4357-4368.
13. Hulth, A., Andrews, N., Ethelberg, S., Dreesman, J., Faensen, D., van Pelt, W. and Schnitzler, J. (2010). Practical usage of computer-supported outbreak detection in five European countries. Eurosurveillance 15, pii=19658.
14. Hutwagner, L., Browne, T., Seeman, G. M. and Fleischauer, A. T. (2005). Comparing aberration detection methods with simulated data. Emerging Infectious Diseases 11, 314-316.
15. IfSG (2012). Gesetz zur Verhütung und Bekämpfung von Infektionskrankheiten beim Menschen. Available at http://www.gesetze-im-internet.de/ifsg/index.html
16. Jansen, D. A., Käsbohrer, D. A. and Alter, D. T. (2007). Campylobacter-jejuni-Infektionen treten 2007 vermehrt auf. Analyse der Situation durch RKI und BfR. Epidemiologisches Bulletin 36, 331-334.
17. Louis, V. R., Gillespie, I. A., O'Brien, S. J., Russek-Cohen, E., Pearson, A. D., and Colwell, R. R. (2005). Temperature-driven campylobacter seasonality in England and Wales. Applied and Enviromental Microbiology 71, 85-92.
18. Madigan, D. (2005). Bayesian data mining for health surveillance. In: Lawson, A. B. and Kleinman, K. (Eds.), Spatial and Syndromic Surveillance for Public Health. John Wiley & Sons, Ltd., Chichester, pp. 203-221.
19. Manitz, J. (2010). Automated detection of infectious disease outbreaks. Master's thesis, Department of Statistics, Ludwig-Maximilians University Munich, Germany. Available at http://epub.ub.uni-muenchen.de/11908/
20. Martínez-Beneito, M. A., Conesa, D., López-Quílez, A. and López-Maside, A. (2008). Bayesian Markov switching models for the early detection of influenza epidemics. Statistics in Medicine 27, 4455-4468.
21. Noufaily, A., Enki, D. G., Farrington, P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An improved algorithm for outbreak detection in multiple surveillance systems. Statistics in Medicine 32, 1206-1222.
22. Nylen, G., Dunstan, F., Palmer, S. R., Andersson, Y., Bager, F., Cowden, J., Feierl, G., Galloway, Y., Kapperud, G., Megraud, F., Molbak, K., Petersen, L. R. and Ruutu, P. (2002). The seasonal distribution of campylobacter infection in nine European countries and New Zealand. Epidemiology and Infection 128, 383-390.
23. Patrick, M. E., Christiansen, L. E., Waino, M., Ethelberg, S., Madsen, H. and Wegener, H. C. (2004). Effects of climate on incidence of Campylobacter spp. in humans and prevalence in broiler flocks in Denmark. Applied and Enviromental Microbiology 70, 7474-7480.
24. Riebler, A., Held, L. and Rue, H. (2012). Estimation and extrapolation of time trends in registry data - borrowing strength from related populations. Annals Applied Statistics 6, 304-333.
25. Robert Koch-Institute, (2011). Infektionsepidemiologisches Jahrbuch für 2010. Berlin.
26. Robert Koch-Institute (2012). Survstatrki.de. Available at http://www3.rki.de/SurvStat, last accessed June 13, 2012.
27. Rue, H. and Held, L. (2005). Gaussian Markov random fields: Theory and Applications, Volume 104. Monographs on Statistics and Applied Probability. London: Chapman & Hall.
28. Rue, H. and Martino, S. (2009). INLA: Functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation. R package version 0.0.
29. Rue, H., Martino, S. and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 71, 319-392.
30. Ruiz-Cárdenas, R., Krainski, E. T. and Rue, H. (2012). Direct fitting of dynamic models using integrated nested Laplace approximations INLA. Computational Statistics & Data Analysis 56, 1808-1828.
31. Schrödle, B., Held, L., Riebler, A. and Danuser, J. (2011). Using integrated nested Laplace approximations for the evaluation of veterinary surveillance data from Switzerland: a case-study. Journal of the Royal Statistical Society: Series C (Applied Statistics) 60, 261-279.
32. Sonesson, C. and Bock, D. (2003). A review and discussion of prospective statistical surveillance in public health. Journal of the Royal Statistical Society, Series A (Statistics in Society) 166, 5-21.
33. Statistisches Bundesamt (2007) Bevölkerung: Deutschland, Stichtag: 31.12.2007, Altersjahre. Available at http://www-genesis.destatis.de, last accessed February 22, 2012.
34. Stroup, D., Wharton, M., Kafadar, K. and Dean, A. G. (1993). Evaluation of a method for detecting aberrations in public health surveillance data. American Journal of Epidemiology 137, 373-380.
35. Stroup, D. F. and Thacker, S. B. (1993). A Bayesian approach to the detection of aberrations in public health surveillance data. Epidemiology 4, 435-443.
36. Unkel, S., Farrington, C. P., Garthwaite, P. H., Robertson, C., and Andrews, N. (2012). Statistical methods for the prospective detection of infectious disease outbreaks: a review. Journal of the Royal Statistical Society, Series A (Statistics in Society) 175, 49-82.