The analysis of hospital infection data using hidden Markov models

Biostatistics

Volumn 5, Issue 2, 2004, Pages 223-237

  Cooper, Ben ^a,b   Lipsitch, Marc ^a  

^a Boston University Medical Campus   (United States)

^b Statistics Commission   (United Kingdom)

Author keywords

Count data; Hidden Markov models; Hospital epidemiology; Interrupted time series; SIS epidemic model; Time series

Indexed keywords

ANTIBIOTIC RESISTANCE; ARTICLE; BACTERIAL INFECTION; BIOLOGICAL MODEL; COMPARATIVE STUDY; CROSS INFECTION; DISEASE TRANSMISSION; EPIDEMIC; GRAM NEGATIVE INFECTION; HUMAN; INTENSIVE CARE UNIT; PROBABILITY; STAPHYLOCOCCUS INFECTION;

CROSS INFECTION; DISEASE OUTBREAKS; DISEASE TRANSMISSION, HORIZONTAL; DRUG RESISTANCE, BACTERIAL; GRAM-NEGATIVE BACTERIAL INFECTIONS; GRAM-POSITIVE BACTERIAL INFECTIONS; HUMANS; INTENSIVE CARE UNITS; MARKOV CHAINS; MODELS, BIOLOGICAL; STAPHYLOCOCCAL INFECTIONS;

EID: 3142564766     PISSN: 14654644     EISSN: None     Source Type: Journal    
DOI: 10.1093/biostatistics/5.2.223     Document Type: Article

References (42)

1. AKAIKE, H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B. N. and Csàki, F. (eds), Second International Symposium on Inference Theory, Budapest: AkadÉmiai Kiadó, pp. 267-281.
2. AL-OSH, M. A. AND ALZAID, A. A. (1987). First-order integer-valued autoregressive (INAR(1)) process. Journal of Time Series Analysis 8, 261-275.
3. ALMUNEEF, M., BALTIMORE, R., FARREL, P., REAGAN-CIRINCIONE, P. AND DEMBRY, L. (2001). Molecular typing demonstrating transmission of gram-negative rods in a neonatal intensive care unit in the absence of a recognized epidemic. Clinical Infectious Diseases 32, 220-227.
4. AURANEN, K., ARJAS, E., LEINO, T. AND TAKALA, A. K. (2000). Transmission of pneumococcal carriage in families: a latent Markov process model for binary longitudinal data. Journal of the American Statistical Association 95, 1044-1053.
5. AUSTIN, D. J., BONTEN, M. J., WEINSTEIN, R. A., SLAUGHTER, S. AND ANDERSON, R. M. (1999). Vancomycin-resistant enterococci in intensive-care hospital settings: transmission dynamics, persistence, and the impact of infection control programs. Proceedings of the National Academy of Sciences USA 96, 6908-13.
6. BAILEY, N. T. J. (1975). The Mathematical Theory of Infectious Diseases. London: Charles Griffin.
7. BAQUERO, F., COQUE, T. AND CANTON, R. (2002). Allodemics. Lancet Infectious Diseases 2, 591-2.
8. BAUM, L. E., PETRIE, T., SOULES, G. AND WEISS, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics 41, 164-171.
9. BECKER, N. (1989), Analysis of Infectious Disease Data. London: Chapman and Hall.
10. BROOKHART, M. A., HUBBARD, A. E., VAN DER LAAN, M. J., COLFORD, JR., J. M. AND EISENBERO, J. N. (2002). Statistical estimation of parameters in a disease transmission model: analysis of a Cryptosporidium outbreak. Statistics in Medicine 21, 3627-38.
11. BROWN, S., BENNEYAN, J., THEOBALD, D., SANDS, K., HAHN, M., POTTER-BYNOE, G., STELLING, J., O'BRIEN, T. AND GOLDMANN, D. (2002). Binary cumulative sums and moving averages in nosocomial infection cluster detection. Emerging Infections Diseases 8, 1426-32.
12. BUREAU, A., SHIBOSKI, S. AND HUGHES, J. P. (2003). Applications of continuous time hidden Markov models to the study of misclassified disease outcomes. Statistics in Medicine 22, 441-62.
13. CAMERON, A. C. AND TRIVEDI, P. K. (1998). Regression Analysis of Count Data. Cambridge: Cambridge University Press.
14. CARDINAL, M., ROY, R. AND LAMBERT, J. (1999). On the application of integer-valued time series models for the analysis of disease incidence. Statistics in Medicine 18, 2025-39.
15. COOPER, B. S., MEDLEY, G. F. AND SCOTT, G. M. (1999). Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. Journal of Hospital Infection 43, 131-47.
16. COOPER, B. S., STONE, S. P., KIBBLER, C. C., COOKSON, B. D., ROBERTS, J. A., MEDLEY, G. F., DUCKWORTH, G. J., LAI, R. AND EBRAHIM, S. (2003). Systematic review of isolation policies in the hospital management of methicillin-resistant Staphyhcoccus aureus. Health Technology Assessment 7, 1-194.
17. Cox, D. R. (1981). Statistical analysis of time series: some recent developments. Scandinavian Journal of Statistics 8, 93-115.
18. FLYNN, D. M., WEINSTEIN, R. A. AND KABINS, S. (1988). Infections with gram-negative bacilli in a cardiac surgery intensive care unit: the relative role of enterobacter. Journal of Hospital Infection 11 Suppl A, 367-73.
19. FRIDKIN, S. K., STEWARD, C. D., EDWARDS, J. R., PRYOR, E. R., MCGOWAN, JR, J. E., ARCHIBALD, L. K., GAYNES, R. P AND TENOVER, F. C. (1999). Surveillance of antimicrobial use and antimicrobial resistance in United States hospitals: project ICARE phase 2. Project Intensive Care Antimicrobial Resistance Epidemiology (ICARE) hospitals. Clinical Infectious Diseases 29, 245-52.
20. GRIMMET, G. R. AND STIRZAKER, D. R. (1992). Probability and Random Processes. Oxford: Oxford University Press.
21. GRUNWALD, G. K., HYNDMAN, R. J., TEDESCO, L. AND TWEEDIE, R. L. (2000). Non-gaussian conditional linear AR(1) models. Australian and New Zealand Journal of Statistics 42, 479-495.
22. HARVEY, A. C. AND FERNANDES, C. (1989). Time series models for count or qualitative observations. Journal of Business and Economic Statistics 7, 407-417.
23. IHAKA, R. AND GENTLEMAN, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics 5, 299-314.
24. KEELING, M. J. AND GRENFELL, B. T. (2000). Individual-based perspectives on R(0). Journal of Theoretical Biology 203, 51-61.
25. LAFAIX, C., CAMERLYNCK, P., MAR, I., BALDE, I. AND REY, M. (1969). Notion de pseudo-épidémie hospitalière. [The concept of a hospital pseudo-epidemic]. Bulletin de la Société Médicale d'Afrique Noire de Langue Française 14, 713-21.
26. LE STRAT, Y. AND CARRAT, F. (1999). Monitoring epidemiologic surveillance data using hidden Markov models. Statistics in Medicine 18, 3463-78.
27. LINDSEY, J. K. (1999). Models for Repeated Measurements. Oxford: Oxford University Press.
28. LIPSITCH, M., BERGSTROM, C. T. AND LEVIN, B. R. (2000). The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proceedings of the National Academy of Sciences USA 97, 1938-43.
29. LOPEZ-LOZANO, J. M., MONNET, D. L., YAGUE, A., BURGOS, A., GONZALO, N., CAMPILLOS, P. AND SAEZ, M. (2000). Modelling and forecasting antimicrobial resistance and its dynamic relationship to antimicrobial use: a time series analysis. International Journal of Antimicrobial Agents 14, 21-31.
30. MACDONALD, I. L. AND ZUCCHINI, W. (1997). Hidden Markov and Other Models for Discrete-valued Time Series. London: Chapman and Hall.
31. MCKENZIE, E. (1985). Some simple models for discrete variate time-series. Water Resources Bulletin 21, 645-650.
32. O'NEILL, P. D. AND ROBERTS, G. O. (1999). Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society: Series A 162, 121-129.
33. PELUPESSY, I., BONTEN, M. J. AND DIEKMANN, O. (2002). How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proceedings of the National Academy of Sciences USA 99, 5601-5605.
34. PLOWMAN, R., GRAVES, N., GRIFFIN, M., ROBERTS, J., SWAN, A. V., COOKSON, B. AND TAYLOR, L. (1999). The Socio-economic Burden of Hospital Acquired Infection. London: PHLS.
35. SATTEN, G. A. AND LONGINI, I. M. (1996). Markov chains with measurement error: estimating the true course of a marker of the progression of human immunodeficiency virus disease. Applied Statistics 45, 275-309.
36. SCHNABEL, R. B., KOONTZ, R. B. AND WEISS, B. (1985). A modular system of algorithms for unconstrained minimization. ACM Transactions on Mathematical Software 11, 419-440.
37. SCOTT, S. L. (2002). Bayesian methods for hidden Markov models: recursive computing in the 21st century. Journal of the American Statistical Association 97, 337-351.
38. SÉBILLE, V., CHEVRET, S. AND VALLERON, A. J. (1997). Modeling the spread of resistant nosocomial pathogens in an intensive-care unit. Infection Control and Hospital Epidemiology 18, 84-92.
39. SMITH, T. AND VOUNATSOU, P. (2003). Estimation of infection and recovery rates for highly polymorphic parasites when detectability is imperfect, using hidden Markov models. Statistics in Medicine 22, 1709-24.
40. TSAY, R. S. (1992). Model checking via parametric bootstraps in time series analysis. Applied Statistics 41, 1-15.
41. ZEGER, S. L. (1988). A regression model for time series of counts. Biometrika 75, 621-629.
42. ZEGER, S. L. AND QAQISH, B. (1988). Markov regression models for time series: a quasi-likelihood approach. Biometrics 44, 1019-31.