Summary
Parameter estimation in nosocomial infections poses specific problems for estimation techniques. The mathematical description of the spread of nosocomial infections incorporates transmission as a dynamic part; the outcome is discrete and the amount of available information is usually small. We transfer an estimation technique developed previously for plant epidemics to nosocomial infections and demonstrate its application to a data set related to methicillin-resistant Staphyloccocus aureus.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stollenwerk, N., Briggs, K.M.: Master equation solution of a plant disease model. Phys. Lett. A, 274, 84–91 (2000).
Stollenwerk, N.: Parameter estimation in nonlinear systems with dynamic noise. In: Matthies M., Malchow H. & Kriz J. (eds) Integrative Systems Approaches to Natural and Social Sciences—System Science 2000. Springer, Berlin Heidelberg New York (2001).
Escolano, S., Golmard, S.L. et al.: A multi-state model for evolution of intensive care unit patients: prediction of nosocomial infections and deaths. Stat. Med., 19, 3465–82 (2000).
Meyer, E., Schwab, F. et al.: Temporal changes in bacterial resistance in German intensive care units, 2001–2003: data from the SARI (surveillance of antimicrobial use and antimicrobial resistance in intensive care units) project. J. Hosp. Infect., 60, 348–52 (2005).
Cooper, B.S., Stone, S.P. et al.: Isolation measures in the hospital management of methicillin resistant Staphylococcus aureus (MRSA): systematic review of the literature. BMJ, 329, 533 (2004).
Farrington, M., Trundle, C. et al.: Effects on nursing workload of different methicillinresistant Staphylococcus aureus (MRSA) control strategies. J. Hosp. Infect., 46, 118–22 (2000).
Cooper, B., Lipsitch, M.: The analysis of hospital infection data using hidden Markov models. Biostatistics, 5, 223–37 (2004).
Pelupessy, I., Bonten, M.J. et al.: How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proc. Natl. Acad. Sci. USA, 99, 5601–5. Epub 2002 Apr 9. (2002).
Austin, D.J., Anderson, R.M.: Transmission dynamics of epidemic methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci in England and Wales. J. Infect. Dis., 179, 883–891 (1999).
Lipsitch, M., Bergstrom, C.T. et al.: The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proc. Natl. Acad. Sci. USA, 97, 1938–43 (2000).
Sagel, U., Mikolajczyk, R.T. et al.: Using mandatory data collection on multiresistant bacteria for internal surveillance in a hospital. Biom. J., 46, 93 (2004).
Sagel, U., Mikolajczyk, R.T. et al.: Using mandatory data collection on multiresistant bacteria for internal surveillance in a hospital. Methods Inf. Med., 43, 483–485 (2004).
Farrington, M., Redpath, C. et al.: Winning the battle but losing the war: methicillinresistant Staphylococcus aureus (MRSA) infection at a teaching hospital. QJM, 91, 539–48 (1998).
Grundmann, H., Hori, S. et al.: Determining confidence intervals when measuring genetic diversity and the discriminatory abilities of typing methods for microorganisms. J. Clin. Microbiol., 39, 4190–2 (2001).
Enright, M.C., Robinson, D.A., Randler, G., Feil, E.J., Grundmann, H., Spratt, B.G. The evolutionary history of methicillin-resistant Staphylococcus aureus (MRSA), Proc. Natl. Acad. Sci. USA, 99, 7687–7692 (2002).
Robinson, D.A., Kearns, A.M. et al.: Re-emergence of early pandemic Staphylococcus aureus as a community-acquired methicillin-resistant clone. Lancet, 365, 1256–8 (2005).
Cooper, B.S., Medley, G.F. et al.: Methicillin-resistant Staphylococcus aureus in hospitals and the community: stealth dynamics and control catastrophes. Proc. Natl. Acad. Sci. USA, 101, 10223–8 (2004).
Fridkin, S.K., Hageman, J.C. et al.: Methicillin-resistant Staphylococcus aureus disease in three communities. N. Engl. J. Med., 352, 1436–44 (2005).
van Kampen, N.G.: Stochastic Processes in Physics and Chemistry. North-Holland, Amsterdam (1992).
Gardiner, C.W.: Handbook of Stochastic Methods. Springer, Berlin Heidelberg New York (1985).
Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comp. Phys., 22, 403–434 (1976).
Gillespie, D.T.: Monte Carlo simulation of random walks with residence time dependent transition probability rates. J. Comp. Phys., 28, 395–407 (1978).
Feistel, R.: Betrachtung der Realisierung stochastischer Prozesse aus automatentheoretischer Sicht. Wiss. Z. WPU Rostock, 26, 663–670 (1977).
Stollenwerk, N., Drepper, F., Siegel, H.: Testing nonlinear stochastic models on phytoplankton biomass time series. Ecological Modelling, 144, 261–277 (2001).
Stollenwerk, N., Maiden, M.C.J., Jansen, V.A.A.: Diversity in pathogenicity can cause outbreaks of meningococcal disease. Proc. Natl. Acad. Sci. USA, 101, 10229–10234 (2004).
Jansen, V.A.A., Stollenwerk, N., Jensen, H.J., Ramsay, M.E., Edmunds,W.J., Rhodes, C.J.: Measles outbreaks in a population with declining vaccine uptake. Science, 301, 804 (2003).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Birkhäuser Boston
About this chapter
Cite this chapter
Stollenwerk, N., Mikolajczyk, R. (2008). An Algorithm for Parameter Estimation in Nosocomial Infections. In: Deutsch, A., et al. Mathematical Modeling of Biological Systems, Volume II. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4556-4_3
Download citation
DOI: https://doi.org/10.1007/978-0-8176-4556-4_3
Publisher Name: Birkhäuser Boston
Print ISBN: 978-0-8176-4555-7
Online ISBN: 978-0-8176-4556-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)