NOSOCOMIAL INFECTIONS AND THEIR MODELS: A NARRATIVE REVIEW
DOI:
https://doi.org/10.60787/tnamp.v24.685Keywords:
Nosocomial infections, Deterministic modeling, Stochastic Modeling, Hospital acquired infections, Hospital associated infectionsAbstract
Nosocomial infections also known as hospital acquired infections or healthcare associated infections pose significant burden on global healthcare system. These infections occur from interactions between patients, healthcare workers, the hospital environment and other users of the hospital. Mathematical modeling has proved to be an important tool for investigating the dynamics and assessing the impact of intervention strategies.
This narrative review focuses on deterministic and stochastic modeling of nosocomial infection transmission, with emphasis on variables and transmission pathways used in existing models. Relevant literatures were identified through search of major scientific database, using keywords related to models of nosocomial infections.
Although these modeling approaches contributed significantly to the study of the dynamics of nosocomial infections, they do have some limitations. This study reviewed existing models of nosocomial infections; highlighted key variables studied, and also identified gaps in the literature. The study revealed the need to include other contact patterns to enhance theunderstanding and control of nosocomial infections.
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References
McBryde, E.S., Bradley, L.C., Whitby, M., McElwain, D.L.(2004). An investigation of contact transmission of methicillin-resistant staphylococcus aureus. J Hosp infect 58(2), 104-8
Sandu, A. M., Ciubotaru, R. O., Iosub, M. V., and Petrariu, F. D. (2025). Healthcare-associated infections: The role of microbial evolution, artificial intelligence and environmental change. Infectious Diseases and Therapy. https://doi.org/10.1007/s40121-025-01143-0
World Health Organization. (2024). Global report on infection prevention and control. World Health Organization. https://www.who.int/publications/i/item/9789240103986
Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. The Lancet, 399(10325), 629–655. https://doi.org/10.1016/S0140-6736(21)02724-0
Onwuliri, C. D., Ezebialu, I. U., Adebisi, A., Eleje, G.U., Akinola, B., Ezebialu, C.U., Abdullahi, K., Okoro, B., Okoroafor, C., John, O., Gadanya, M., Alombah, F., Okechukwu, E., Swomen, H. and Okwor, T.J.(2025).
Systematic review and meta-analysis of the prevalence and types of health-care-associated infections in Nigeria. BMC Infectious Diseases, 25, Article 836. https://doi.org/10.1186/s12879-025-11246-1
Abubakar, U. (2020). Point-prevalence survey of hospital acquired infections in three acute care hospitals in Northern Nigeria. Antimicrobial Resistance and Infection Control, 9, Article 63. https://doi.org/10.1186/s13756-020-00722-9
Cooper, B.S., Medley, G.F., Scott, G.M. (1999). Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. J Hosp Infect 43(2), 131-47
Wang, X., and Ruan, S. (2017). Modeling nosocomial infections of methicillin-resistant Staphylococcus aureus with environmental contamination. Scientific Reports, 7(1), 580. https://doi.org/10.1038/s41598-017-00691-6
Farr, W., (1840). Progress of epidemics. Second report to the Registrar General on England, 91-98.
Okuonghae D., Omame A. (2020). Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria. Chaos Solitons Fractals, 139 (2020), 110032
Anderson, R. M., and May, R. M. (1991). Infectious diseases of humans: Dynamics and control. Oxford University Press.
Bergstrom, C. T., Lo, M., and Lipsitch, M. (2004). Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. Proceedings of the National Academy of Sciences of the United States of America, 101(36), 13285–13290. https://doi.org/10.1073/pnas.0402298101
Wang, J., Wang, P., Megal, P., Wang, Y., Zhuo, J., Lu, X., Ruan, S. (2011). Modelling the transmission dynamics of methicillin-resistant staphylococcus aureus in Beijing Tongren Hospital. J Hosp Infect 79(4): 302-308
Ozturk, B. A. (2025). Impact of environmental contamination on a fractional MRSA model. Bulletin of Biomathematics, 3(1), 62–78.
Allen, L. J. S. (2008). An introduction to stochastic epidemic models. In F. Brauer, P. van den Driessche, and J. Wu (Eds.), Mathematical Epidemiology (pp. 81–130). Springer. https://doi.org/10.1007/978-3-540-78911-6_3
Semmelweiss, I., (1861). Die tiologie, der Begriff und die Prophylaxis des KIndbettfiebers.
Ross, R. (1911). The prevention of malaria. John Murray.
Macdonald, G. (1957). The epidemiology and control of malaria. Oxford University Press.
Austin, D. J., Kristinsson, K. G. and Anderson, R. M. “The transmission dynamics of epidemic methicillin-resistant Staphylococcus aureus (MRSA),” Proceedings of the National Academy of Sciences, vol. 96, no. 12, pp. 6908–6913, 1999.
Boldin, B., Bonten, M. J. M., and Diekmann, O. (2007). Relative effects of barrier precautions and topical antibiotics on nosocomial bacterial transmission: Results of multi-compartment models. PLoS Computational Biology, 3(6), e119. https://doi.org/10.1371/journal.pcbi.0030119
Cooper, B. S., Medley, G. F., Stone, S. P., Kibbler, C. C., Cookson, B. D., Roberts, J. A., Duckworth, G., Lai, R., Ebrahim, S., Brown, E. M., French, G. L., and Charlett, A. (2004). Methicillin-resistant Staphylococcus aureus in hospitals and the community: Stealth dynamics and control catastrophes. Proceedings of the National Academy of Sciences, 101(27), 10223–10228. https://doi.org/10.1073/pnas.0303793101
McBryde, E. S., Pettitt, A. N., Cooper, B. S., and McElwain, D. L. S. (2007a). A stochastic mathematical model of methicillin-resistant Staphylococcus aureus transmission in an intensive care unit: Predicting the impact of interventions. Journal of Theoretical Biology, 245(3), 470–481. https://doi.org/10.1016/j.jtbi.2006.10.024
McBryde, E. S., Pettitt, A. N., and McElwain, D. L. S. (2007b). Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models. Journal of the Royal Society Interface, 4(15), 745–754. https://doi.org/10.1098/rsif.2007.0229
Chamchod, F., and Ruan, S. (2012). Modeling the spread of methicillin-resistant Staphylococcus aureus in hospitals: Transmission dynamics, control strategies, and parameter estimation. SIAM Journal on Applied Mathematics, 72(2), 596–623. https://doi.org/10.1137/110831195
Wang, X., Xiao, Y., Wang, J., and Lu, X. (2012). A mathematical model of effects of environmental contamination and presence of volunteers on hospital infections in China. Journal of Theoretical Biology, 293, 161–173. https://doi.org/10.1016/j.jtbi.2011.10.009
Browne, C., and Webb, G. F. (2015). A nosocomial epidemic model with infection of patients due to contaminated rooms. Discrete and Continuous Dynamical Systems – Series B, 12(4), 761–787. https://doi.org/10.3934/mbe.2015.12.761
Gokbulut, N., Farman, M., Hürdoğanoglu, U., Sultanoglu, N., Güler, E., Hınçal, E., and Süer, K. (2024). Dynamical analysis of methicillin-resistant Staphylococcus aureus infection in North Cyprus with optimal control: Prevalence and awareness. Scientific Reports, 14, 18531
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