Opinion statement
Antimicrobial agent effectiveness continues to be threatened by the rise and spread of pathogen strains that exhibit drug resistance. This challenge is most acute in healthcare facilities where the well-established connection between resistance and suboptimal antimicrobial use has prompted the creation of antimicrobial stewardship programs (ASPs). Mathematical models offer tremendous potential for serving as an alternative to controlled human experimentation for assessing the effectiveness of ASPs. Models can simulate controlled randomized experiments between groups of virtual patients, some treated with the ASP measure under investigation, and some without. By removing the limitations inherent in human experimentation, including health risks, study cohort size, possible number of replicates, and effective study duration, model simulations can provide valuable information to inform decisions regarding the design of new ASPs, as well as evaluation and improvement of existing ASPs. To date, the potential of mathematical modeling methods in evaluating ASPs is largely untapped and much work remains to be done to leverage this potential.
Similar content being viewed by others
References and Recommended Reading
Papers of particular interest, published recently, have been highlighted as: • Of importance
Centers for Disease Control and Prevention Antimicrobial Resistance. http://www.cdc.gov/drugresistance/about.html> acesso em 27:10–13.
Hand K. Antibiotic stewardship. Clin Med. 2013;13(5):499–503. doi:10.7861/clinmedicine.13-5-499.
Schechner V, Temkin E, Harbarth S, et al. Epidemiological interpretation of studies examining the effect of antibiotic usage on resistance. Clin Microbiol Rev. 2013;26(2):289–307. doi:10.1128/CMR.00001-13.
Centers for Disease Control and Prevention Antibiotic/Antimicrobial Resistance. http://www.cdc.gov/drugresistance/> acesso em.
Centers for Disease Control and Prevention. Antibiotic resistance threats in the United States, 2013. http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf> (2013). acesso em.
Angebault C, Andremont A. Antimicrobial agent exposure and the emergence and spread of resistant microorganisms: issues associated with study design. Eur J Clin Microbiol Infect Dis. 2013;32(5):581–95.
Smith RA, M’ikanatha NM, Read AF. Antibiotic resistance: a primer and call to action. Health Commun. 2015;30(3):309–14.
Hicks LA, Taylor Jr TH, Hunkler RJ. US outpatient antibiotic prescribing, 2010. N Engl J Med. 2013;368(15):1461–2.
Snyder GM, Patel PR, Kallen AJ, et al. Antimicrobial use in outpatient hemodialysis units. Infect Control Hosp Epidemiol. 2013;34(04):349–57.
Fishman N. Policy statement on antimicrobial stewardship by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), and the Pediatric Infectious Diseases Society (PIDS). Infect Control Hosp Epidemiol. 2012;33(04):322–7.
Bal AM, Gould IM. Antibiotic stewardship: overcoming implementation barriers. Curr Opin Infect Dis. 2011;24(4):357–62. doi:10.1097/QCO.0b013e3283483262.
Palmay L, Walker SA, Leis JA, et al. Antimicrobial stewardship programs: a review of recent evaluation methods and metrics. Curr Treatment Option Infect Dis. 2014;6(2):113–31.
Lawes T, Lopez-Lozano JM, Nebot C, et al. Turning the tide or riding the waves? Impacts of antibiotic stewardship and infection control on MRSA strain dynamics in a Scottish region over 16 years: non-linear time series analysis. BMJ Open. 2015;5(3):e006596-2014-006596. doi:10.1136/bmjopen-2014-006596.
Madaras-Kelly KJ, Remington RE, Sloan KL, et al. Guideline-based antibiotics and mortality in healthcare-associated pneumonia. J Gen Intern Med. 2012;27(7):845–52.
Xie J, Wang Y, Zheng X, et al. Modeling and forecasting Acinetobacter baumannii resistance to set appropriate use of cefoperazone-sulbactam: results from trend analysis of antimicrobial consumption and development of resistance in a tertiary care hospital. Am J Infect Control. 2015;43:861–4.
Chamchod F, Ruan S. Modeling methicillin-resistant Staphylococcus aureus in hospitals: transmission dynamics, antibiotic usage and its history. Theor Biol Med Model. 2012;9:25-4682-9-25. doi:10.1186/1742-4682-9-25.
Caudill L, Lawson B. A hybrid agent-based and differential equations model for simulating antibiotic resistance in a hospital ward. In: Pasupathy L, Kim S-H, Tolk A, Hill R, Kuhl M, editors. Proceedings of the 2013 Winter Simulation Conference, December 2013. IEEE; p. 1419–1431. doi:10.1109/WSC.2013.6721527. Links in-host pathogen and antibiotic dynamics with ward-level infection dynamics. This model is specifically designed to investigate a wide range of ASMs.
Caudill L, Lawson B. A Unified inter-host and in-host model of antibiotic resistance and infection spread in a hospital ward, University of Richmond Mathematics and Computer Science Technical Report 5–2015 http://scholarship.richmond.edu/mathcs-reports/1 (2015).
D’Agata E, Horn MA, Ruan S, et al. Efficacy of infection control interventions in reducing the spread of multidrug-resistant organisms in the hospital setting. PLoS One. 2012;7(2):e30170.
Deeny SR, Worby CJ, Tosas Auguet O, et al. Impact of mupirocin resistance on the transmission and control of healthcare-associated MRSA. J Antimicrob Chemother. 2015;70(12):3366–78. doi:10.1093/jac/dkv249.
Doan TN, Kong DC, Marshall C, et al. Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units. Virulence. 2015. doi:10.1080/21505594.2015.1076615.
Felton TW, Goodwin J, O’Connor L, et al. Impact of bolus dosing versus continuous infusion of Piperacillin and Tazobactam on the development of antimicrobial resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2013;57(12):5811–9. doi:10.1128/AAC.00867-13.
Geli P, Laxminarayan R, Dunne M, et al. “One-Size-Fits-All”? Optimizing treatment duration for bacterial infections. PLoS ONE. 2012;7(1):e29838.
Grima DT, Webb GF, D’Agata EM. Mathematical model of the impact of a nonantibiotic treatment for Clostridium difficile on the endemic prevalence of vancomycin-resistant Enterococci in a hospital setting. Comput Math Methods Med. 2012;2012:605861. doi:10.1155/2012/605861.
Hurford A, Morris AM, Fisman DN, et al. Linking antimicrobial prescribing to antimicrobial resistance in the ICU: before and after an antimicrobial stewardship program. Epidemics. 2012;4(4):203–10.
Kardaś-Słoma L, Boëlle PY, Opatowski L, et al. Antibiotic reduction campaigns do not necessarily decrease bacterial resistance: the example of methicillin-resistant Staphylococcus aureus. Antimicrob Agents Chemother. 2013;57(9):4410–6. doi:10.1128/AAC.00711-13. Links in-hospital dynamics to community dynamics in order to consider the effects of outpatient AM usage on inpatient resistance levels.
Obolski U, Hadany L. Implications of stress-induced genetic variation for minimizing multidrug resistance in bacteria. BMC Med. 2012;10:89-7015-10-89. doi:10.1186/1741-7015-10-89.
Sypsa V, Psichogiou M, Bouzala G, et al. Transmission dynamics of carbapenemase-producing Klebsiella pneumoniae and anticipated impact of infection control strategies in a surgical unit. PloS one. 2012;7(7):e41068.
Schultsz C, Bootsma MC, Loan HT, et al. Effects of infection control measures on acquisition of five antimicrobial drug-resistant microorganisms in a tetanus intensive care unit in Vietnam. Intensive Care Med. 2013;39(4):661–71.
Tan MW, Lye DC, Ng TM, et al. Mathematical model to quantify the effects of risk factors on carbapenem-resistant Acinetobacter baumannii. Antimicrob Agents Chemother. 2014;58(9):5239–44. doi:10.1128/AAC.02791-14.
Ternent L, Dyson RJ, Krachler A, et al. Bacterial fitness shapes the population dynamics of antibiotic-resistant and-susceptible bacteria in a model of combined antibiotic and anti-virulence treatment. J Theor Biol. 2015;372:1–11. Investigates the use of anti-virulence drugs to delay the initiation of AM treatment and to reduce the total volume of AM required for treatment success.
zur Wiesch PA, Kouyos R, Abel S, et al. Cycling empirical antibiotic therapy in hospitals: meta-analysis and models. PLoS Pathogen. 2014;10(6):e1004225.
Yakob L, Riley TV, Paterson DL, et al. Assessing control bundles for Clostridium difficile: a review and mathematical model. Emerg Microbe Infect. 2014;3(6):e43.
Hamilton KW, Fishman NO. Antimicrobial stewardship interventions: thinking inside and outside the box. Infect Dis Clin North Am. 2014;28(2):301–13.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Lester Caudill declares that he has no conflict of interest.
Joanna Wares declares that she has no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by the authors.
Additional information
This article is part of the Topical Collection on Antimicrobial Stewardship
Rights and permissions
About this article
Cite this article
Caudill, L., Wares, J.R. The Role of Mathematical Modeling in Designing and Evaluating Antimicrobial Stewardship Programs. Curr Treat Options Infect Dis 8, 124–138 (2016). https://doi.org/10.1007/s40506-016-0074-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40506-016-0074-8