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Research Methods in Healthcare Epidemiology and Antimicrobial Stewardship—Mathematical Modeling

Part of: Research and Methodology in Infection Control

Published online by Cambridge University Press:  08 August 2016

Sean L. Barnes ,
Parastu Kasaie ,
Deverick J. Anderson  and
Michael Rubin
Sean L. Barnes*
Affiliation:
Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, Maryland
Parastu Kasaie
Affiliation:
Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
Deverick J. Anderson
Affiliation:
Department of Medicine, Duke University School of Medicine, Durham, North Carolina
Michael Rubin
Affiliation:
Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
*
Address correspondence to Sean L. Barnes, PhD, Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, University of Maryland, 4352 Van Munching Hall, 7699 Mowatt Ln, College Park, MD 20742-1815 ([email protected]).
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Abstract

Mathematical modeling is a valuable methodology used to study healthcare epidemiology and antimicrobial stewardship, particularly when more traditional study approaches are infeasible, unethical, costly, or time consuming. We focus on 2 of the most common types of mathematical modeling, namely compartmental modeling and agent-based modeling, which provide important advantages—such as shorter developmental timelines and opportunities for extensive experimentation—over observational and experimental approaches. We summarize these advantages and disadvantages via specific examples and highlight recent advances in the methodology. A checklist is provided to serve as a guideline in the development of mathematical models in healthcare epidemiology and antimicrobial stewardship.

Infect Control Hosp Epidemiol 2016;1–7

Type
SHEA White Papers
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 11 , November 2016 , pp. 1265 - 1271
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Bonten, MJ, Austin, DJ, Lipsitch, M. Understanding the spread of antibiotic resistant pathogens in hospitals: mathematical models as tools for control. Clin Infect Dis 2001;33:1739–1746.CrossRefGoogle ScholarPubMed
2. Caro, JJ, Briggs, AH, Siebert, U, Kuntz, KM, ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices—overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. Med Decis Making 2012;32:667–677.CrossRefGoogle ScholarPubMed
3. Doan, TN, Kong, DC, Kirkpatrick, CM, McBryde, ES. Optimizing hospital infection control: the role of mathematical modeling. Infect Control Hosp Epidemiol 2014;35:1521–1530.Google Scholar
4. Grundmann, H, Hellriegel, B. Mathematical modelling: a tool for hospital infection control. Lancet Infect Dis 2006;6:39–45.Google Scholar
5. Opatowski, L, Guillemot, D, Boelle, PY, Temime, L. Contribution of mathematical modeling to the fight against bacterial antibiotic resistance. Curr Opin Infect Dis 2011;24:279–287.Google Scholar
6. van Kleef, E, Robotham, JV, Jit, M, Deeny, SR, Edmunds, WJ. Modelling the transmission of healthcare associated infections: a systematic review. BMC Infect Dis 2013;13:294.CrossRefGoogle ScholarPubMed
7. Keeling, MJ, Rohani, P. Modeling Infectious Diseases in Humans and Animals. Princeton: Princeton University Press; 2008.CrossRefGoogle Scholar
8. Vynnycky, E, White, RG. An Introduction to Infectious Disease Modelling. New York: Oxford University Press; 2010.Google Scholar
9. Grassly, NC, Fraser, C. Mathematical models of infectious disease transmission. Nat Rev Microbiol 2008;6:477–487.Google Scholar
10. Riley, S. Large-scale spatial-transmission models of infectious disease. Science 2007;316:1298–1301.Google Scholar
11. North, MJ, Collier, NT, Vos, JR. Experiences creating three implementations of the repast agent modeling toolkit. ACM Trans Model Comput Simul 2006;16:1–25.CrossRefGoogle Scholar
12. AnyLogic. The AnyLogic Company website. http://www.xjtek.com. Published 2012. Accessed May 1, 2016.Google Scholar
13. NetLogo. The NetLogo website. http://ccl.northwestern.edu/netlogo/. Published 1999. Accessed May 1, 2016.Google Scholar
14. Sterman, JD. Systems dynamics modeling: tools for learning in a complex world. IEEE Eng Manag Rev 2002;30:42–42.CrossRefGoogle Scholar
15. Rahmandad, H, Sterman, J. Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Manag Sci 2008;54:998–1014.Google Scholar
16. An, G. Agent-based computer simulation and SIRs: building a bridge between basic science and clinical trials. Shock 2001;16:266–273.Google Scholar
17. Gu, W, Killeen, GF, Mbogo, CM, Regens, JL, Githure, JI, Beier, JC. An individual-based model of Plasmodium falciparum malaria transmission on the coast of Kenya. Trans R Soc Trop Med Hyg 2003;97:43–50.CrossRefGoogle ScholarPubMed
18. Hotchkiss, JR, Strike, DG, Simonson, DA, Broccard, AF, Crooke, PS. An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit. Crit Care Med 2005;33:168–176.Google Scholar
19. Zorzenon dos Santos, RM, Coutinho, S. Dynamics of HIV infection: a cellular automata approach. Phys Rev Lett 2001;87:168102.CrossRefGoogle ScholarPubMed
20. Kasaie, P, Andrews, JR, Kelton, WD, Dowdy, DW. Timing of tuberculosis transmission and the impact of household contact tracing: an agent-based simulation model. Am J Respir Crit Care Med 2014;189:845–852.Google Scholar
21. Macal, CM, North, MJ. Toward teaching agent-based simulation. In: Proceedings of the 2010 Winter Simulation Conference (WSC). Baltimore: WSC; 2010.Google Scholar
22. Kasaie, P, Kelton, WD. Guidelines for design and analysis in agent-based simulation studies. In: Proceedings of the 2015 Winter Simulation Conference: Social and Behavioral Simulation. Piscataway, NJ: IEEE Press; 2015.Google Scholar
23. Austin, DJ, Anderson, RM. Studies of antibiotic resistance within the patient, hospitals and the community using simple mathematical models. Philos Trans R Soc Lond B Biol Sci 1999;354:721–738.CrossRefGoogle ScholarPubMed
24. Beggs, CB, Noakes, CJ, Shepherd, SJ, Kerr, KG, Sleigh, PA, Banfield, K. The influence of nurse cohorting on hand hygiene effectiveness. Am J Infect Control 2006;34:621–626.CrossRefGoogle ScholarPubMed
25. Beggs, CB, Shepherd, SJ, Kerr, KG. Increasing the frequency of hand washing by healthcare workers does not lead to commensurate reductions in staphylococcal infection in a hospital ward. BMC Infect Dis 2008;8:114.Google Scholar
26. D’Agata, EM, Horn, MA, Ruan, S, Webb, GF, Wares, JR. Efficacy of infection control interventions in reducing the spread of multidrug-resistant organisms in the hospital setting. PLOS ONE 2012;7:e30170.Google Scholar
27. D’Agata, EM, Magal, P, Olivier, D, Ruan, S, Webb, GF. Modeling antibiotic resistance in hospitals: the impact of minimizing treatment duration. J Theor Biol 2007;249:487–499.CrossRefGoogle ScholarPubMed
28. Sebille, V, Chevret, S, Valleron, AJ. Modeling the spread of resistant nosocomial pathogens in an intensive-care unit. Infect Control Hosp Epidemiol 1997;18:84–92.CrossRefGoogle Scholar
29. Austin, DJ, Bonten, MJ, Weinstein, RA, Slaughter, S, Anderson, RM. Vancomycin-resistant enterococci in intensive-care hospital settings: transmission dynamics, persistence, and the impact of infection control programs. Proc Natl Acad Sci U S A 1999;96:6908–6913.CrossRefGoogle ScholarPubMed
30. Cooper, BS, Medley, GF, Scott, GM. Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. J Hosp Infect 1999;43:131–147.Google Scholar
31. Cooper, BS, Medley, GF, Stone, SP, et al. Methicillin-resistant Staphylococcus aureus in hospitals and the community: stealth dynamics and control catastrophes. Proc Natl Acad Sci U S A 2004;101:10223–10228.CrossRefGoogle ScholarPubMed
32. Grundmann, H, Hori, S, Winter, B, Tami, A, Austin, DJ. Risk factors for the transmission of methicillin-resistant Staphylococcus aureus in an adult intensive care unit: fitting a model to the data. J Infect Dis 2002;185:481–488.Google Scholar
33. McBryde, ES, Pettitt, AN, McElwain, DL. A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions. J Theor Biol 2007;245:470–481.Google Scholar
34. Robotham, JV, Jenkins, DR, Medley, GF. Screening strategies in surveillance and control of methicillin-resistant Staphylococcus aureus (MRSA). Epidemiol Infect 2007;135:328–342.Google Scholar
35. Bootsma, MC, Diekmann, O, Bonten, MJ. Controlling methicillin-resistant Staphylococcus aureus: quantifying the effects of interventions and rapid diagnostic testing. Proc Natl Acad Sci U S A 2006;103:5620–5625.Google Scholar
36. Forrester, M, Pettitt, AN. Use of stochastic epidemic modeling to quantify transmission rates of colonization with methicillin-resistant Staphylococcus aureus in an intensive care unit. Infect Control Hosp Epidemiol 2005;26:598–606.Google Scholar
37. Laskowski, M, Demianyk, BCP, Witt, J, Mukhi, SN, Friesen, MR, McLeod, RD. Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study. IEEE Trans Inf Technol Biomed 2011;15:877–889.Google Scholar
38. Barnes, S, Golden, B, Wasil, E. MRSA transmission reduction using agent-based modeling and simulation. INFORMS J Comput 2010;22:635–646.CrossRefGoogle Scholar
39. Barnes, SL, Morgan, DJ, Harris, AD, Carling, PC, Thom, KA. Preventing the transmission of multidrug-resistant organisms: modeling the relative importance of hand hygiene and environmental cleaning interventions. Infect Control Hosp Epidemiol 2014;35:1156–1162.CrossRefGoogle ScholarPubMed
40. Meng, Y, Davies, R, Hardy, K, Hawkey, P. An application of agent-based simulation to the management of hospital-acquired infection. J Simulat 2010;4:60–67.Google Scholar
41. Jaramillo, C, Taboada, M, Epelde, F, Rexachs, D, Luque, E. Agent based model and simulation of MRSA transmission in emergency departments. Procedia Comput Sci 2015;51:443–452.Google Scholar
42. Lee, BY, Singh, A, Bartsch, SM, et al. The potential regional impact of contact precaution use in nursing homes to control methicillin-resistant Staphylococcus aureus . Infect Control Hosp Epidemiol 2013;34:151–160.Google Scholar
43. Macal, CM, North, MJ, Collier, N, et al. Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation. J Transl Med 2014;12:124.CrossRefGoogle ScholarPubMed
44. Rubin, MA, Jones, M, Leecaster, M, et al. A simulation-based assessment of strategies to control Clostridium difficile transmission and infection. PLOS ONE 2013;8:e80671.Google Scholar
45. Barnes, S, Golden, B, Wasil, E, Furuno, J, Harris, A. An application of factorial design to compare the relative effectiveness of hospital infection control measures. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu M, eds., Proceedings of the 2011 Winter Simulation Conference, 2011; 1283–1294.CrossRefGoogle Scholar
46. Montville, R, Chen, Y, Schaffner, DW. Risk assessment of hand washing efficacy using literature and experimental data. Int J Food Microbiol 2002;73:305–313.Google Scholar
47. Eubank, S, Guclu, H, Kumar, VS, et al. Modelling disease outbreaks in realistic urban social networks. Nature 2004;429:180–184.Google Scholar
48. Barnes, SL, Harris, AD, Golden, BL, Wasil, EA, Furuno, JP. Contribution of interfacility patient movement to overall methicillin-resistant Staphylococcus aureus prevalence levels. Infect Control Hosp Epidemiol 2011;32:1073–1078.Google Scholar
49. Hetem, DJ, Bootsma, MC, Bonten, MJ. Prevention of surgical site infections: decontamination with mupirocin based on preoperative screening for Staphylococcus aureus carriers or universal decontamination? Clin Infect Dis 2016;62:631–636.Google Scholar
50. Lee, BY, Wong, KF, Bartsch, SM, et al. The Regional Healthcare Ecosystem Analyst (RHEA): a simulation modeling tool to assist infectious disease control in a health system. J Am Med Inform Assoc 2013;20:e139–e146.Google Scholar
51. Slayton, RB, Toth, D, Lee, BY, et al. Vital signs: estimated effects of a coordinated approach for action to reduce antibiotic-resistant infections in health care facilities— United States. MMWR Morb Mortal Wkly Rep 2015;64:826–831.Google Scholar
52. Smith, DL, Dushoff, J, Perencevich, EN, Harris, AD, Levin, SA. Persistent colonization and the spread of antibiotic resistance in nosocomial pathogens: resistance is a regional problem. Proc Natl Acad Sci U S A 2004;101:3709–3714.CrossRefGoogle ScholarPubMed
53. Bartsch, SM, Huang, SS, Wong, KF, Avery, TR, Lee, BY. The spread and control of norovirus outbreaks among hospitals in a region: a simulation model. Open Forum Infect Dis 2014;1:ofu030.Google Scholar
54. Lee, BY, Bartsch, SM, Wong, KF, et al. Simulation shows hospitals that cooperate on infection control obtain better results than hospitals acting alone. Health Aff (Millwood) 2012;31:2295–2303.CrossRefGoogle ScholarPubMed
55. Lee, BY, McGlone, SM, Wong, KF, et al. Modeling the spread of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks throughout the hospitals in Orange County, California. Infect Control Hosp Epidemiol 2015;32:562–572.CrossRefGoogle Scholar
56. Bobashev, GV, Goedecke, DM, Feng, Y, Epstein, JM. A hybrid epidemic model: combining the advantages of agent-based and equation-based approaches. In: Henderson SG, Biller B, Hsieh M-H, Shortle J, Tew JD, Barton RR, eds., Proceedings of the 2007 Winter Simulation Conference, 2007; 1532–1537.Google Scholar
57. Swinerd, C, McNaught, KR. Design classes for hybrid simulations involving agent-based and system dynamics models. Simul Model Pract Theory 2012;25:118–133.Google Scholar
58. Yu, B, Wang, J, McGowan, M, Vaidyanathan, G, Younger, K. Gryphon: a hybrid agent-based modeling and simulation platform for infectious diseases. In: Chai S-K, Salerno J, Mabry P, eds., Advances in Social Computing. Vol. 6007. Berlin: Springer; 2010:199–207.CrossRefGoogle Scholar
59. Wikipedia. Comparison of system dynamics software. https://en.wikipedia.org/wiki/Comparison_of_system_dynamics_software. Published 2016. Accessed May 31, 2016.Google Scholar
60. System Dynamics Society. Tools for system dynamics. http://tools.systemdynamics.org. Published 2016. Accessed May 31, 2016.Google Scholar
61. Wikipedia. Comparison of agent-based modeling software. https://en.wikipedia.org/wiki/Comparison_of_agent-based_modeling_software. Published 2016. Accessed May 31, 2016.Google Scholar
62. Agent-based models methodology and philosophy. Simulators. http://www.agent-based-models.com/blog/resources/simulators/. Published 2016. Accessed May 31, 2016.Google Scholar
63. Allan, B. Survey of agent based modeling and simulation tools v1.1. http://www.grids.ac.uk/Complex/ABMS/. Published 2010. Accessed May 31, 2016.Google Scholar