Modelling the transmission of healthcare associated infections: a systematic review

BMC Infectious Diseases

William J Edmunds²  Sarah R Deeny¹  Mark Jit¹  Julie V Robotham¹  Esther van Kleef¹ 

[1] Modelling and Economics unit, Public Health England, Colindale, London, UK;Infectious Disease Epidemiology Department, Faculty of Epidemiology and Population Health, Centre of Mathematical Modelling, London School of Hygiene and Tropical Medicine, London, UK

关键词: Epidemiology;    Healthcare-associated infections;    Mathematical modelling;   

Others  :  1147510
DOI  :  10.1186/1471-2334-13-294

 received in 2012-11-29, accepted in 2013-06-21,  发布年份 2013

【 摘 要 】

Background

Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time.

Methods

MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings.

Results

In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.

The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data.

Conclusions

Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.

【 授权许可】

   
2013 van Kleef et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

【 参考文献 】

• [1]European Centre of Diseases Control: Annual Epidemiological Report on Communicable Diseases in Europe 2008: Report on the State of Communicable Diseases in the EU and EEA/EFTA Countries. Stockholm: European Centre of Disease Control; 2008.
• [2]MRSA and MSSA bacteraemia and C. difficile infection mandatory data (official statistics). http://www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/HCAI/LatestPublicationsFromMandatorySurveillanceMRSACDIAndGRE/ webcite
• [3]Walker AS, Eyre DW, Wyllie DH, Dingle KE, Harding RM, O’Connor L, Griffiths D, Vaughan A, Finney J, Wilcox MH, Crook DW, Peto TE A: Characterisation of clostridium difficile hospital ward-based transmission using extensive epidemiological data and molecular typing. PLoS Med 2012, 9:e1001172.
• [4]Riggs MM, Sethi AK, Zabarsky TF, Eckstein EC, Jump RLP, Donskey CJ: Asymptomatic carriers are a potential source for transmission of epidemic and nonepidemic clostridium difficile strains among long-term care facility residents. Clin Infect Dis 2007, 45:992-998.
• [5]Hensgens MPM, Keessen EC, Squire MM, Riley TV, Koene MGJ, de Boer E, Lipman LJ A, Kuijper EJ: Clostridium difficile infection in the community: a zoonotic disease? Clin Microbiol Infect 2012, 67:1-11.
• [6]Khanna S, Pardi DS, Aronson SL, Kammer PP, Orenstein R, Sauver JLS, Harmsen WS, Zinsmeister AR: The Epidemiology of Community-Acquired Clostridium diffi cile Infection: a Population-Based Study. Am J Gastroenterol 2011, 107:89-95.
• [7]Braga TM, Pomba C, Lopes MFS: High-level vancomycin resistant Enterococcus faecium related to humans and pigs found in dust from pig breeding facilities. Vet Microbiol 2013, 161:344-349.
• [8]Health Protection Agency: English National Point Prevalence Survey on Healthcare-associated Infections and Antimicrobial Use, 2011 - Preliminary Data. London: Health Protection Agency; 2011:1-140.
• [9]Jit M, Brisson M: Modelling the Epidemiology of Infectious Diseases for Decision Analysis A Primer.. 2011, 29:371-386.
• [10]Grundmann H, Hellriegel B: Mathematical modelling: a tool for hospital infection control. Lancet Infect Dis 2006, 6:39-45.
• [11]Ancel Meyers L, Newman MEJ, Martin M, Schrag S: Applying network theory to epidemics: control measures for Mycoplasma pneumoniae outbreaks. Emerg Infect Dis 2003, 9:204-10.
• [12]McBryde E, McElwain DLS: A mathematic model investigating the impact of an environmental reservoir on the prevalence and control of vancomycin-resistant enterococci. J Infect Dis 2006, 193:1473-1474.
• [13]Massad E, Lundberg S, Yang HM: Modeling and simulating the evolution of resistance against antibiotics. Int J Biomed Comput 1993, 33:65-81.
• [14]Robotham JV, Graves N, Cookson BD, Barnett AG, Wilson JA, Edgeworth JD, Batra R, Cuthbertson BH, Cooper BS: Screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus in intensive care units: cost effectiveness evaluation. BMJ 2011., 343
• [15]Wang J, Wang L, Magal P, Wang Y, Zhuo J, Lu X, Ruan S: Modelling the transmission dynamics of meticillin-resistant Staphylococcus aureus in Beijing Tongren hospital. J Hosp Infect 2011, 79:302-308.
• [16]Sebille V, Chevret S, Valleron A: Modeling the spread of resistant nosocomial pathogens in an intensive-care unit. Infect Control Hosp Epidemiol 1997, 18:84-92.
• [17]Milazzo L, Bown JL, Eberst A, Phillips G, Crawford JW: Modelling of Healthcare Associated Infections: a study on the dynamics of pathogen transmission by using an individual-based approach. Comput Methods Programs Biomed 2011, 104:260-265.
• [18]Austin DJ, Anderson RM: Transmission dynamics of epidemic methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci in England and Wales. J Infect Dis 1999, 179:883-891.
• [19]Lesosky M, McGeer A, Simor A, Green K, Low DE, Raboud J: Effect of patterns of transferring patients among healthcare institutions on rates of nosocomial methicillin-resistant Staphylococcus aureus transmission: a Monte Carlo simulation. Infect Control Hosp Epidemiol 2011, 32:136-147.
• [20]Lee BY, McGlone SM, Wong KF, Yilmaz SL, Avery TR, Song Y, Christie R, Eubank S, Brown ST, Epstein JM, Parker JI, Burke DS, Platt R, Huang SS: Modeling the spread of methicillin-resistant staphylococcus aureus (mrsa) outbreaks throughout the hospitals in orange county, California. Infect Control Hosp Epidemiol 2011, 32:562-572.
• [21]Kardas-Sloma L, Boelle PY, Opatowski L, Brun-Buisson C, Guillemot D, Temime L: Impact of antibiotic exposure patterns on selection of community-associated methicillin-resistant Staphylococcus aureus in hospital settings. Antimicrob Agents Chemother 2011, 55:4888-4895.
• [22]Christopher S, Verghis RM, Antonisamy B, Sowmyanarayanan TV, Brahmadathan KN, Kang G, Cooper BS: Transmission dynamics of methicillin-resistant Staphylococcus aureus in a medical intensive care unit in India. PLoS ONE [Electronic Resource] 2011., 6
• [23]Hubben G, Bootsma M, Luteijn M, Glynn D, Bishai D, Bonten M, Postma M: Modelling the costs and effects of selective and universal hospital admission screening for methicillin-resistant Staphylococcus aureus. PLoS ONE [Electronic Resource] 2011, 6:e14783.
• [24]Bootsma MCJ, Wassenberg MWM, Trapman P, Bonten MJM: The nosocomial transmission rate of animal-associated ST398 meticillin-resistant Staphylococcus aureus. J R Soc Interface 2011, 8:578-584.
• [25]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.
• [26]Meng Y, Davies R, Hardy K, Hawkey P: An application of agent-based simulation to the management of hospital-acquired infection. Journal of Simulation 2010, 4:60-67.
• [27]Pressley J, D’Agata EMC, Webb GF: The effect of co-colonization with community-acquired and hospital-acquired methicillin-resistant Staphylococcus aureus strains on competitive exclusion. J Theor Biol 2010, 264:645-656.
• [28]Temime L, Opatowski L, Pannet Y, Brun-Buisson C, Boelle PY, Guillemot D: Peripatetic health-care workers as potential superspreaders. Proc Natl Acad Sci USA 2009, 106:18420-18425.
• [29]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.
• [30]Skov RL, Jensen KS: Community-associated meticillin-resistant Staphylococcus aureus as a cause of hospital-acquired infections. J Hosp Infect 2009, 73:364-370.
• [31]Cooper B, Lipsitch M: The analysis of hospital infection data using hidden Markov models. Biostatistics 2004, 5:223-237.
• [32]Kypraios T, O’Neill PD, Huang SS, Rifas-Shiman SL, Cooper BS: Assessing the role of undetected colonization and isolation precautions in reducing Methicillin-Resistant Staphylococcus aureus transmission in intensive care units. BMC Infect Dis 2009., 10
• [33]Webb GF, Horn MA, D’Agata EMCD, Moellering RC, Ruan S: Competition of hospital-acquired and community-aqcuired methicillin-resistant Staphylococcus aureus strains in hospitals. J Biol Dyn 2010, 1:115-129.
• [34]D’Agata EMC, Webb GF, Horn MA, Moellering RC Jr, Ruan S: Modeling the invasion of community-acquired methicillin-resistant Staphylococcus aureus into hospitals. Clin Infect Dis 2009, 48:274-284.
• [35]Cooper BS, Medley GF, Stone SP, Kibbler CC, Cookson BD, Roberts JA, Duckworth G, Lai R, Ebrahim S: Methicillin-resistant Staphylococcus aureus in hospitals and the community: stealth dynamics and control catastrophes. Proc Natl Acad Sci USA 2004, 101:10223-10228.
• [36]Drovandi CC, Pettitt AN: Multivariate Markov process models for the transmission of methicillin-resistant Staphylococcus aureus in a hospital ward. Biometrics 2008, 64:851-859.
• [37]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. BioMed Central Full Text
• [38]Robotham JV, Scarff CA, Jenkins DR, Medley GF: Meticillin-resistant Staphylococcus aureus (MRSA) in hospitals and the community: model predictions based on the UK situation. J Hosp Infect 2007, 65(2):93-99.
• [39]Robotham JV, Jenkins DR, Medley GF: Screening strategies in surveillance and control of methicillin-resistant Staphylococcus aureus (MRSA). Epidemiol Infect 2007, 135:328-342.
• [40]McBryde ES, Pettitt AN, McElwain DLS: 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.
• [41]Forrester ML, Pettitt AN, Gibson GJ: Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data. Biostatistics 2007, 8:383-401.
• [42]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.
• [43]Bootsma MCJ, Diekmann O, Bonten MJM: Controlling methicillin-resistant Staphylococcus aureus: quantifying the effects of interventions and rapid diagnostic testing. Proc Natl Acad Sci USA 2006, 103:5620-5625.
• [44]Raboud J, Saskin R, Simor A, Loeb M, Green K, Low DE, McGeer A: Modeling transmission of methicillin-resistant Staphylococcus aureus among patients admitted to a hospital. Infect Control Hosp Epidemiol 2005, 26:607-615.
• [45]Barnes S, Golden B, Wasil E: MRSA transmission reduction using agent-based modeling and simulation. INFORMS J Comput 2010, 22:635-646.
• [46]D’Agata EMC, Webb GF, Pressley J: Rapid emergence of co-colonization with community-acquired and hospital-acquired methicillin-resistant Staphylococcus aureus strains in the hospital setting. Mathematical Modelling of Natural Phenomena 2010, 5:76-93.
• [47]Wolkewitz M, Dettenkofer M, Bertz H, Schumacher M, Huebner J: Environmental contamination as an important route for the transmission of the hospital pathogen VRE: modeling and prediction of classical interventions. Infectious Diseases: Research and Treatment 2008, 1:3-11.
• [48]Cooper BS, Medley GF, Bradley SJ, Scott GM: An augmented data method for the analysis of nosocomial infection data. Am J Epidemiol 2008, 168:548-557.
• [49]Austin DJ, Bonten MJM: Vancomycin-resistant enterococci in intensive care hospital settings. Memorias do Instituto Oswaldo Cruz 1998, 93:587-588.
• [50]McBryde ES, Pettitt AN, Cooper BS, McElwain DLS: Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models. J R Soc Interface 2007, 4:745-754.
• [51]D’Agata EMC, Webb G, Horn M: A mathematical model quantifying the impact of antibiotic exposure and other interventions on the endemic prevalence of vancomycin-resistant enterococci. J Infect Dis 2005, 192:2004-2011.
• [52]Perencevich EN, Fisman DN, Lipsitch M, Harris AD, Morris JG Jr, Smith DL: Projected benefits of active surveillance for vancomycin-resistant enterococci in intensive care units. Clin Infect Dis 2004, 38:1108-1115.
• [53]Pelupessy I, Bonten MJM, Diekmann O: How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proc Natl Acad Sci USA 2002, 99:5601-5605.
• [54]D’Agata EMC, Horn MA, Webb GF: The impact of persistent gastrointestinal colonization on the transmission dynamics of vancomycin-resistant enterococci. J Infect Dis 2002, 185:766-773.
• [55]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 USA 1999, 96:6908-6913.
• [56]Ortiz A, Banks HT, Castillo-Chavez C, Chowell G, Wang X: A discrete events delay differential system model for transmission of Vancomycin-resistant enterococcus (VRE) in hospitals. Journal of Inverse and Ill-Posed Problems 2010, 18:787-821.
• [57]Ortiz AR, Banks HT, Castillo-Chavez C, Chowell G, Wang X: A deterministic methodology for estimation of parameters in dynamic markov chain models. J Biol Syst 2011, 19:71-100.
• [58]Starr JM, Campbell A: Mathematical modeling of Clostridium difficile infection. Clin Microbiol Infect 2001, 7:432-437.
• [59]Starr JM, Campbell A, Renshaw E, Poxton IR, Gibson GJ: Spatio-temporal stochastic modelling of Clostridium difficile. J Hosp Infect 2009, 71:49-56.
• [60]Lanzas C, Dubberke ER, Lu Z, Reske KA, Grohn YT: Epidemiological model for Clostridium difficile transmission in healthcare settings. Infect Control Hosp Epidemiol 2011, 32:553-561.
• [61]Otto S, Day T: A Biologist’s Guide to Mathematical Modelling in Ecology and Evoluation. 1st edition. Oxfordshire: Princeton University Press; 2007:76.
• [62]Vanni T, Karnon J, Madan J, White RG, Edmunds WJ, Foss AM, Legood R: Calibrating Models in Economic Evaluation. PharmacoEconomics 2011, 29:35-49.
• [63]Jackson BR, Thomas A, Carroll KC, Adler FR, Samore MH: Use of strain typing data to estimate bacterial transmission rates in healthcare settings. Infect Control Hosp Epidemiol 2005, 26:638-645.
• [64]Leman SC, Levy F, Walker ES: Modeling the spread of infectious disease using genetic information within a marked branching process. Stat Med 2009, 28:3626-3642.
• [65]Bootsma MCJ, Bonten MJM, Nijssen S, Fluit AC, Diekmann O: An algorithm to estimate the importance of bacterial acquisition routes in hospital settings. Am J Epidemiol 2007, 166:841-51.
• [66]Kwok KO, Leung GM, Lam WY, Riley S: Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong. Proc R Soc Lond B Biol Sci 2007, 274:611-617.
• [67]Kouyos RD, Abel Zur Wiesch P, Bonhoeffer S: On being the right size: the impact of population size and stochastic effects on the evolution of drug resistance in hospitals and the community. PLoS Pathog 2011, 7:e1001334.
• [68]Webb GF, D’Agata EMC, Magal P, Ruan S: A model of antibiotic-resistant bacterial epidemics in hospitals. Proc Natl Acad Sci USA 2005, 102:13343-13348.
• [69]Lee BY, McGlone SM, Bailey RR, Wettstein ZS, Umscheid CA, Muder RR: Economic impact of outbreaks of norovirus infection in hospitals. Infect Control Hosp Epidemiol 2011, 32:191-193.
• [70]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 USA 2004, 101:3709-3714.
• [71]Donker T, Wallinga J, Grundmann H: Patient referral patterns and the spread of hospital-acquired infections through national health care networks. PLoS Comput Biol 2010, 6:e1000715.
• [72]Greer AL, Fisman DN: Use of models to identify cost-effective interventions: Pertussis vaccination for pediatric health care workers. Pediatrics 2011, 128:e591-e599.
• [73]Smith DL, Levin SA, Laxminarayan R: Strategic interactions in multi-institutional epidemics of antibiotic resistance. Proc Natl Acad Sci USA 2005, 102:3153-3158.
• [74]Nishiura H, Kuratsuji T, Quy T, Phi NC, Van Ban V, Ha LD, Long HT, Yanai H, Keicho N, Kirikae T, Sasazuki T, Anderson RM: Rapid awareness and transmission of severe acute respiratory syndrome in Hanoi French Hospital, Vietnam. AmJTrop Med Hyg 2005, 73:17-25.
• [75]Basu S, Andrews JR, Poolman EM, Gandhi NR, Shah NS, Moll A, Moodley P, Galvani AP, Friedland GH: Prevention of nosocomial transmission of extensively drug-resistant tuberculosis in rural South African district hospitals: an epidemiological modelling study. Lancet 2007, 370:1500-1507.
• [76]Cori A, Boelle PY, Thomas G, Leung GM, Valleron AJ: Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong. PLoS Comput Biol 2009, 5:e1000471.
• [77]Ueno T, Masuda N: Controlling nosocomial infection based on structure of hospital social networks. J Theor Biol 2008, 254:655-666.
• [78]Bergstrom CT, Lo M, Lipsitch M: Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. Proc Natl Acad Sci USA 2004, 101:13285-13290.
• [79]Boldin B, Bonten MJM, Diekmann O: Relative effects of barrier precautions and topical antibiotics on nosocomial bacterial transmission: results of multi-compartment models. Bull Math Biol 2007, 69:2227-2248.
• [80]Kouyos RD, Abel Zur Wiesch P, Bonhoeffer S: Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards. PLoS Comput Biol 2011, 7:e1001094.
• [81]Kribs-Zaleta CM, Jusot JF, Vanhems P, Charles S: Modeling Nosocomial Transmission of Rotavirus in Pediatric Wards. Bull Math Biol 2011, 73:1413-1442.
• [82]Sebille V, Valleron AJ: A computer simulation model for the spread of nosocomial infections caused by multidrug-resistant pathogens. Comput Biomed Res 1997, 30:307-322.
• [83]Haber MJ, Levin BR, Kramarz P: Antibiotic control of antibiotic resistance in hospitals: a simulation study. BMC Infect Dis 2010., 1025 August 2010
• [84]Friedman A, Ziyadi N, Boushaba K: A model of drug resistance with infection by health care workers. Mathematical Biosciences & Engineering: MBE 2010, 7:779-792.
• [85]Lipsitch M, Bergstrom CT, Levin BR: The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proc Natl Acad Sci USA 2000, 97:1938-1943.
• [86]D’Agata EMC, 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.
• [87]Reluga TC: Simple models of antibiotic cycling. Math Med Biol 2005, 22:187-208.
• [88]Chowa K, Wanga X, Curtiss R, Castillo-Chavez C: Evaluating the efficacy of antimicrobial cycling programmes and patient isolation on dual resistance in hospitals. J Biol Dyn 2011, 5:27-43.
• [89]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.
• [90]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.
• [91]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, 33:164-168.
• [92]Hotchkiss JR, Holley P, Crooke PS: Analyzing pathogen transmission in the dialysis unit: time for a (schedule) change? Clinical Journal of The American Society of Nephrology: CJASN 2007, 2:1176-1185.
• [93]Massad E, Burattini MN, Coutinho FAB: An optimization model for antibiotic use. Appl Math Comput 2008, 201:161-167.
• [94]Bakhir VM, Grishin VP, Panicheva SA, Toloknov VI: Assessment of the effectiveness of medical instruments sterilization by electrochemically activated solutions and computer modeling of the dynamics of hospital infections. [Russian] Otsenka effektivnosti sterilizatsii meditsinskogo instrumentariia elektro. Meditsinskaia Tekhnika 1999, 14-16.
• [95]van den Dool C, Bonten MJM, Hak E, Wallinga J: Modeling the effects of influenza vaccination of health care workers in hospital departments. Vaccine 2009, 27:6261-6267.
• [96]van den Dool C, Bonten MJM, Hak E, Heijne JCM, Wallinga J: The effects of influenza vaccination of health care workers in nursing homes: insights from a mathematical model. PLoS Medicine / Public Library of Science 2008, 5:e200.
• [97]Polgreen PM, Tassier TL, Pemmaraju SV, Segre AM: Prioritizing healthcare worker vaccinations on the basis of social network analysis. Infect Control Hosp Epidemiol 2010, 31:893-900.
• [98]van den Dool C, Hak E, Bonten MJM, Wallinga J: A model-based assessment of oseltamivir prophylaxis strategies to prevent influenza in nursing homes. Emerg Infect Dis 2009, 15:1547-1555.
• [99]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 transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 2011, 15:877-89.
• [100]Nuno M, Reichert TA, Chowell G, Gumel AB: Protecting residential care facilities from pandemic influenza. Proc Natl Acad Sci USA 2008, 105:10625-10630.
• [101]Greer AL, Fisman DN: Keeping vulnerable children safe from pertussis: preventing nosocomial pertussis transmission in the neonatal intensive care unit. Infect Control Hosp Epidemiol 2009, 30:1084-1089.
• [102]Fukutome A, Watashi K, Kawakami N, Ishikawa H: Mathematical modeling of severe acute respiratory syndrome nosocomial transmission in Japan: the dynamics of incident cases and prevalent cases. Microbiol Immunol 2007, 51:823-832.
• [103]O’Neill PD: Introduction and snapshot review: relating infectious disease transmission models to data. Stat Med 2010, 29:2069-77.
• [104]Vanderpas J, Louis J, Reynders M, Mascart G, Vandenberg O: Mathematical model for the control of nosocomial norovirus. J Hosp Infect 2009, 71:214-222.
• [105]Bilcke J, Beutels P, Brisson M, Jit M: Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide. Medical decision making : an international journal of the Society for Medical Decision Making 2011, 31:675-92.
• [106]Oakley JE, Hagan AO: Probabilistic sensitivity analysis of complex models: a Bayesian approach. 2004, 751-769.
• [107]Artalejo JR, Economou A, Lopez-Herrero MJ: On the number of recovered individuals in the SIS and SIR stochastic epidemic models. Math Biosci 2010, 228:45-55.
• [108]Beardmore RE, Pena-Miller R: Rotating antibiotics selects optimally against antibiotic resistance, in theory. Mathematical Biosciences & Engineering: MBE 2010, 7:527-552.
• [109]Noakes CJ, Beggs CB, Sleigh PA, Kerr KG: Modelling the transmission of airborne infections in enclosed spaces. Epidemiol Infect 2006, 134:1082-1091.
• [110]Köck R, Becker K, Cookson B, van Gemert-Pijnen JE, Harbarth S, Kluytmans J, Mielke M, Peters G, Skov RL, Struelens MJ, Tacconelli E, Navarro Torné A, Witte W, Friedrich AW: Methicillin-resistant Staphylococcus aureus (MRSA): burden of disease and control challenges in Europe. Euro surveillance : bulletin européen sur les maladies transmissibles = European communicable disease bulletin 2010, 15:19688.
• [111]MRSA surveillance. http://www.cdc.gov/mrsa/statistics/mrsa-surveillance-summary.html webcite
• [112]Surveillance for Methicillin-resistant Staphylococcus aureus (MRSA) in Patients Hospitalized in Canadian Acute-Care Hospitals Participating in CNISP 2006–2007 Preliminary Results. http://www.phac-aspc.gc.ca/nois-sinp/reports-rapport/mrsa-sarm_result-eng.php webcite
• [113]Dubberke ER, Olsen M a: Burden of Clostridium difficile on the Healthcare System. Clin Infect Dis 2012, 55(suppl 2):S88-S92.
• [114]Bauer MP, Notermans DW, van Benthem BHB, Brazier JS, Wilcox MH, Rupnik M, Monnet DL, van Dissel JT, Kuijper EJ: Clostridium difficile infection in Europe: a hospital-based survey. Lancet 2011, 377:63-73.
• [115]de Kraker ME A, Davey PG, Grundmann H: Mortality and hospital stay associated with resistant Staphylococcus aureus and Escherichia coli bacteremia: estimating the burden of antibiotic resistance in Europe. PLoS Med 2011, 8:e1001104.
• [116]World Health Organization: Report on the Burden of Endemic Health Care-Associated Infection Worldwide - Clean Care Is Safer Care. Geneva; 2011.
• [117]Allegranzi B, Bagheri Nejad S, Combescure C, Graafmans W, Attar H, Donaldson L, Pittet D: Burden of endemic health-care-associated infection in developing countries: systematic review and meta-analysis. Lancet 2011, 377:228-41.
• [118]Deleo FR, Otto M, Kreiswirth BN, Chambers HF: Community-associated meticillin-resistant Staphylococcus aureus. Lancet 2010, 375:1557-68.
• [119]Ricciardi R, Nelson J, Griffith JL, Concannon TW: Do admissions and discharges to long-term care facilities influence hospital burden of Clostridium difficile infection? J Hosp Infect 2012, 80:156-61.
• [120]Opatowski L, Guillemot D, Boëlle P-Y, Temime L: Contribution of mathematical modeling to the fight against bacterial antibiotic resistance. Curr Opin Infect Dis 2011, 24:279-87.
• [121]Temime L, Hejblum G, Setbon M, Valleron A: The rising impact of mathematical modelling in epidemiology : antibiotic resistance research as a case study. Epidemiol Infect 2008, 136:289-298.
• [122]Hornbeck T, Naylor D, Segre AM, Thomas G, Herman T, Polgreen PM: Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections. J Infect Dis 2012, 206:1549-57.
• [123]Ypma RJF, Bataille AMA, Stegeman A, Koch G, Wallinga J, van Ballegooijen WM: Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data. Proceedings Biological sciences/The Royal Society 2012, 279:444-5.
• [124]Morelli MJ, Thébaud G, Chadœuf J, King DP, Haydon DT, Soubeyrand S: A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data. PLoS Comput Biol 2012, 8:e1002768.