title: Characterising the Transmission Dynamics of Acinetobacter baumannii in Intensive Care Units Using Hidden Markov Models.
abstract: Little is known about the transmission dynamics of Acinetobacter baumannii in hospitals, despite such information being critical for designing effective infection control measures. In the absence of comprehensive epidemiological data, mathematical modelling is an attractive approach to understanding transmission process. The statistical challenge in estimating transmission parameters from infection data arises from the fact that most patients are colonised asymptomatically and therefore the transmission process is not fully observed. Hidden Markov models (HMMs) can overcome this problem. We developed a continuous-time structured HMM to characterise the transmission dynamics, and to quantify the relative importance of different acquisition sources of A. baumannii in intensive care units (ICUs) in three hospitals in Melbourne, Australia. The hidden states were the total number of patients colonised with A. baumannii (both detected and undetected). The model input was monthly incidence data of the number of detected colonised patients (observations). A Bayesian framework with Markov chain Monte Carlo algorithm was used for parameter estimations. We estimated that 96-98% of acquisition in Hospital 1 and 3 was due to cross-transmission between patients; whereas most colonisation in Hospital 2 was due to other sources (sporadic acquisition). On average, it takes 20 and 31 days for each susceptible individual in Hospital 1 and Hospital 3 to become colonised as a result of cross-transmission, respectively; whereas it takes 17 days to observe one new colonisation from sporadic acquisition in Hospital 2. The basic reproduction ratio (R0) for Hospital 1, 2 and 3 was 1.5, 0.02 and 1.6, respectively. Our study is the first to characterise the transmission dynamics of A. baumannii using mathematical modelling. We showed that HMMs can be applied to sparse hospital infection data to estimate transmission parameters despite unobserved events and imperfect detection of the organism. Our results highlight the need to optimise infection control in ICUs.
authors.affiliation: Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia.
authors.forename: Tan N
authors.initials: TN
authors.lastname: Doan
authors.affiliation: Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia.
authors.forename: David C M
authors.initials: DC
authors.lastname: Kong
authors.affiliation: Victorian Infectious Diseases Service, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Caroline
authors.initials: C
authors.lastname: Marshall
authors.affiliation: Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia.
authors.forename: Carl M J
authors.initials: CM
authors.lastname: Kirkpatrick
authors.affiliation: Victorian Infectious Diseases Service, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Emma S
authors.initials: ES
authors.lastname: McBryde
mesh_terms: D000151:Acinetobacter Infections / Q000453:epidemiology / Q000517:prevention & control / Q000635:transmission*; D040981:Acinetobacter baumannii / Q000302:isolation & purification*; D001499:Bayes Theorem; D002353:Carrier State / Q000453:epidemiology / Q000382:microbiology; D003428:Cross Infection / Q000453:epidemiology / Q000382:microbiology / Q000517:prevention & control / Q000635:transmission*; D004196:Disease Outbreaks / Q000517:prevention & control; D063373:Hand Hygiene; D006742:Hospital Bed Capacity; D006801:Humans; D017053:Infection Control / Q000379:methods; D055552:Infectious Disease Medicine; D007362:Intensive Care Units; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011481:Protective Clothing; D017751:Safety Management; D016635:Universal Precautions; D014739:Victoria / Q000453:epidemiology