title: An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit.
abstract: OBJECTIVE
  To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit.
  
  
  METHODS
  A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined.
  
  
  METHODS
  The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns.
  
  
  METHODS
  Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients.
  
  
  RESULTS
  The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Per-encounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing "crossover" between caregiver domains diminish the likelihood of more widespread dissemination.
  
  
  CONCLUSIONS
  Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit.
authors.affiliation: CRISMA Laboratory, Department of Critical Care, University of Pittsburgh, USA.
authors.forename: John R
authors.initials: JR
authors.lastname: Hotchkiss
authors.forename: David G
authors.initials: DG
authors.lastname: Strike
authors.forename: Dana A
authors.initials: DA
authors.lastname: Simonson
authors.forename: Alain F
authors.initials: AF
authors.lastname: Broccard
authors.forename: Philip S
authors.initials: PS
authors.lastname: Crooke
mesh_terms: D017028:Caregivers / Q000706:statistics & numerical data*; D003428:Cross Infection / Q000517:prevention & control / Q000635:transmission*; D016983:Enterococcus; D016908:Gram-Positive Bacterial Infections / Q000635:transmission*; D006801:Humans; D017758:Infectious Disease Transmission, Patient-to-Professional / Q000706:statistics & numerical data; D017757:Infectious Disease Transmission, Professional-to-Patient / Q000706:statistics & numerical data; D007362:Intensive Care Units; D016013:Likelihood Functions; D008505:Medical Staff, Hospital; D016106:Methicillin Resistance; D008962:Models, Theoretical; D009741:Nursing Staff, Hospital; D010561:Personnel Staffing and Scheduling; D011336:Probability; D012017:Referral and Consultation / Q000706:statistics & numerical data*; D012306:Risk; D013203:Staphylococcal Infections / Q000635:transmission*; D020713:Vancomycin Resistance