Transmission dynamics of methicillin-resistant Staphylococcus aureus in a medical intensive care unit

Abstract

Intensive care units (ICUs) play an important role in the epidemiology of methicillin-resistant Staphyloccocus aureus (MRSA). Although successful interventions are multi-modal, the relative efficacy of single measures remains unknown. We developed a discrete time, individual-based, stochastic mathematical model calibrated on cross-transmission observed through prospective surveillance to explore the transmission dynamics of MRSA in a medical ICU. Most input parameters were derived from locally acquired data. After fitting the model to the 46 observed cross-transmission events and performing sensitivity analysis, several screening and isolation policies were evaluated by simulating the number of cross-transmissions and isolation-days. The number of all cross-transmission events increased from 54 to 72 if only patients with a past history of MRSA colonization are screened and isolated at admission, to 75 if isolation is put in place only after the results of the admission screening become available, to 82 in the absence of admission screening and with a similar reactive isolation policy, and to 95 when no isolation policy is in place. The method used (culture or polymerase chain reaction) for admission screening had no impact on the number of cross-transmissions. Systematic regular screening during ICU stay provides no added-value, but aggressive admission screening and isolation effectively reduce the number of cross-transmissions. Critically, colonized healthcare workers may play an important role in MRSA transmission and their screening should be reinforced.

Dynamic contact networks of patients and MRSA spread in hospitals

“…Agent-based models, on the other hand, request more computational resources but are more flexible and allow the addition of different levels of complexity by defining updating and interaction rules for each individual [26][27][28] . Previous models typically focused either on the population dynamics within a single ward 29 or in a single hospital with a few wards 23,30 , between one hospital and the community 31 , or between multiple hospitals with a simple ward structure 23,27 . As in any modeling exercise, these studies make a series of assumptions regarding different aspects of the population.…”

Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data

“…Inclusion of data on social networks allowed simulating more innovative and realistic infection prevention and control strategies, including heterogeneous hand-hygiene compliance or cohorting levels [16,19,22,27,31,32,34,35]. Handhygiene compliance was the most common intervention studied [20,22,27,29,31,34,35,37,39,40 [26,46,57], and HCW vaccination [17][18][19]. Other models explored the role of patient-HCW interactions through variations in cohorting by modifying patient: HCW ratios [16,31,34], social interactions in hospitals [24,28,36]…”

A dynamic model for infectious diseases: The role of vaccination and treatment

“…2. If conditions l 2 < 2(l 1 + m)), (l 1 + m) 2 > 2l(n + m 1 ) and (n + m 1 ) 2 < n 1 2 (17) hold, (13) yields two values for ν 2 > 0 and there will be four real values for ν. Again from (16) we get two values each for T say T 1 and T 2 and δ say δ 1 and δ 2 .…”

“…Mathematical modeling is a tool that has been popularly employed in prevention and control of infectious diseases such as severe acute respiratory syndrome (SARS) [1], human immunodeficiency virus infection/acquired immune deficiency syndrome (HIV/AIDS) [4], H5N1 (avian flu) [37] and H1N1 (swine flu) [35]. Also, they have been useful in studying some of the drug resistant strains of malaria [12], tuberculosis [5], methicillinresistant staphylococcus aureus (MRSA) [17] and marine bacteriophage infection [3]. Simple deterministic models are based on dividing the population into compartments such as susceptible, exposed, infected and recovered.…”