original article A Model-Based Strategy to Control the Spread of Carbapenem- Resistant Enterobacteriaceae: Simulate and Implement Mirian de Freitas DalBen, MD; 1,2 Elisa Teixeira Mendes, MD; 1,2 Maria Luisa Moura, MD; 1 Dania Abdel Rahman, MD; 1 Driele Peixoto, MD; 1 Sania Alves dos Santos, PhD; 1,2 Walquiria Barcelos de Figueiredo, RN; 3 Pedro Vitale Mendes, MD; 4 Leandro Utino Taniguchi, MD, PhD; 4 Francisco Antonio Bezerra Coutinho, DPhil; 5 Eduardo Massad, MD; 5 Anna Sara Levin, MD, PhD 1,2 objective. To reduce transmission of carbapenem-resistant Enterobacteriaceae (CRE) in an intensive care unit with interventions based on simulations by a developed mathematical model. design. Before-after trial with a 44-week baseline period and 24-week intervention period. setting. Medical intensive care unit of a tertiary care teaching hospital. participants. All patients admitted to the unit. methods. We developed a model of transmission of CRE in an intensive care unit and measured all necessary parameters for the model input. Goals of compliance with hand hygiene and with isolation precautions were established on the basis of the simulations and an intervention was focused on reaching those metrics as goals. Weekly auditing and giving feedback were conducted. results. The goals for compliance with hand hygiene and contact precautions were reached on the third week of the intervention period. During the baseline period, the calculated R0 was 11; the median prevalence of patients colonized by CRE in the unit was 33%, and 3 times it exceeded 50%. In the intervention period, the median prevalence of colonized CRE patients went to 21%, with a median weekly Rn of 0.42 (range, 0–2.1). conclusions. The simulations helped establish and achieve specific goals to control the high prevalence rates of CRE and reduce CRE transmission within the unit. The model was able to predict the observed outcomes. To our knowledge, this is the first study in infection control to measure most variables of a model in real life and to apply the model as a decision support tool for intervention. Infect Control Hosp Epidemiol 2016;37:1315 – 1322 Resistance of Enterobacteriaceae to carbapenems is an impor- tant topic owing to the increasing frequency of infections caused by these agents, the difficult treatment, the high mortality, and the potential for transmission of resistance via mobile genetic elements. 1 The resistance emerged 2 decades ago and became a major public health threat in many countries. 2–4 Mathematical models to study the dynamics of pathogen transmission are used in community-acquired infections. In the hospital, studies attempted to use mathematical models to understand the transmission of multidrug-resistant micro- organisms and to estimate the impact of interventions, but most are restricted to theoretical studies without direct application. 5–7 Furthermore, these models exclude patients under isolation pre- cautions from the transmission chain, which may be incorrect. We developed a mathematical model to describe the trans- mission of carbapenem-resistant Enterobacteriaceae (CRE) in an intensive care unit (ICU), including isolated patients as possible sources of infection, in order to establish goals of compliance with hand hygiene (HH) and with contact pre- cautions (CP). The effect of the model-based intervention was evaluated, aimed at reducing nosocomial acquisition of CRE in the unit. methods Our study was conducted prospectively in a 14-bed ICU of a tertiary care teaching hospital. It consisted of a 44-week baseline period and a 24-week intervention period. Affiliations: 1. Infection Control Department and LIM54, Hospital das Clínicas, University of São Paulo, São Paulo, Brazil; 2. Department of Infectious Diseases and Institute of Tropical Medicine, University of São Paulo, São Paulo, Brazil; 3. Nursing Division, Faculty of Medicine, Hospital das Clinicas, University of São Paulo, São Paulo, Brazil; 4. Intensive Care Unit, Emergency Medicine Discipline, Hospital das Clínicas, University of São Paulo, São Paulo, Brazil; 5. Discipline of Medical Informatics, School of Medicine, University of São Paulo, São Paulo, Brazil. © 2016 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2016/3711-0007. DOI: 10.1017/ice.2016.168 Received February 12, 2016; accepted June 18, 2016; electronically published September 9, 2016 infection control & hospital epidemiology november 2016, vol. 37, no. 11