The goal of this project is to create a new agent-based model of methicillin resistant Staphylococcus aureus (MRSA), an antibiotic-resistant bacterium that most commonly causes skin infections but can cause serious and fatal infections of any organ. During the past decade, MRSA has spread exponentially and is a serious public health threat. New strains of MRSA have rapidly evolved in the community, interacting with MRSA in the healthcare environment, which first appeared almost 50 years ago. MRSA has a number of distinctive features that represent challenges and opportunities for agent-based modeling. Our model will allow agents to adapt their behavior dependent on disease conditions and perceptions of risk. The model will include theoretically based and empirically derived variables representing behavioral features of the population and the healthcare system that are relevant to infectious disease transmission and control. These will include variables representing social networks relevant for disease transmission, social networks relevant for information transfer and individual variation in propensity to follow health recommendations. This project brings together investigators with unique interdisciplinary expertise: epidemiology, MRSA, social sciences, Bayesian statistics, agent-based modeling, high performance computing, and public health. Specifically, we will develop a flexible agent-based model scaling up in stages to the population of the Chicago metropolitan area. The 1st stage will include a corridor across the south side of Chicago and adjoining suburbs, an area in which MRSA has been a serious problem and has been extensively studied both in community and healthcare settings. Guided by our research team's expertise in MRSA, our model will capture features of the environment and characteristics of the individual that are particularly salient for MRSA epidemiology. We will develop variables to represent innate individual MRSA risk, location-specific MRSA transmission probabilities, and contamination of fomites (inanimate objects on which MRSA is present). Using the model, we will test hypotheses about factors contributing to MRSA spread. We will determine which clinical, public health and institutional measures are likely to have the greatest impact on the epidemic. An important component of the proposed work will be to study variation in model outcomes (i.e., model uncertainty), and the effect of changes in model parameters, network specifications, and other variables on this variation. To do this, we shall use high-performance computing capabilities at the University of Chicago and Argonne National Laboratory to run individual model configurations thousands of times. Public Health Relevance: Computational models have been used to understand the effectiveness of different types of control interventions for infectious diseases. In this project, we model the spread and control of methicillin resistant Staphylococcus aureus (MRSA), an antibiotic-resistant bacterium that now kills more people each year in the U.S. than AIDS. Our models, which will introduce variables representing variation in infection-related behavior, will help identify best practices for infection control and treatment.
Computational models have been used to understand the effectiveness of different types of control interventions for infectious diseases. In this project, we model the spread and control of methicillin resistant Staphylococcus aureus (MRSA), an antibiotic-resistant bacterium that now kills more people each year in the U.S. than AIDS. Our models, which will introduce variables representing variation in infection-related behavior, will help identify best practices for infection control and treatment.
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