Modeling MRSA in the Community

描述（由申请人提供）：本项目的目标是创建一种新的基于药物的耐甲氧西林金黄色葡萄球菌（MRSA）模型，MRSA是一种耐药性细菌，最常引起皮肤感染，但可引起任何器官的严重和致命感染。在过去的十年中，MRSA呈指数级传播，是一个严重的公共卫生威胁。MRSA的新菌株在社区中迅速发展，与医疗环境中的MRSA相互作用，这首次出现在近50年前。MRSA具有许多独特的特征，这些特征代表了基于代理的建模的挑战和机遇。我们的模型将允许代理人根据疾病状况和风险感知来调整他们的行为。该模型将包括基于理论和经验得出的变量，代表与传染病传播和控制相关的人口和医疗保健系统的行为特征。这些将包括代表与疾病传播相关的社交网络、与信息传递相关的社交网络以及遵循健康建议倾向的个体差异的变量。该项目汇集了具有独特跨学科专业知识的研究人员：流行病学，MRSA，社会科学，贝叶斯统计，基于代理的建模，高性能计算和公共卫生。具体来说，我们将开发一个灵活的基于代理的模型，逐步扩大到芝加哥大都市区的人口。第一阶段将包括一条穿过芝加哥南侧和毗邻郊区的走廊，在该地区，MRSA一直是一个严重的问题，并已在社区和医疗机构进行了广泛的研究。在我们的研究团队在MRSA方面的专业知识的指导下，我们的模型将捕捉环境的特征和MRSA流行病学特别突出的个体特征。我们将开发变量来代表先天个体MRSA风险，特定位置的MRSA传播概率和污染物（MRSA存在的无生命物体）。使用该模型，我们将测试有关MRSA传播因素的假设。我们将确定哪些临床、公共卫生和机构措施可能对这一流行病产生最大影响。拟议工作的一个重要组成部分将是研究模型结果的变化（即，模型不确定性），以及模型参数、网络规格和其他变量的变化对这种变化的影响。为此，我们将使用芝加哥大学和阿贡国家实验室的高性能计算能力，对单个模型配置进行数千次运行。 公共卫生相关性：计算模型已被用于了解不同类型的传染病控制干预措施的有效性。在这个项目中，我们模拟了耐甲氧西林金黄色葡萄球菌（MRSA）的传播和控制，MRSA是一种耐药性细菌，现在每年在美国杀死的人比艾滋病还多。我们的模型将引入代表感染相关行为变化的变量，将有助于确定感染控制和治疗的最佳实践。

DESCRIPTION (provided by applicant): 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.