Contact Network Epidemiology of Influenza and Other Nosocomial Infections

描述（由申请人提供）：根据CDC，医疗相关感染每年影响美国医院约200万患者。流感和MRSA等感染通常会通过医护人员传播给住院患者。为了更好地了解这些传染病是如何传播的，必须考虑所研究的人口要素之间的流动和相互作用模式。标准流行病学模型假设人口完全混合（例如，每个人都有可能遇到受感染的个体），或者可以通过根据外部信息将人口细分为少量隔间来充分模拟非均匀混合（例如，年龄或类型）。这些假设在小环境中可能不成立，在小环境中，单个个体行为的差异可能对疾病扩散产生强大影响。社会网络理论的最新研究提出了一种不同的方法来研究传染病，使用接触网络来模拟疾病传播。与其使用微分方程来模拟感染在群体中的扩散，不如使用传播概率来确定感染个体是否通过接触将疾病传播给易感个体。虽然其他人已经使用网络模型来研究传染病在城市或区域范围内的传播，但我们是该国唯一一个真正的多学科小组，专注于开发网络模型和技术，专门研究医院获得性感染。本研究的总体目标是开发和使用网络流行病学模型，以设计更有效的策略，预防和控制医疗相关感染的传播。该项目的具体目标是：（1）创建一个基于多个数据源的医院获得性感染的代理级模拟器，并整合关于人与人之间交互的细粒度但去识别的信息;（二）试点完善科技型并购、整合、分析医院移动和互动数据，以跟踪感染并支持我们的疾病模拟工作;以及（3）应用我们的模型来模拟常见的医院获得性感染的扩散，更具体地说，流感和耐甲氧西林金黄色葡萄球菌（MRSA）。该项目将提供一个急需的框架，用于评价和比较医院的病人安全措施和感染控制干预措施，如病人聚集、有针对性的洗手合规措施、工人接种疫苗战略等，并将原型化先进的联系人跟踪技术（例如，传感器微粒），其在将来可以在传染病爆发的情况下主动部署（例如，禽流感、SARS）。简而言之，这里倡导的方法和技术将大大提高我们对医疗保健相关感染传播的理解，并将直接转化为改善患者和医护人员的安全。公共卫生相关性：疫苗接种和手部卫生通常被认为是预防医院获得性感染传播的最有效措施。然而，我们既没有一个理论框架，也没有经验数据，以确定最有可能获得和传播传染性病原体的工人，因此谁应该在流感疫苗或手部卫生依从性运动的最高优先级。在这项提案中，新的方法和先进的技术被应用到模型工人/病人的运动和互动，提供这样一个框架;通过使用真实的医护人员的运动数据，沿着真实的医疗中心的建筑数据，我们正在建立一个新的标准，医疗相关的疾病传播研究。

DESCRIPTION (provided by applicant): According to the CDC, healthcare-associated infections affect about two million patients in American hospitals each year. Infections like influenza and MRSA routinely spread to and among hospitalized patients, often via healthcare workers. To better understand how these infections spread, one must consider patterns of movement and interaction between elements of the population under study. Standard epidemiological models assume either that populations mix perfectly (e.g. that everyone is equally likely to encounter an infected individual), or that nonhomogenous mixing can be adequately modeled by subdividing the population into a small number of compartments according to external information (e.g., by age or type). These assumptions may not hold in small settings, where differences in a single individual's behavior can have a powerful impact on disease diffusion. Recent research in social network theory suggests a different approach to the study of infectious diseases that uses contact networks to model disease transmission. Rather than using differential equations to model diffusion of infection through a population as a group, transmission probabilities are used to determine whether or not an infected individual passes the disease on to a susceptible individual on contact. Although others have used network models to study the spread of infectious disease at an urban or regional scale, we are the only truly multidisciplinary group in the country focused on developing both network models and technology specifically to study hospital-acquired infections. The overarching goal of this research is to develop and use network epidemiology models in order to design more effective strategies for preventing and controlling the spread of healthcare-associated infections. The specific aims of this project are: (1) to create an agent-levels simulator for hospital-acquired infections based on multiple data sources and incorporating fine-grained, yet de-identified, information about person-to-person interactions; (2) to pilot and refine the technology-based acquisition, integration, and analysis of hospital movement and interaction data for the purpose of infection tracking and to support our disease simulation efforts; and (3) to apply our models to simulate diffusion of commonly-occurring hospital-acquired infections, and, more specifically, influenza and methicillin-resistant Staphylococcus aureus (MRSA). This project will provide a much-needed framework for the evaluation and comparison of hospital patient safety measures and infection control interventions such as patient cohorting, targeted hand washing compliance measures, worker vaccination strategies etc., and will prototype advanced contact-tracking technology (e.g., sensor motes) that may, in the future, be deployed proactively in the event of an infectious disease outbreak (e.g., avian influenza, SARS). In short, the methods and technology advocated here will dramatically improve our understanding of healthcare-associated infection transmission, and will translate directly to improved patient and healthcare worker safety. PUBLIC HEALTH RELEVANCE: Vaccination and hand hygiene are commonly believed to be the most effective measures for preventing the spread of hospital-acquired infections. However, we have neither a theoretical framework nor empirical data to identify workers most likely to acquire and transmit infectious agents and who therefore should have the highest priority in influenza vaccine or hand hygiene adherence campaigns. In this proposal, new methods and advanced technology are applied to model worker/patient movement and interaction to provide just such a framework; by using real healthcare worker movement data, along with real healthcare center architectural data, we are establishing a new standard for healthcare-associated disease transmission studies.