Contact Network Epidemiology of Influenza and Other Nosocomial Infections

描述（由申请人提供）：根据美国疾病控制与预防中心的数据，美国医院每年约有200万患者受到医疗保健相关感染的影响。流感和耐甲氧西林金黄色葡萄球菌等传染病通常通过医护人员在住院病人之间传播。为了更好地理解这些感染是如何传播的，人们必须考虑所研究人群中各组成部分之间的运动模式和相互作用。标准流行病学模型要么假设种群完全混合（例如，每个人都有可能遇到受感染的个体），要么假设可以根据外部信息（例如，按年龄或类型）将种群细分为少量区域，从而充分模拟非同质混合。这些假设在小型环境中可能不成立，在小型环境中，单个个体的行为差异可能对疾病扩散产生强大的影响。最近对社会网络理论的研究提出了一种不同的方法来研究传染病，即使用接触网络来模拟疾病传播。传染概率不是使用微分方程来模拟感染在一个群体中的扩散，而是用来确定受感染的个体是否通过接触将疾病传染给易感个体。虽然其他人已经使用网络模型来研究城市或区域范围内传染病的传播，但我们是国内唯一真正专注于开发网络模型和专门研究医院获得性感染的技术的多学科小组。本研究的总体目标是开发和使用网络流行病学模型，以便设计更有效的策略来预防和控制医疗保健相关感染的传播。该项目的具体目标是：(1)基于多个数据源创建医院获得性感染的代理级模拟器，并结合关于人与人之间交互的细粒度但未识别的信息；(2)试点和完善基于技术的医院移动和互动数据的获取、整合和分析，以跟踪感染并支持我们的疾病模拟工作；(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，是流感和耐甲氧西林金黄色葡萄球菌（MRSA）。该项目将为评估和比较医院患者安全措施和感染控制干预措施（如患者队列、有针对性的洗手合规措施、工人接种疫苗战略等）提供急需的框架，并将为先进的接触追踪技术（如传感器记录仪）提供原型，这些技术将来可能在发生传染病暴发（如禽流感、SARS）时主动部署。简而言之，本文倡导的方法和技术将极大地提高我们对卫生保健相关感染传播的理解，并将直接转化为改善患者和卫生保健工作者的安全。公共卫生相关性：疫苗接种和手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，我们既没有理论框架，也没有经验数据来确定最有可能获得和传播传染因子的工人，因此，谁应该在流感疫苗或坚持手卫生运动中享有最高优先权。在这个提议中，新方法和先进技术被应用于模范工作者/病人的运动和互动，以提供这样一个框架；通过使用真实的医护人员移动数据，以及真实的医疗中心架构数据，我们正在为医疗相关疾病传播研究建立一个新的标准。

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.