Social Networks and the Spread of Influenza and other Nosocomial Infections

描述（由申请人提供）：在这个K01申请中，Philip Polgreen医学博士寻求获得与社会网络相关的图论和数学模拟方面的专业知识，以开发更有效的干预措施，以最大限度地减少医院感染的传播（特别是流感，MRSA和艰难梭菌）。Polgreen博士召集了一群非常强大的导师和跨学科的合作者，他们都高度致力于他的成功。医疗保健相关感染每年影响美国医院约200万患者。此外，医院是传染性病原体传播的放大器：感染或定植传染性病原体的患者往往与免疫系统受损的未感染患者近距离接触。例如，SARS在社区传播不多，但在医院传播广泛。耐甲氧西林金黄色葡萄球菌和艰难梭菌历史上首先在医疗机构传播，后来在社区传播。此外，许多传染病专家担心，H5N1流感或耐万古霉素金黄色葡萄球菌菌株的传播可能会在医院里被放大。接种疫苗和保持手部卫生是预防医院获得性感染传播的最有效措施。然而，没有数据或理论框架来确定最有可能获得和传播传染原的卫生保健工作者（即，那些应该在流感疫苗运动中得到最高优先重视或应该成为加强坚持手部卫生计划的重点的人）。此外，以前用于研究医院感染传播的数学模型是基于这样一个假设，即医护人员和患者的混合是均匀的。这些模型忽略了社会网络（聚类和小世界属性），这些网络定义了医护人员和患者之间互动的真实本质，因此可能产生误导性的结果。Polgreen博士的假设是，一些医疗工作者群体比其他群体更有可能传播医院内的病原体。为了验证这一假设，他将收集爱荷华大学医院和诊所医护人员与患者之间接触的信息[通过直接观察、键盘登录数据、射频识别（RFID）数据和传感器数据]。这些信息将使他能够开发图表（社会网络）模型，描述通过呼吸途径（3英尺内接触）传播的病原体和通过直接接触传播的病原体的传播。然后，他将通过数学模拟来估计不同卫生保健工作者群体中不同的疫苗接种和手部卫生策略如何影响院内病原体的传播。这项工作将导致更有效的感染控制干预措施和战略。

DESCRIPTION (provided by applicant): In this K01 application, Philip Polgreen, MD, seeks to gain expertise in graph theory pertaining to social networks and in mathematical simulations for developing more effective interventions to minimize the spread of nosocomial infections (specifically influenza, MRSA, and C. difficile). Dr. Polgreen has assembled a group of extremely strong mentors and interdisciplinary collaborators who are all highly committed to his success. Healthcare associated infections affect about 2 million patients in U.S. hospitals each year. Furthermore, hospitals serve as amplifiers for the spread of infectious pathogens: patients who are infected or colonized with transmissible pathogens are often in close proximity to uninfected patients with compromised immune systems. For example, SARS did not spread much in the community but spread widely in hospitals. MRSA and C. difficile historically spread first in healthcare facilities and later in the community. Also, many infectious disease experts are concerned that the spread of H5N1 influenza or strains of vancomycin-resistant S. aureus could be magnified in hospitals. Vaccination and hand hygiene are the most effective measures for preventing the spread of hospital-acquired infections. However, no data or theoretical framework exist to identify the healthcare workers who are most likely to acquire and transmit infectious agents (i.e., those who should have the highest priority in influenza vaccine campaigns or should be the focus of programs to increase adherence with hand hygiene). In addition, the mathematical models previously used to study spread of infections in hospitals are based on the assumption that the mixing of healthcare workers and patients is homogeneous. These models ignore the social networks (clustering and small world properties) that define the true nature of the interactions between healthcare workers and patients and thus can yield misleading results. Dr. Polgreen's hypothesis is that some groups of healthcare workers are substantially more likely to spread nosocomial pathogens than are other groups. To test this hypothesis, he will collect information [from direct observation, keyboard login-data, Radio Frequency Identification Badge (RFID) data, and sensor mote data] on the contacts between healthcare workers and patients at University of Iowa Hospitals and Clinics. This information will allow him to develop graph (social network) models that describe the spread of pathogens that are transmitted by the respiratory route (contact within 3 feet) and those spread by direct contact. He will then estimate, through mathematical simulations, how different vaccination and hand hygiene strategies in various groups of healthcare workers affect the spread of nosocomial pathogens. This work will lead to more effective infection control interventions and strategies.