Social Networks and the Spread of Influenza and other Nosocomial Infections

描述（由申请人提供）：在该K01申请中，Philip Polgreen（医学博士）寻求获得与社交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，从而最大限度地减少医院感染（特别是流感、MRSA和C. difficile）。Polgreen博士召集了一群非常强大的导师和跨学科合作者，他们都高度致力于他的成功。医疗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：感染传染性病原体或被传染性病原体定殖的患者往往与免疫系统受损的未感染患者非常接近。例如，SARS在社区传播不多，但在医院广泛传播。MRSA和C.艰难梭菌在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S菌株的传播。金黄色葡萄球菌在医院里会被放大。接种疫苗和手部卫生是预防医院获得性感染传播的最有效措施。然而，不存在数据或理论框架来识别最有可能获得和传播传染性病原体的医护人员（即，那些应该在流感疫苗接种活动中享有最高优先权或应该成为提高手部卫生遵守率计划的重点的人）。此外，以前用于研究医院感染传播的数学模型是基于医疗工作者和患者的混合是均匀的假设。这些模型忽略了社交网络（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性质，因此可能会产生误导性的结果。Polgreen博士的假设是，某些群体的医护人员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，他将收集有关爱荷华州大学医院和诊所的医护人员与患者之间接触的信息[来自直接观察、键盘登录数据、射频识别徽章（RFID）数据和传感器微粒数据]。这些信息将使他能够开发图形（社交网络）模型，描述通过呼吸道传播（3英尺内接触）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医疗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医院病原体的传播。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.