Social Networks and the Spread of Influenza and other Nosocomial Infections

描述(申请人提供)：在这份K01申请中，菲利普·波尔格林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，将医院感染(特别是流感、MRSA和艰难梭菌)的传播降至最低。波尔格林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200万名患者。此外，医院是传染性病原体传播的放大装置：感染或定居了可传播病原体的患者往往与免疫系统受损的未感染患者关系密切。例如，SARS在社区中的传播并不多，但在医院中广泛传播。历史上，MRSA和艰难梭菌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放大。接种疫苗和手部卫生是防止医院获得性感染传播的最有效措施。然而，目前还没有数据或理论框架来确定哪些医护人员最有可能获得和传播感染性病原体(即那些在流感疫苗接种活动中应该具有最高优先级的人，或者应该成为提高手部卫生依从性的计划的重点)。此外，以前用于研究医院感染传播的数学模型是基于医护人员和患者的混合是同质的假设。这些模型忽略了定义医护人员和患者之间互动的真实性质的社交网络(集群和小世界属性)，因此可能会产生误导性的结果。波尔格林博士的假设是，一些医护人员群体比其他群体传播医院病原体的可能性要大得多。为了验证这一假设，他将[从直接观察、键盘登录数据、射频识别徽章(RFID)数据和传感器微尘数据]收集爱荷华大学医院和诊所医护人员与患者之间的接触信息。这些信息将使他能够开发图形(社交网络)模型，描述通过呼吸道途径(3英尺以内的接触)传播的病原体和通过直接接触传播的病原体。然后，他将通过数学模拟，估计不同医护人员群体中不同的疫苗接种和手卫生策略如何影响医院内病原体的传播。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.