CK22-008 Midwest Virtual Laboratory of Pathogen Transmission in Healthcare Settings (MVL-PATHS)

CK22-008 中西部医疗环境中病原体传播虚拟实验室 (MVL-PATHS)

• 批准号: 10696123
• 负责人: Majid Bani Yaghoub
• 金额: $ 18.3万
• 依托单位: UNIVERSITY OF MISSOURI KANSAS CITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10696123
• 关键词: CK22; 008; Midwest; Virtual; Laboratory; CK22; 008; Midwest; Virtual; Laboratory

Midwest Virtual Laboratory of Pathogen Transmission in Healthcare Settings (MVL-PATHS) Project Summary Antimicrobial Resistant (AMR) pathogens have become a significant public health threat. Also, the COVID-19 pandemic has further revealed disparities in healthcare settings. By developing and implementing novel mathematical and computation models, the long-term goals are to optimize AMR control and preventive interventions and to improve the health equity. The central hypothesis is that the outputs of mathematical and computation models will provide optimized and effective guidelines to reduce the threat of AMR pathogen spread and reduce health disparities in healthcare settings. The rationale underlying this project is to fill the critical gap in modeling workforce capacity and develop a new generation of mathematical models for healthcare research. The central hypothesis will be tested by pursuing three specific aims to develop and employ a, (i) One Health modeling approach to understand the source, distribution and spread of AMR Enterobacteriaceae with a focus on Extended- spectrum beta-lactamase (ESBL)-producing E. coli, (ii) a novel Real-Time modeling approach to identify AMR pathogen transmission by asymptomatic spreaders and contaminated medical devices in hospitals, (iii) a novel Agent-Based Nested modeling approach to identify the effects of caregivers as vectors of disease spread, and effects of limited staffing and specialized care on equitable quality of care in nursing homes. We will pursue these aims using an innovative combination of mathematical and computational modeling techniques. These include both recently developed techniques of including human behavior in models and more-established techniques that have been applied very little to the study of health equity and AMR pathogen spread. The workforce development objectives of this proposal are to (i) enhance mathematical and computational modeling research capabilities of the public health workforce and (ii) increase the number of junior modeling professionals that are trained and experienced in modeling transmission of pathogens in healthcare settings partly incorporated with health disparities. The expected outcomes of this work are the successful training of five predoctoral fellows and creating a virtual laboratory of enhanced mathematical models to identify strategies for reducing the threat AMR pathogen spread and reducing health disparities. The results will have an important positive impact immediately because the virtual laboratory can also be used by healthcare professionals to further investigate the drivers of disease spread and estimate the relative benefits of multiple control and prevention strategies in a timely and cost-effective manner. In addition, the research outputs of this project will expand and strengthen national one-health efforts to combat resistance and will have a direct impact on CDC and its public health partners’ ability to reduce the costs, morbidity and mortality of healthcare associated infections.

医疗环境中病原体传播的中西部虚拟实验室（MVL路径） 项目摘要 抗菌素（AMR）病原体已成为重大的公共卫生威胁。另外，Covid-19 大流行进一步揭示了医疗机构的差异。通过开发和实施小说 数学和计算模型，长期目标是优化AMR控制和预防性 干预措施并改善健康平等。中心假设是数学和 计算模型将提供优化有效的指南，以减少AMR病原体的威胁 并减少医疗机构中的健康差异。该项目的基本原理是填补关键空白 建模劳动力能力并开发新一代的医疗保健研究模型。这 中心假设将通过追求三个特定目标来开发和采用a，（i）一种健康建模来检验。 了解AMR肠杆菌科的来源，分布和传播的方法，重点是 频谱β-内酰胺酶（ESBL） - 生产大肠杆菌，（ii）一种新型的实时建模方法，可识别AMR 病原体传播是通过医院中无症状的散布器和受污染的医疗设备传播，（iii）一种新颖 基于代理的嵌套建模方法，以识别护理人员作为疾病传播的媒介的影响，并影响 人员配备有限和专业护理疗养院公平护理质量。我们将追求这些目标 使用数学和计算建模技术的创新组合。这两个都包括 最近开发了将人类行为纳入模型和建立的技术的技术 我们对健康公平和AMR病原体扩散的研究很少。劳动力发展 该提案的目标是（i）增强数学和计算建模的研究能力 公共卫生劳动力和（ii）增加了受过培训的初级建模专业人员的数量和 在医疗保健环境中病原体传播建模的经验部分与健康分布合并。 这项工作的预期结果是成功培训了五个前专家并创建虚拟的培训 增强数学模型的实验室，以识别减少威胁AMR病原体传播的策略 并减少健康差异。结果将立即产生重要的积极影响，因为虚拟 医疗保健专业人员还可以使用实验室来进一步调查疾病传播的驱动因素和 及时及时估算多个控制和预防策略的相对好处。在 此外，该项目的研究成果将扩大和加强国家的一项健康努力来对抗 阻力，将直接影响CDC及其公共卫生伙伴降低成本，发病率的能力 与医疗保健相关感染的死亡率。