Analysis and Simulation of Bacterial Infections and Resource Strain in Hospitals during the COVID-19 Pandemic

项目总结/摘要 抗微生物剂耐药（AR）病原体仍然是美国医疗保健相关感染（HAI）的主要原因。 美国的事实上，这些现有的和新出现的耐药药物的流行继续， 给美国的医疗体系带来沉重负担。更好地控制现有AR病原体相关HAI 并为可能出现的新AR生物体做好准备，更好、更有针对性的鉴定， 需要制定干预战略。在这里，对于这个医疗保健中的传染病建模， 改善预防研究和医疗保健提供（MInD医疗保健）网络的研究项目 项目，我们建议开发一个新的模型推理系统的层次结构，能够模拟和 预测HAI爆发，量化个体患者定植风险，并确定最佳干预措施 接近。具体来说，我们将使用多种AR病原体的住院记录和诊断数据 来自纽约市的四家主要医院，进行了一系列的建模研究。我们将开发两个 数学建模结构：1）能够模拟AR病原体传播的集合种群模型 跨多个医疗保健设施的动态;以及2）能够模拟个体- 各级患者感染状况、传播动态和多个医院内的移动。这些模型 将与贝叶斯推理方法结合使用，以模拟观察到的AR病原体爆发， 估计关键的流行病学特征和个体间无症状携带的概率 患者，并支持AR病原体预测系统的开发。由于模型是高维的， 并且观测值是稀疏的，新的推理方法，能够对数据进行扩充，建立高效的模型 优化，也将得到发展。此外，我们将使用优化的模型结构， 模拟测试六项干预措施的有效性：1）手部卫生和屏障预防措施; 2）隔离 感染; 3）环境清洁; 4）医院内的主动患者筛查; 5）接触者追踪;以及6） 入院时的筛查。这些干预措施将单独和捆绑测试，并用于告知目标 控制方法。此外，我们将开发一个框架，用于确定最大限度地 考虑到成本和后勤限制，降低人工智能费率。最后，我们建议与疾病预防控制中心和 MInD医疗保健网络中的其他研究小组开发标准化干预方案， 在不同的模型形式之间使用的模拟干预结果的相互比较， 网络

PROJECT SUMMARY/ABSTRACT Antimicrobial resistant (AR) pathogens remain a major cause of healthcare associated infections (HAIs) in the United States. Indeed, the prevalence of these existing and emerging drug-resistant agents continues to impose a heavy burden on U.S. healthcare systems. To better control existing AR pathogen-associated HAIs and prepare for the possible emergence of a novel AR organism, better, more targeted identification and intervention strategies need to be developed. Here, for this Modeling Infectious Diseases in Healthcare Research Projects to Improve Prevention Research and Healthcare Delivery (MInD Healthcare) network project, we propose to develop a hierarchy of new model-inference systems capable of simulating and forecasting HAI outbreaks, quantifying individual patient colonization risk, and identifying optimal intervention approaches. Specifically, we will use hospitalization records and diagnostic data for multiple AR pathogens from four major hospitals in New York City to conduct a series of modeling studies. We will develop two mathematical modeling structures: 1) a metapopulation model capable of simulating AR pathogen transmission dynamics across multiple healthcare facilities; and 2) an agent-based model capable of simulating individual- level patient infection status, transmission dynamics, and movements within multiple hospitals. These models will be used in conjunction with Bayesian inference methods to simulate observed outbreaks of AR pathogens, estimate critical epidemiological characteristics and asymptomatic carriage probabilities among individual patients, and support development of an AR pathogen forecasting system. As the models are high dimension and the observations are sparse, new inference methods, capable of data augmentation and efficient model optimization, will also be developed. Additionally, we will use the optimized model structures to run free simulations testing the effectiveness of six interventions: 1) hand hygiene and barrier precautions; 2) isolation of infections; 3) environmental cleaning; 4) active patient screening within hospitals; 5) contact tracing; and 6) screening at admission. These interventions will be tested singly and in bundles and used to inform targeted control approaches. Further, we will develop a framework for identifying intervention bundles that maximally reduce HAI rates given cost and logistical constraints. Lastly, we propose to collaborate with the CDC and the other research groups in the MInD Healthcare network to develop standardized intervention scenarios and inter-comparisons of simulated intervention outcomes among the different model forms used across the network.