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

项目摘要/摘要 抗菌素耐药(AR)病原体仍然是医疗保健相关感染(HAI)的主要原因 美国。事实上，这些现有的和新出现的抗药性药物的流行继续 给美国的医疗体系带来沉重的负担。更好地控制现有的与AR病原体相关的HAI 并为可能出现的新型AR有机体做好准备，更好、更有针对性的识别和 需要制定干预策略。在这里，为医疗保健中的传染病建模 改善预防研究和医疗服务(Mind Healthcare)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暴发，量化单个患者的定居风险，并确定最佳干预措施 接近了。具体地说，我们将使用多种AR病原体的住院记录和诊断数据 从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)可模拟AR病原体传播的集合种群模型 跨多个医疗机构的动态；以及2)基于代理的模型，能够模拟个人- 将患者的感染状况、传播动态和在多家医院内的流动情况进行分级。这些型号 将与贝叶斯推理方法结合使用来模拟观察到的AR病原体的爆发， 估计关键的流行病学特征和个体中无症状携带的概率 并支持AR病原体预测系统的开发。由于模型是高维的 并且观测数据具有稀疏性、新的推理方法、能够进行数据扩充和高效的模型 优化，也将得到发展。此外，我们将使用优化的模型结构来自由运行 模拟测试六种干预措施的有效性：1)手卫生和屏障预防措施；2)隔离 感染；3)环境清洁；4)医院内积极的病人筛查；5)接触者追踪；以及6) 入场时放映。这些干预措施将单独和捆绑进行测试，并用于通知目标 控制接近了。此外，我们将开发一个框架，以确定最大限度地 在成本和物流受限的情况下，降低HAI比率。最后，我们建议与疾病控制与预防中心和 Mind Healthcare网络中的其他研究小组开发标准化的干预方案和 不同模型形式之间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.