Mathematical Modeling of the Superspreaders of Antibiotic-Resistante Bacteria

描述（由申请人提供）：抗微生物细菌（ARB）的出现和传播是最严重的公共卫生威胁之一。入住医疗机构的患者是ARB的主要储存库。据估计，5-10%的患者发生与住院直接相关的感染，导致美国每年超过9万人死亡。与这些感染有关的病原中约有70%对一种或多种抗菌素具有耐药性。与抗菌素敏感细菌引起的感染相比，ARB引起的感染死亡率最高可高5倍。为了全面了解导致ARB传播的众多相互关联的变量，有必要整合个人层面和人口层面的数据。生物学研究为了解单个患者水平的行为提供了必要的数据，但ARB的传播需要了解患者和医护人员之间ARB的复杂传播动态。这一跨学科建议的目标是将个体患者数据与数学模型相结合，以提供ARB传播的人群水平分析。将建立模型来描述ARB的超级传播者，这是导致ARB传播的主要患者亚组。个人患者数据将从100多万住院患者的广泛综合在线病历系统中获得。患者ARB细菌负荷及其与抗生素暴露（ARB传播的主要危险因素）的相关性数据将从以往的前瞻性临床研究中获得。这些模型的模拟将用于预测预防策略的有效性。这项建议将加强医学、公共卫生和数学在研究和教育方面的伙伴关系。研究结果将通过会议、出版物、本科生、研究生和研究生教育、医学生、护士和医生广泛传播。拟议的工作将有助于更好地了解导致ARB传播的因素，并将有助于制定和执行有效的公共卫生政策。

DESCRIPTION (provided by applicant): The emergence and spread of antimicrobial-resistant bacteria (ARB) is one of the most serious public health threats. Patients admitted to healthcare institutions are the main reservoirs of ARB. It is estimated that 5-10% of patients develop an infection directly related to their hospitalization, resulting in over 90,000 deaths per year in the US. Approximately 70% of the causative pathogens implicated in these infections are resistant to one or more antimicrobials. Infections caused by ARB are associated with up to 5-times higher mortality rates than infections caused by antimicrobial-susceptible bacteria. To gain a complete understanding of the numerous interrelated variables that contribute to the spread of ARB, integration of individual-level and population-level data is necessary. Biological studies provide essential data to understand behavior at the single patient-level, but the spread of (ARB) requires an understanding of the complex transmission dynamics of ARB between patients and healthcare workers. The goal of this interdisciplinary proposal is to integrate individual-patient data with mathematical modeling to provide population-level analysis of the spread of ARB. Models will be created to characterize the superspreaders of ARB, the subgroup of patients responsible for the majority of ARB spread. Individual-level patient data will be obtained from an extensive integrated on-line medical record system of over one million hospitalized patients. Patient data of bacterial loads of ARB and its correlation with antibiotic exposure, the main risk factor for ARB spread, will be obtained from previous prospective clinical studies. Simulations of the models will be used to predict the effectiveness of preventative strategies. This proposal will enhance the partnership between medicine, public health and mathematics in both research and education. The results will be disseminated broadly through conferences, publications, undergraduate, graduate, and post-graduate education, medical students, nurses and physicians. The proposed work will lead to a better understanding of the factors which contribute to the spread of ARB, and will allow the development and implementation of effective public health policies.