Modeling of infectious network dynamics for surveillance, control and prevention enhancement (MINDSCAPE)

用于加强监测、控制和预防的感染网络动态建模 (MINDSCAPE)

• 批准号: 10662399
• 负责人: Travis Christian Porco
• 金额: $ 40.03万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10662399
• 关键词: Modeling; infectious; network; dynamics; surveillance

PROJECT SUMMARY: Mathematical analysis, computational statistics, and machine learning are increasingly being deployed to understand and predict the dynamics of healthcare associated infections (HAI) and antimicrobial-resistant infections (ARI). However, the utility of these models to guiding clinical and health policy decisions often remains unclear. One challenge is that model calibration quickly becomes obsolete as the epidemiology of HAI and ARI changes. To address this gap, we propose to use mathematical modeling and machine learning approaches to build decision-making technologies that improve the risk assessment, prevention, and control of HAI and ARI. Our proposed technologies account for spatial and temporal dynamics, provide continuous, real-time feedback to clinicians and are robust to changes in risk factors and disease prevalence over time. We anticipate that implementation of these technological improvements will help healthcare institutions to substantially reduce the burden of HAI and ARI. We concentrate our efforts on two of the most important HAI: methicillin-resistant Staphylococcus aureus and Clostridioides difficile infections. To conduct these studies, we assembled a team of mathematical modelers, machine learning specialists, health economists, clinical informaticists, infectious disease physicians, and hospital epidemiologists based in California, New York, and Texas. Clinical, microbiological and environmental data to train our models will come from three academic quaternary medical centers and an expanding network of community hospitals. The first aim is to calculate the patient-specific risk of acquiring or transmitting a HAI or ARI. We hypothesize that the risk of acquiring an HAI or ARI is more accurately determined when data on patient movement and pathogen exposure are integrated into predictive models. This type of analysis is also expected to improve the risk assessment of automated systems used to detect HAI and ARI outbreaks. The second aim is to prevent invasive methicillin-resistant Staphylococcus aureus (MRSA) infections. One objective is to show that cost-effective reduction of invasive MRSA infections and hospital-based transmission can be achieved via personalized decisions for who should be screened for asymptomatic carriage and decolonized. Our third aim is to control the spread of Clostridioides difficile infections (CDI). We hypothesize that by calculating the number of CDI averted and the cost saved, models of disease transmission will demonstrate the benefit pre- emptive adoption of contact precautions for patients who are at high risk of transmitting CDI. We also expect to identify environmental pathways that contribute to the risk of CDI superspreading and would benefit from enhanced surveillance and decontamination. Finally, to better understand the importance of antibiotic stewardship programs, we characterize the specific role a patient's antibiotic, infection, social, exposure and colonization history plays in the personal risk of acquiring an invasive MRSA infection and CDI.

项目概要： 数学分析、计算统计和机器学习越来越多地被应用于 了解并预测医疗保健相关感染 (HAI) 和抗菌药物耐药性的动态 感染（ARI）。然而，这些模型在指导临床和卫生政策决策方面的效用常常 仍不清楚。一项挑战是，随着 HAI 的流行病学研究，模型校准很快就会过时。 和 ARI 的变化。为了解决这一差距，我们建议使用数学建模和机器学习 建立决策技术的方法，以改进风险评估、预防和 HAI 和 ARI 的控制。我们提出的技术考虑了空间和时间动态，提供 向临床医生提供持续、实时的反馈，并且对风险因素和疾病患病率的变化具有稳健性 随着时间的推移。我们预计这些技术改进的实施将有助于医疗保健 机构大幅减轻 HAI 和 ARI 的负担。我们将精力集中在两个最重要的领域 重要的HAI：耐甲氧西林金黄色葡萄球菌和艰难梭菌感染。进行 在这些研究中，我们组建了一个由数学建模师、机器学习专家、健康专家组成的团队 经济学家、临床信息学家、传染病医生和医院流行病学家 加利福尼亚州、纽约州和德克萨斯州。用于训练我们模型的临床、微生物和环境数据将会到来 来自三个学术四级医疗中心和不断扩大的社区医院网络。第一个 目的是计算患者感染或传播 HAI 或 ARI 的特定风险。我们假设 当有关患者活动的数据和 病原体暴露被纳入预测模型。这种类型的分析也有望改善 用于检测 HAI 和 ARI 爆发的自动化系统的风险评估。第二个目的是防止 侵袭性耐甲氧西林金黄色葡萄球菌 (MRSA) 感染。目标之一是表明 可以通过以下方式经济有效地减少侵入性 MRSA 感染和医院传播 个性化决定哪些人应该接受无症状携带者筛查和去殖民化。我们的第三个目标 是为了控制艰难梭菌感染（CDI）的传播。我们假设通过计算 避免了 CDI 的数量并节省了成本，疾病传播模型将证明预先的好处 对传播 CDI 高风险的患者主动采取接触预防措施。我们也期望 确定导致 CDI 超级传播风险的环境途径，并从中受益 加强监测和净化。最后，为了更好地了解抗生素的重要性 在管理计划中，我们描述了患者的抗生素、感染、社交、暴露和感染的具体作用 定植史影响个人感染侵入性 MRSA 感染和 CDI 的风险。