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

项目总结： 数学分析、计算统计和机器学习正越来越多地被部署到 了解和预测医疗保健相关感染(HAI)和抗菌素耐药性的动态 感染(ARI)。然而，这些模型对指导临床和卫生政策决策的效用往往 目前仍不清楚。其中一个挑战是，随着禽流感的流行，模型校准很快就会过时 而艾瑞也变了。为了解决这一差距，我们建议使用数学建模和机器学习 构建决策技术的方法，以改进风险评估、预防和 控制HAI和ARI。我们提出的技术考虑了空间和时间动态，提供了 向临床医生提供持续、实时的反馈，并对风险因素和疾病患病率的变化保持稳健 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，以大幅减轻HAI和ARI的负担。我们把精力集中在两个最重要的 重要HAI：耐甲氧西林金黄色葡萄球菌和艰难梭状芽孢杆菌感染。进行，进行 在这些研究中，我们组建了一个数学建模团队，机器学习专家，健康 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。临床、微生物学和环境数据将用于培训我们的模型 来自三个学术第四医学中心和一个不断扩大的社区医院网络。第一 目的是计算感染或传播HAI或ARI的特定患者的风险。我们假设 更准确地确定获得HAI或ARI的风险时，患者的活动和 病原体暴露被整合到预测模型中。这种类型的分析也有望改善 用于检测HAI和ARI疫情的自动化系统的风险评估。第二个目标是防止 侵袭性耐甲氧西林金黄色葡萄球菌(MRSA)感染。其中一个目标是表明 可通过以下途径经济高效地减少侵袭性MRSA感染和医院传播 对谁应该进行无症状携带者筛查和非殖民化的个性化决定。我们的第三个目标 是控制艰难梭状芽胞杆菌感染(CDI)的传播。我们假设，通过计算 避免了CDI的数量和节省的成本，疾病传播模型将展示在 对传播CDI的高危患者采取主动接触预防措施。我们也期待着 确定导致CDI超级传播风险并将从中受益的环境途径 加强监测和净化。最后，为了更好地理解抗生素的重要性 管理计划，我们描述了患者的抗生素、感染、社交、暴露和 殖民史在个人获得侵袭性MRSA感染和CDI的风险中起着重要作用。

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.