Building mathematical modeling workforce capacity to support infectious disease and healthcare research

建立数学建模劳动力能力以支持传染病和医疗保健研究

• 批准号: 10618070
• 负责人: Peihua Qiu
• 金额: $ 28.41万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10618070
• 关键词: Building; mathematical; modeling; workforce; capacity

Project Summary In this project, we will train predoctoral students to design and conduct infectious disease modeling analyses. The specific aims are: (1) To develop a transparent sequential learning algorithm for spatio-temporal disease surveillance and early detection of disease clusters. This machine-learning-based surveillance algorithm recursively updates its learned objectives using up-to-date data in a real-time fashion, while accommodating seasonality, latent spatio-temporal correlation, and other complex data structure. It does not impose any parametric forms on the data distribution, spatio-temporal data variation, and spatio-temporal data correlation. (2) To develop a competing risks modeling framework for transmission dynamics of antimicrobial-resistant and antimicrobial-susceptible pathogens at the individual level in healthcare centers and at the population level in communities. This framework couples individual exposure data in healthcare centers with aggregated data in communities at large to assess transmissibility, susceptibility and health disparity determinants, and the relative contributions of healthcare-associated and community-associated infections, while accounting for environmental contamination and superspreaders. (3) To develop an agent-based model to assess 1) effectiveness of strategies combining early detection, antimicrobial intervention and patient management on containing both antimicrobial- sensitive and antimicrobial-resistant pathogens; and 2) optimal control strategies for vaccine- preventable infectious diseases. This agent-based model will be developed under the MInD- Healthcare Framework to increase its reproducibility and generalizability. We will systematically evaluate effectiveness of control strategies determined by surveillance, antimicrobial treatment and patient management under several transmission settings and mutation parameters. This project will produce novel statistical methods for surveillance, inference and agent-based modeling. Findings may potentially influence surveillance practice and intervention policies for emerging and endemic pathogens and their drug-resistant mutants. This project will fully prepare trainees for independent and collaborative research in both methodology and practice related to healthcare-associated infections and disease transmission in broader settings.

项目摘要 在这个项目中，我们将培训前学生设计和进行传染病 建模分析。具体目的是： （1）为时空疾病开发透明的顺序学习算法 监视和疾病簇的早期检测。这种基于机器学习的监视 算法递归地更新其学习的目标，该目标是使用实时时尚的最新数据， 同时适应季节性，潜在时空相关性和其他复杂数据 结构。它没有在数据分布（时空数据）上施加任何参数形式 变化和时空数据相关。 （2）开发竞争风险建模框架以进行传输动态 抗菌抗菌和抗菌敏感的病原体在单个水平中的病原体 医疗保健中心和社区的人口水平。这个框架夫妻 医疗保健中心的个人暴露数据，在整个社区中拥有汇总数据 评估可传播性，敏感性和健康差异决定因素以及相对 会计时，与医疗保健相关和社区相关感染的贡献 用于环境污染和超级污染者。 （3）开发基于代理的模型来评估1）早期结合策略的有效性 检测，抗菌干预和患者治疗，均包含两种抗菌 - 敏感和抗菌病原体； 2）疫苗的最佳控制策略 - 可预防的传染病。这种基于代理的模型将在思维中开发 医疗保健框架以提高其可重复性和概括性。我们将系统地 评估通过监视，抗菌治疗确定的控制策略的有效性 以及在几个传输设置和突变参数下的患者管理。 该项目将产生用于监视，推理和基于代理的新型统计方法 造型。调查结果可能会影响监视实践和干预政策 新兴和流行病原体及其耐药突变体。这个项目将完全 为学员准备方法和实践的独立和协作研究 与医疗保健相关的感染和疾病传播有关。