Collaborative Research: DMS/NIGMS 1: Identifiability investigation of Multi-scale Models of Infectious Diseases

合作研究：DMS/NIGMS 1：传染病多尺度模型的可识别性研究

• 批准号: 10794480
• 负责人: Stanca M. Ciupe
• 金额: $ 18.34万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-27 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794480
• 关键词: 2019-nCoV; Animals; Biological; Birds; Case Study; Cessation of life; Collaborations; Collection; Communicable Diseases; Couples; Culicidae; Data; Data Collection; Data Set; Databases; Development; Disease; Disease Outbreaks; Disease Progression; Education; Educational workshop; Epidemiology; Experimental Designs; Frequencies; Goals; Grant; Health; Human; Immune system; Immunologic Markers; Incidence; Individual; Infection; Infectious Agent; Interdisciplinary Study; Intervention; Investigation; Length; Link; Mathematics; Measurement; Methods; Modeling; National Institute of General Medical Sciences; Noise; Parameter Estimation; Pathogenesis; Pharmacologic Substance; Play; Policies; Population; Probability; Process; Public Health; Quarantine; Recommendation; Reporting; Reproducibility; Reproducibility of Results; Research; Role; Sample Size; Selection Bias; Series; Societies; Source; Specific qualifier value; Statistical Models; Students; System; Techniques; Testing; Time; Uncertainty; Viral; Viral Load result; Viral Markers; Virus; Virus Diseases; Work; age related; curriculum development; data exchange; disease transmission; improved; infection rate; infectious disease model; influenza infection; interest; junior high school; mathematical methods; mathematical model; model development; multi-scale modeling; multidisciplinary; outcome prediction; outreach; pathogen; predictive modeling; programs; protein biomarkers; recruit; response; student training; synergism; transmission process; undergraduate student; vector; vector mosquito; vector transmission; wild bird

The emergence and re-emergence of pathogens and their impact on society has reinforced the need for integration and synergy across scientific fields and biological scales in order to advance understanding, predicting, and responding to pathogen spread. Multi-scale mathematical models that consider the timing and length of individual infections when modeling transmission into the population can aid recommendations for optimal interventions. One shortcoming when evaluating data using multi-scale models comes from data scarcity in the expansion stages of the infection and transmission, the differences in data magnitude and frequency at each scale, together with the complexity of the models considered. To determine the source of combined biases in parameter estimation, we will use a combined empirical-theoretical approach for investigating structural and practical parameter identifiability of multi-scale models of infectious diseases that may inform optimal experimental design. The proposed research will facilitate a better understanding of the sources of uncertainty when fitting multi-scale models to multi-scale infectious disease data, with a focus on Usutu and SARS-CoV-2 viruses. By combining empirical and theoretical approaches we aim to determine structural and practical parameter identifiability of multi-scale models, to inform optimal experimental design, and to improve our ability to make predictions and suggest interventions. Our proposal will focus on three major mathematical challenges: (1) Developing methods for improving practical identifiability in within-host systems; (2) Use experimental data to inform development of transmission models; (3) Build a quantitative framework to predict parameter identifiability in multi-scale systems. The overarching goal of the proposed work is to integrate multi-scale mathematical model development and statistical models for data fitting with collection of longitudinal virus titers and probability of transmission data in order to decrease uncertainty and improve results reproducibility. This will ultimately improve our understanding of infection disease transmission and persistence.

病原体的出现和重新出现及其对社会的影响增强了需求 跨科学领域和生物量表的整合和协同作用，以提高理解， 预测并响应病原体扩散。考虑时间安排的多尺度数学模型 在建模到人群时，单个感染的长度可以帮助 最佳干预措施的建议。使用多尺度评估数据时的一个缺点 模型来自感染和传播的扩展阶段中的数据稀缺性，差异 在每个尺度上的数据幅度和频率中，以及所考虑的模型的复杂性。到 确定参数估计中组合偏差的来源，我们将使用一种合并的经验理论方法来研究多尺度模型的结构和实际参数可识别性 可能为最佳实验设计提供信息的传染病。拟议的研究将促进 将多尺度模型拟合到多尺度感染性时，可以更好地理解不确定性的来源 疾病数据，侧重于USUTU和SARS-COV-2病毒。通过结合经验和理论 我们旨在确定多尺度模型的结构和实践参数可识别性， 为最佳的实验设计提供信息，并提高我们做出预测的能力并建议 干预措施。我们的建议将集中在三个主要的数学挑战上：（1）开发用于 改善宿主内部系统中的实践可识别性； （2）使用实验数据为开发提供信息 传输模型； （3）建立一个定量框架，以预测多尺度的参数可识别性 系统。拟议工作的总体目标是整合多尺度数学模型 通过收集纵向病毒滴度和概率收集的数据拟合的开发和统计模型 传输数据以减少不确定性并改善结果可重复性。最终将 提高我们对感染疾病传播和持久性的理解。