Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in Delaware

预测建模

• 批准号: 10266331
• 负责人: Steven J. Stanhope
• 金额: $ 23.49万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-30 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266331
• 关键词: 2019-nCoV; Address; Behavioral; Biological Process; COVID-19; COVID-19 pandemic; Cessation of life; Characteristics; Chemical Engineering; Chemicals; Collaborations; Communicable Diseases; Communities; Complement; Complex; Contact Tracing; Dangerousness; Data; Data Science; Data Set; Delaware; Differential Equation; Disease; Disease Outbreaks; Economics; Epidemic; Epidemiology; Equation; Evolution; Felis catus; Foundations; Future; Handwashing; Health Sciences; Healthcare; Hospitalization; Individual; Infection; Infrastructure; Institution; Intervention; Intervention Studies; Kinetics; Laboratories; Learning; Letters; Link; Medical; Medical Research; Modeling; Physiological; Policies; Population; Population Heterogeneity; Process; Protocols documentation; Public Health; Quarantine; Reagent; Recovery; Research; Research Personnel; Resort; Resource Sharing; SARS-CoV-2 infection; Sample Size; Sampling; Specific qualifier value; Speed; Stretching; System; Systems Development; Testing; Time; Translational Research; Treatment Protocols; Uncertainty; Ursidae Family; Vaccines; Viral; Virus; Virus Diseases; Work; base; biological systems; chemical reaction; combat; computer science; curative treatments; data streams; design; dynamical evolution; epidemiological model; experience; flexibility; infection rate; insight; kinetic model; mathematical model; mortality; multidisciplinary; novel; novel coronavirus; pandemic disease; predictive modeling; psychologic; reaction rate; residence; response; social; theories; tool; transmission process; usability; user friendly software

PROJECT SUMMARY AND ABSTRACT. In this project, our team seeks to develop and evaluate a unique predictive modeling approach that can be applied to the spread of SARS-CoV-2 and subsequently adapted to address other emergent infectious diseases. Robust and accurate predictive models are needed to allow healthcare and public health experts to devise and evaluate interventions for controlling viral spread and mitigating the effects of disease. When new disease-caus- ing viruses arise (such as the recent novel coronavirus SARS-CoV-2), deploying useful predictive models is challenging. First, the transmission characteristics within often diverse populations are not immediately under- stood; and second, many existing model frameworks are based on a necessarily simplified set of serially-com- partmentalized transmissions between susceptible, exposed, infected, and recovered (SEIR) groups that may not accurately represent the realities of the new virus. In the current proposal, we develop and validate a novel modeling approach based on principles of chemical reaction kinetics (CRK). The CRK approach allows us to model the infection/transmission of any virus with the same formalism employed to describe the chemical reac- tion of one molecule (an infected individual) with another (an uninfected). Our approach also employs “Residence Time Distribution” theory, which is typically applied to understand large-scale chemical reactors where reagents move, mix, and interact in a complex manner, to capture elegantly and effectively the uncertainties involved in complex disease processes, especially those resulting in recovery or mortality. In the long-term, our CRK-based system will provide a readily-adaptable and facile framework that can be linked to relevant data streams available through INBRE partner institutions in the state of Delaware, which has a population basis that is broadly repre- sentative of the nation, to allow rapid use. In addition, the model itself will be accessible to researchers, clinicians, and public health experts through a convenient online interface. In the long-term, the model and interface will be vetted and deployed following a detailed Resource Sharing Plan designed to assure usability and impact. In the initial 12-month study proposed here, we begin development of this system by utilizing existing datasets for SARS-CoV-2 to deploy a flexible model framework that predicts fundamental aspects of SARS-CoV-2 spread. In short, a set of ordinary differential equations is developed based on CRK principles where the “reaction rate constants” directly connect to physiological and epidemiological parameters and where recovery and death are characterized by directly determinable “Mean-Time-To-Recovery” and “Mean-Time-To-Death” parameters. We will proceed by addressing two aims: 1) Develop and validate a “Chemical Reaction Kinetics”-based model of COVID-19 infection for the State of Delaware; 2) Develop Optimal CRK Model-Based Mitigation Strategies and Implement a Model and Mitigation Strategy in a User-Friendly Software Appropriate for Policymakers. Execution of these aims will provide a new CRK-based model that will provide a foundation for more advanced modeling tools and will serve as a powerful adjunct to the more traditional models based on SEIR-derived frameworks.

项目摘要和摘要。 在这个项目中，我们的团队试图开发和评估一种独特的预测建模方法 应用于SARS-COV-2的传播，随后适用于解决其他新兴的传染病。 需要强大而准确的预测模型，以允许医疗保健和公共卫生专家设计和 评估干预措施，以控制病毒扩散并减轻疾病的影响。当新的疾病 - 可亲 - 出现病毒（例如最近新型的冠状病毒SARS-COV-2），部署有用的预测模型是 具有挑战性的。首先，大多潜水人群中的传播特征通常不会立即不足 摊位；其次，许多现有的模型框架是基于必要的简化串行组合集的基础 易感，暴露，感染和恢复（SEIR）的党派传播，可能 不能准确代表新病毒的现实。在当前的提案中，我们开发并验证了一项小说 基于化学反应动力学原理（CRK）原理的建模方法。 CRK方法使我们能够 建模任何具有相同格式的病毒的感染/传播，以描述化学重新 一个分子（一个被感染的个体）与另一个分子（未感染）。我们的方法也“居住 时间分布理论，通常用于理解试剂的大规模化学反应器 以复杂的方式移动，混合和相互作用，以优雅有效地捕获涉及的不确定性 复杂的疾病过程，尤其是导致康复或死亡率的过程。从长远来看，我们的基于CRK 系统将提供一个易于适应和设施的框架，可以链接到可用的相关数据流 通过特拉华州的INBRE合作伙伴机构，该机构的人口基础广泛代表 国家的宣传，以允许快速使用。此外，研究人员，临床医生可以访问该模型， 和公共卫生专家通过便利的在线界面。从长远来看，模型和界面将是 遵循详细的资源共享计划进行审查和部署，旨在确保可用性和影响。在 在此提出的最初的12个月研究，我们通过使用现有数据集来开发该系统 SARS-COV-2部署灵活的模型框架，以预测SARS-COV-2传播的基本方面。 简而言之，一组普通的微分方程是根据CRK原理开发的，其中“反应速率 常数”直接连接到物理和流行病学参数以及恢复和死亡的地方 以直接确定的“平均时间到恢复”和“平均时间到死亡”参数为特征。我们 将通过解决两个目标来进行：1）基于“化学反应动力学”的基于“化学反应动力学”的模型 特拉华州的Covid-19感染； 2）制定基于CRK模型的最佳缓解策略和 在适合决策者的用户友好软件中实施模型和缓解策略。执行 这些目标中有一个新的基于CRK的模型，该模型将为更先进的建模提供基础 工具，将作为基于SEIR衍生框架的更传统模型的强大辅助手段。