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，该方法可以 应用于SARS-CoV-2的传播，随后被调整以应对其他紧急传染病。 需要强大和准确的预测模型，以使医疗保健和公共卫生专家能够设计和 评估控制病毒传播和减轻疾病影响的干预措施。当新的疾病-原因- 病毒的出现(例如最近的新型冠状病毒SARS-CoV-2)，部署有用的预测模型是 很有挑战性。首先，通常不同人群中的传播特征不会立即低于- 其次，许多现有的模型框架都是基于一组必要简化的串行化组件-- 易感、暴露、感染和康复(SEIR)组之间的部分传播，可能 不能准确反映新病毒的真实情况。在当前的提案中，我们开发并验证了一部小说 基于化学反应动力学原理的建模方法。CRK方法使我们能够 用描述化学反应的相同形式对任何病毒的感染/传播进行建模。 一种分子(受感染的个体)与另一种分子(未受感染的个体)相互作用。我们的方法还采用了“居住地” “时间分布”理论，这一理论通常被应用于理解大型化学反应器中的反应物 以复杂的方式移动、混合和交互，以优雅而有效地捕捉到 复杂的疾病过程，尤指那些导致康复或死亡的过程。从长远来看，我们基于CRK的 系统将提供易于适应和方便的框架，该框架可以链接到可用的相关数据流 通过INBRE在特拉华州的伙伴机构，特拉华州拥有广泛代表的人口基础- 国家的句子，以允许快速使用。此外，该模型本身将向研究人员、临床医生、 和公共卫生专家通过一个方便的在线界面。从长远来看，模型和界面将是 根据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力。在 这里提出的最初12个月的研究，我们开始开发这个系统，利用现有的数据集 部署一个灵活的模型框架，预测SARS-CoV-2传播的基本方面。 简而言之，在CRK原理的基础上发展了一组常微分方程式，其中反应速率 常量“直接关系到生理和流行病学参数，以及康复和死亡 以可直接确定的“平均恢复时间”和“平均死亡时间”参数为特征。我们 我将着手解决两个目标：1)开发和验证基于化学反应动力学的模型 新冠肺炎感染对特拉华州的影响；2)开发基于CRK模型的最优缓解策略和 在适合政策制定者的用户友好软件中实施模型和缓解策略。行刑 将提供基于CRK的新模型，该模型将为更高级的建模提供基础 工具，并将作为基于SEIR派生框架的更传统模型的强大附件。