A computational modeling framework for COVID-19 vaccination

项目总结/摘要 2019冠状病毒病（COVID-19），由严重急性呼吸系统综合征冠状病毒2引起 （SARS-CoV-2）目前仍是全球大流行病。迫切需要定量研究来澄清 目前的疫苗接种运动对大流行演变和经济增长的影响，以及 指导今后的政策发展。本提案的总体目标是建立一个新的 美国COVID-19疫苗接种活动调查的计算建模框架， 并纳入真实的数据，以评估COVID-19疫苗接种对公共卫生和 经济为了实现这一目标，该团队将追求三个具体目标：（1）建模 疫苗接种影响下的COVID-19传播和扩散;（2）对经济模型进行建模 COVID-19疫苗接种的影响;（3）对该州的查塔努加地区进行案例研究 田纳西拟议的研究是重要的，因为它将纳入详细的特点， 当前疫苗接种活动的潜在局限性（如疫苗有效性、分阶段分配 计划、公众对疫苗接种的抵制以及SARS新变种带来的疫苗突破- CoV-2）转化为复杂的建模框架，这将使我们能够做出更准确的预测 对大流行病的进展和长期演变的影响。因此，该项目有望推进 目前对COVID-19传播的理解，并量化疫情之间的相互作用， 在COVID-19影响下的传播、经济增长和疾病预防和干预 疫苗接种，所有这些都对控制和管理这一大流行病很重要。该方法是 创新的计算框架的发展，整合了新的机械和 机器学习模型，并将COVID-19的流行和经济方面联系起来。的 该项目的创新还体现在将复杂的计算模型， 严谨的数学分析、密集的数值模拟和详细的数据验证。项目 代表了应用和计算数学家之间的跨学科合作， 长期对传染病建模感兴趣（Wang），流行病学家， 在疾病预防控制中心的经验和区域COVID-19工作组（希思）的现任成员，一家企业和 管理学教授，有公共卫生背景（马伦），统计学家，有专业知识， 机器学习和生物医学数据分析（MA）。该项目的成功不仅将建立一个 为SARS-CoV-2的复杂传播动力学和健康以及 COVID-19疫苗接种的经济影响，但也为政府提供了重要的指导方针 在大流行病管理和政策制定方面，

Project Summary/Abstract Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global pandemic at present. Quantitative research is urgently needed to clarify the impacts of the current vaccination campaign on the pandemic evolution and economic growth, and to guide future policy development. The overall objective of this proposal is to establish a new computational modeling framework for an investigation of the COVID-19 vaccination campaign in the US, and to incorporate real data to assess the impacts of COVID-19 vaccination on public health and the economy. To achieve this objective, the team will pursue three specific aims: (1) Modeling the transmission and spread of COVID-19 under the impact of vaccination; (2) Modeling the economic impact of COVID-19 vaccination; (3) Conducting a case study for the Chattanooga region in the state of Tennessee. The proposed research is significant because it will incorporate detailed characteristics and potential limitations of the current vaccination campaign (such as the vaccine efficacy, phased allocation schemes, public resistance to vaccination, and vaccine breakthrough due to new variants of SARS- CoV-2) into a sophisticated modeling framework, which will enable us to make more accurate forecasts on the progression and long-term evolution of the pandemic. As such, the project is expected to advance the current understanding of COVID-19 transmission and to quantify the interaction between epidemic spreading, economic growth, and disease prevention and intervention under the impact of COVID-19 vaccination, all of which are important for the control and management of the pandemic. The approach is innovative in the development of a computational framework that integrates novel mechanistic and machine learning models and that connects the epidemic and economic aspects of COVID-19. The innovation of this project is also reflected by the integration of sophisticated computational modeling, rigorous mathematical analysis, intensive numerical simulation, and detailed data validation. The project represents an interdisciplinary collaboration among an applied and computational mathematician with long-term interest in infectious disease modeling (Wang), an epidemiologist with extensive working experiences at CDC and a current member of the regional COVID-19 task force (Heath), a business and management professor with a background in public heath (Mullen), and a statistician with expertise in machine learning and biomedical data analytics (Ma). The success of this project will not only build a solid knowledge base for the complex transmission dynamics of SARS-CoV-2 and the health and economic impacts of COVID-19 vaccination, but also provide important guidelines for the government agencies and public health administrations in pandemic management and policy development.