Multi-Scale Investigation of SARS-CoV-2 Infection

截至2021年5月初，新型冠状病毒2型严重急性呼吸综合征(SARS-CoV-2)已在全球范围内感染超过1.52亿人，导致超过310万人死亡。这导致迫切需要开发能够检测、预防和治疗这种感染的新型抗病毒药物和疫苗，到目前为止，已有10多亿人接种了至少一剂疫苗。虽然大流行正在展开，但为了控制其影响，需要在接种疫苗和未接种疫苗的人群中提供检测和隔离战略方面的指导。该项目使用多学科方法，将病毒学和免疫学数据与数学和计算模型相结合，以揭示个体感染变异的原因、它们对人口传播的贡献、严重感染的标志以及在接种疫苗和不接种疫苗的情况下的最佳测试和隔离策略。这些方法将为针对SARS-CoV-2严重感染的特定蛋白质和治疗类型提供可测试的假设。提出SARS-CoV-2缓解策略是复杂的，因为对SARS-CoV-2在个人体内的动态及其引发的免疫反应的有限了解，以及感染是否导致无、部分或终身灭菌免疫的不确定性。该项目的重点是在分子、细胞和人群水平上使用新的多尺度数学模型，目的是预测SARS-CoV-2在个体内的动力学，免疫保护机制和导致发病的机制，以及个体感染、检测策略和病毒进入人群的传播之间的联系。这项研究将集中在三个主要的数学挑战上：(1)开发主机内、主机之间的多尺度系统；(2)开发用于模型参数和不确定性估计的数据分析方法；以及(3)为测试实践建立最优控制问题。该项目的总体目标是将数学模型与单细胞中性粒细胞数据、SARS-CoV-2病毒滴度和测试能力知识相结合，以更好地控制SARS-CoV-2的传播并降低疾病严重程度。该奖项反映了NSF的法定使命，通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为是值得支持的。

By the beginning of May 2021, novel coronavirus-2 severe acute respiratory syndrome (SARS-CoV-2) has infected more than 152 million people and resulted in more than 3.1 million deaths worldwide. This has caused an urgent need for the development of novel antivirals and vaccines capable of detecting, preventing, and treating the infection, and to date, more than a billion people have received at least one vaccine dose. While the pandemic is unfolding, guidance regarding testing and quarantining strategies in vaccinated and unvaccinated populations is needed in order to control its effects. This project uses a multi-disciplinary approach, merging virological and immunological data with mathematical and computational models to uncover causes for variability in individual infections, their contributions to transmission in the population, markers of severe infections, and best testing and quarantining strategies in the presence and absence of vaccination. These approaches will provide testable hypotheses for targeting specific proteins and types of treatments for severe infections with SARS-CoV-2. Suggesting SARS-CoV-2 mitigation strategies is complicated by limited knowledge regarding SARS-CoV-2 dynamics inside an individual, the immune responses it elicits, and the uncertainty of whether infections result in no, partial, or life-long sterilizing immunity. The focus of this project is to use novel multi-scale mathematical models at the molecular, cellular and population levels with the aim of predicting the kinetics of SARS-CoV-2 inside an individual, the mechanisms of immune protection and those that lead to pathogenesis, and the connection between individual infections, testing strategies, and the transmission of the virus into the population. The research will focus on three major mathematical challenges: (1) Developing within-host between-host multi-scale systems; (2) Developing data analytics methods for model parameter and uncertainty estimation; and (3) Establishing an optimal control problem for testing practices. The overarching goal of the project is to integrate mathematical models with single-cell neutrophil data, SARS-CoV-2 virus titers, and knowledge of testing abilities to better control the spread of SARS-Cov-2 and reduce disease severity. By bridging the scales, the PI aims to provide a unified picture of SAS-CoV-2 infection that can benefit both the medical and the public health community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.