Multi-Scale Investigation of SARS-CoV-2 Infection

截至2021年5月初，新型冠状病毒-2型严重急性呼吸系统综合征（SARS-CoV-2）在全球已感染超过1.52亿人，造成310多万人死亡。这导致迫切需要开发能够检测、预防和治疗感染的新型抗病毒药物和疫苗，迄今为止，已有超过10亿人接种了至少一剂疫苗。虽然大流行正在展开，但需要在接种疫苗和未接种疫苗的人群中提供有关检测和隔离战略的指导，以控制其影响。该项目采用多学科方法，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、它们对人群传播的贡献、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检测和隔离策略。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。由于对个体内部SARS-CoV-2动态、其引发的免疫反应以及感染是否导致没有、部分或终身消毒免疫的不确定性的了解有限，提出SARS-CoV-2缓解策略变得复杂。该项目的重点是在分子，细胞和群体水平上使用新的多尺度数学模型，旨在预测个体内SARS-CoV-2的动力学，免疫保护机制和导致发病机制，以及个体感染，检测策略和病毒传播到人群之间的联系。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)开发模型参数和不确定性估计的数据分析方法；(3)建立测试实践的最优控制问题。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病毒滴度和检测能力知识相结合，以更好地控制SARS-CoV-2的传播并降低疾病严重程度。通过弥合不同的范围，该项目旨在提供一个统一的sars - cov -2感染情况，这可以使医学界和公共卫生界都受益。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.