RAPID: Collaborative Research: Mathematical tools for analysis of genomic diversity of SARS-CoV-2 virus in the context of its co-evolution with host populations

要了解当前冠状病毒大流行的潜在过程及其在公共卫生干预措施（如社交距离）下可能复发的可能性，需要了解病毒在人类中可能如何演变。为此，需要新的统计和计算技术来提取病毒RNA序列中的信息，这些信息现在可以从世界许多地方的患者样本中获得。 这项工作将集中在描述突变在病毒基因组中积累的机制，部分是通过与宿主的相互作用。然后，该项目将开发新的统计方法，以比较和对比这些机制。更深入地了解病毒基因组中的突变是如何积累的，应该能更好地推断出将在人群中存活的不同病毒株的性质。这些信息反过来可以帮助设计疫苗。该项目由博士后研究人员和国际合作者参与，将开发新的概率和统计方法，根据病毒种群内和不同冠状病毒物种之间的分子遗传学和突变谱来估计SARS-CoV-2的进化速率和模式。SARS-CoV-2病毒的进化历史将被重建，并用于推断其使复发的进化潜力，同时考虑到人类种群和动物病毒抗性的进化。 一组基于分支过程模型的随机动力学模型将能够估计宿主群体中病毒群体的漂移、突变和选择模式。宿主动态的空间方面将基于基于代理的模型（ABM），以期更好地了解病毒的进化是如何由其宿主驱动的。将开发用于推断分支过程模型和ABM相关参数的新统计方法，从近似贝叶斯计算方法的实施开始，该方法可以解决无法明确计算可能性所造成的困难。该赠款将使用冠状病毒援助，救济，该奖项反映了NSF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。

Understanding the potential courses of the current coronavirus pandemic and its possible recurrences in the light of public health interventions, such as social distancing, requires knowledge of how the virus is likely to evolve in humans. To this end, novel statistical and computational techniques are needed to extract the information available in the viral RNA sequences now available from patients sampled from many parts of the world. The work will focus on descriptions of the mechanisms by which mutations accumulate in the viral genome, in part through interactions with their hosts. The project will then develop novel statistical approaches that will be needed to compare and contrast these mechanisms. A deeper understanding of how the mutations in the viral genomes are accumulating should provide better inferences about the nature of different strains of the virus that will survive in the human population. This information in turn can aid in the design of vaccines. Postdoctoral research associates and international collaborators are involved in this project.New probabilistic and statistical methods will be developed to estimate rates and patterns of evolution of SARS-CoV-2 based on molecular phylogenies and mutation spectra within the virus population and among different coronavirus species. The evolutionary past of the SARS-CoV-2 virus will be reconstructed and used to infer its evolutionary potential for enabling recurrences, taking into account the evolution of the human population and animal virus resistance. A set of models of stochastic dynamics based on branching process models will enable estimation of drift, mutation, and selection patterns of the virus population in a host population. Spatial aspects of the host dynamics will be based on agent-based models (ABMs), with a view to better understanding how the evolution of the virus is driven by that of its host. Novel statistical methods for inference about relevant parameters of both the branching process models and the ABMs will be developed, starting from implementations of Approximate Bayesian Computation approaches that can address the difficulty caused by not being able to explicitly compute likelihoods.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.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.