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.
