Characterizing dynamics of SARS-CoV-2 transmission and host response

This project will improve our understanding of the spread of SAR-CoV-2, the virus responsible for COVID-19. The COVID-19 pandemic remains a global threat given the virus's ability to spread rapidly and ongoing viral evolution. Despite successes in vaccine development, new strains of the virus, sometimes with severe health consequences, continue to be identified. The enduring success of treatment strategies requires the characterization of viral evolution during initial and subsequent treatments, and the health outcomes of viral variants in the context of different treatments. This project develops new computational approaches to integrate diverse data types (such as genetic and medical information), identify patterns of associations among these data types, identify treatment targets, and understand how these relationships change over time and with variation in viral and host populations. The project targets COVID-19 as an exemplar system because of the explosion of available data associated with this system, but the methodology developed will be applicable across a broad range of infectious diseases in humans and other animals and plants. Ten local students (including under-represented groups) will conduct research projects related to this award during summers, and a online, nationwide webinar for 30 students nationwide will be offered to broaden the impact and reach of the funded research. Effective and sustainable treatment strategies (e.g., vaccine, drug therapy) for infectious disease outbreaks require 1) understanding how pathogens evolve and whether discrete omics features (e.g., host/viral genomic variants, differential gene expression) exhibit different clinical outcomes and viral phenotypes (e.g., climate association, drug resistance, and vaccine escape mutants); 2) characterizing the transmissivity element of viral and human systems, and 3) investigating biological pathways of biomarkers of anti-CoV-2 immune response and their occurrence and durability. The interplay between expansion and contraction of viral populations and anti-CoV-2 immune responses are likely to generate distinct genomic signatures and immune responses. This project develops and applies phylodynamic and transmission modeling to advance knowledge about the evolution and epidemiology of SARS-CoV-2. It further develops deep learning and statistical techniques to analyze serological (e.g., metabolite data) and phenotypic clinical data. The project leverages large-scale data from monitored communities, including vaccinated individuals. The methods and analytical approaches can facilitate the investigation of transmission and serological dynamics of infectious diseases using high-throughput data in relation to clinical and epidemiological data. The development of new open-source computational tools provides the research community with a comprehensive framework for characterizing genotype-phenotype associations and functional dynamics for COVID-19 in particular and infectious disease research in general. 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.