RAPID: Accelerating Phylodynamic Analyses of SARS-CoV-2

Evolutionary analyses using genomic data are an essential component of the scientific response to the COVID-19 pandemic, which is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). Inferring the evolutionary history, or phylogeny, of virus samples with sampling time and location information allows scientists to estimate the divergence of viral lineages in time and place. These analyses provide time estimates that predate sampling events. Information about mutations, and the rate of mutation, is inherent to these phylogenetic analyses such that specific viral linages with accelerated mutation rates, if they exist, can be identified. Furthermore, molecular phylodynamics includes not only evolutionary history but also information on viral genetic variation and viral population dynamics, again all in the context of geography and time. The software from this project will be used in SARS-CoV-2 research on: patterns of movement and migration; time of outbreak origin; rate of mutation and detection of significant mutations with potential health impact; prevalence in populations at different geographical scales; reproductive number and impact on policy; and infection-to-case reporting rates. Perhaps of most immediate impact is that software from this project will accelerate tracing and dating the origins of outbreaks in specific geographic regions where contact tracing is not effective. Contact tracing and phylogenetic analyses work on different scales, and thus are complementary. Together they provide a more comprehensive view of the transmission patterns for the current pandemic.

Phylodynamic analyses are particularly rich in terms of inferences, albeit at a considerable computational cost. This project will greatly accelerate phylogenetic and phylodynamic analysis of SARS-CoV-2 data sets, and facilitate their computation on National Science Foundation supported computing resources, academic computing centers, as well as cloud computing environments. Specific activities include designing new strategies for efficient parallel computation of large data sets from viral outbreaks focusing on SARS-CoV-2, developing strategies for removing the barriers to easy use of highly performant parallel phylogenetic and phylodynamic analyses, developing algorithms for implementing these new strategies on graphical processing units (GPUs), and working with others to improve the time to results for analyses of SARS-CoV-2 data sets. This RAPID award is made by the Division of Biological Infrastructure using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.

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.