Statistical innovation to integrate sequences and phenotypes for scalable phylodynamic inference

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PROJECT SUMMARY/ABSTRACT This proposal targets the design, development and distribution of Bayesian statistical methods and software to study the historical and real-time emergence of rapidly evolving pathogens, such as Ebola, human immun- odeficiency, influenza, Lassa, SARS-CoV-2, West Nile, yellow fever and Zika viruses. The proposal exploits novel scalable data integration to equip us for large-scale epidemics and pandemics and help inform action- able public health policy. Our multidisciplinary team carries expertise across statistical thinking, data science, evolutionary biology and infectious diseases to leverage advancing sequencing technology and high-throughput biological experimentation that can characterize 1000s of pathogen genomes, phenotype measurements, eco- logical and clinical information from a single outbreak. Our chief innovations are three-fold. First, we will invent and implement scalable Bayesian phylodynamic techniques to integrate phenotypic measurements and study their correlated evolution with disease spread. Second, we will foster biologically-rich evolutionary models to map and understand heterogeneity in disease evolution through new efficient algorithms. Third, we will develop high-dimensional and mixed-type phenotype models to link concerted viral genotype / phenotype changes using massively parallel computing. Although no competing software exists to integrate phenotype and sequence data at this scale, we will compare restricted cases of our models with reduced datasets to current state-of-the-art approaches to evaluate computational performance improvement and bias that these limitations inject using real- world examples. This proposal will deliver low-level toolbox libraries and user-friendly software for deployment across a rapidly expanding range of large-scale problems in statistics and medicine.