Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning.

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

St. Louis Encephalitis Virus in the Southwestern United States: A Phylogeographic Case for a Multi-Variant Introduction Event.

Since the reemergence of St. Louis Encephalitis (SLE) Virus (SLEV) in the Southwest United States, identified during the 2015 outbreak in Arizona, SLEV has been seasonally detected within Culex spp. populations throughout the Southwest United States. Previous work revealed the 2015 outbreak was caused by an importation of SLEV genotype III, which had only been detected previously in Argentina. However, little is known about when the importation occurred or the transmission and genetic dynamics since its arrival into the Southwest. In this study, we sought to determine whether the annual detection of SLEV in the Southwest is due to enzootic cycling or new importations. To address this question, we analyzed 174 SLEV genomes (142 sequenced as part of this study) using Bayesian phylogenetic analyses to estimate the date of arrival into the American Southwest and characterize the underlying population structure of SLEV. Phylogenetic clustering showed that SLEV variants circulating in Maricopa and Riverside counties form two distinct populations with little evidence of inter-county transmission since the onset of the outbreak. Alternatively, it appears that in 2019, Yuma and Clark counties experienced annual importations of SLEV that originated in Riverside and Maricopa counties. Finally, the earliest representatives of SLEV genotype III in the Southwest form a polytomy that includes both California and Arizona samples. We propose that the initial outbreak most likely resulted from the importation of a population of SLEV genotype III variants, perhaps in multiple birds, possibly multiple species, migrating north in 2013, rather than a single variant introduced by one bird.