Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county (preprint)

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape. One Sentence SummaryAnalysis of SARS-CoV-2 genomes in King County, Washington show that diverse areas in the same metropolitan region can have different epidemic dynamics.

Rapid and Accurate Identification of SARS-CoV-2 Omicron Variants Using Droplet Digital PCR (RT-ddPCR) (preprint)

Background Mutations in the receptor binding domain of the SARS-CoV-2 Spike protein are associated with increased transmission or substantial reductions in vaccine efficacy, including in the recently described Omicron variant. The changing frequencies of these mutations combined with their differing susceptibility to available therapies have posed significant problems for clinicians and public health professionals. Objective To develop an assay capable of rapidly and accurately identifying variants including Omicron in clinical specimens to enable case tracking and/or selection of appropriate clinical treatment. Study Design Using three duplex RT-ddPCR reactions targeting four amino acids, we tested 419 positive clinical specimens from February to December 2021 during a period of rapidly shifting variant prevalences and compared genotyping results to genome sequences for each sample, determining the sensitivity and specificity of the assay for each variant. Results Mutation determinations for 99.7% of detected samples agree with NGS data for those samples, and are accurate despite wide variation in RNA concentration and potential confounding factors like transport medium, presence of additional respiratory viruses, and additional mutations in primer and probe sequences. The assay accurately identified the first 15 Omicron variants in our laboratory including the first Omicron in Washington State and discriminated against S-gene dropout Delta specimen. Conclusion We describe an accurate, precise, and specific RT-ddPCR assay for variant detection that remains robust despite being designed prior the emergence of Delta and Omicron variants. The assay can quickly identify mutations in current and past SARS-CoV-2 variants, and can be adapted to future mutations.

A method for variant agnostic detection of SARS-CoV-2, rapid monitoring of circulating variants, detection of mutations of biological significance, and early detection of emergent variants such as Omicron (preprint)

The rapid emergence of new SARS-CoV-2 variants raises a number of public health questions including the capability of diagnostic tests to detect new strains, the efficacy of vaccines, and how to map the geographical distribution of variants to better understand patterns of transmission and possible load on healthcare resources. Next-Generation Sequencing (NGS) is the primary method for detecting and tracing the emergence of new variants, but it is expensive, and it can take weeks before sequence data is available in public repositories. Here, we describe a Polymerase Chain Reaction (PCR)-based genotyping approach that is significantly less expensive, accelerates reporting on SARS-CoV-2 variants, and can be implemented in any testing lab performing PCR. Specific Single Nucleotide Polymorphisms (SNPs) and indels are identified that have high positive percent agreement (PPA) and negative percent agreement (NPA) compared to NGS for the major genotypes that circulated in 2021. Using a 48-marker panel, testing on 1,128 retrospective samples yielded a PPA and NPA in the 96.3 to 100% and 99.2 to 100% range, respectively, for the top 10 most prevalent lineages. The effect on PPA and NPA of reducing the number of panel markers was also explored. In addition, with the emergence of Omicron, we also developed an Omicron genotyping panel that distinguishes the Delta and Omicron variants using four (4) highly specific SNPs. Data from testing demonstrates the capability to use the panel to rapidly track the growing prevalence of the Omicron variant in the United States in December 2021.