Comparative transmissibility of SARS-CoV-2 variants Delta and Alpha in New England, USA

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant quickly rose to dominance in mid-2021, displacing other variants, including Alpha. Studies using data from the United Kingdom and India estimated that Delta was 40-80% more transmissible than Alpha, allowing Delta to become the globally dominant variant. However, it was unclear if the ostensible difference in relative transmissibility was due mostly to innate properties of Deltas infectiousness or differences in the study populations. To investigate, we formed a partnership with SARS-CoV-2 genomic surveillance programs from all six New England US states. By comparing logistic growth rates, we found that Delta emerged 37-163% faster than Alpha in early 2021 (37% Massachusetts, 75% New Hampshire, 95% Maine, 98% Rhode Island, 151% Connecticut, and 163% Vermont). We next computed variant-specific effective reproductive numbers and estimated that Delta was 58-120% more transmissible than Alpha across New England (58% New Hampshire, 68% Massachusetts, 76% Connecticut, 85% Rhode Island, 98% Maine, and 120% Vermont). Finally, using RT-PCR data, we estimated that Delta infections generate on average [~]6 times more viral RNA copies per mL than Alpha infections. Overall, our evidence indicates that Deltas enhanced transmissibility could be attributed to its innate ability to increase infectiousness, but its epidemiological dynamics may vary depending on the underlying immunity and behavior of distinct populations.

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 lineages

Emerging SARS-CoV-2 variants have shaped the second year of the COVID-19 pandemic and the public health discourse around effective control measures. Evaluating the public health threat posed by a new variant is essential for appropriately adapting response efforts when community transmission is detected. However, this assessment requires that a true comparison can be made between the new variant and its predecessors because factors other than the virus genotype may influence spread and transmission. In this study, we develop a framework that integrates genomic surveillance data to estimate the relative effective reproduction number (Rt) of co-circulating lineages. We use Connecticut, a state in the northeastern United States in which the SARS-CoV-2 variants B.1.1.7 and B.1.526 co-circulated in early 2021, as a case study for implementing this framework. We find that the Rt of B.1.1.7 was 6-10% larger than that of B.1.526 in Connecticut in the midst of a COVID-19 vaccination campaign. To assess the generalizability of this framework, we apply it to genomic surveillance data from New York City and observe the same trend. Finally, we use discrete phylogeography to demonstrate that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of B.1.1.7 were larger than those resulting from B.1.526 introductions. Our framework, which uses open-source methods requiring minimal computational resources, may be used to monitor near real-time variant dynamics in a myriad of settings.

Subgenomic RNAs as molecular indicators of asymptomatic SARS-CoV-2 infection

In coronaviridae such as SARS-CoV-2, subgenomic RNAs (sgRNA) are replicative intermediates, therefore, their abundance and structures could infer viral replication activity and severity of host infection. Here, we systematically characterized the sgRNA expression and their structural variation in 81 clinical specimens collected from symptomatic and asymptomatic individuals with a goal of assessing viral genomic signatures of disease severity. We demonstrated the highly coordinated and consistent expression of sgRNAs from individuals with robust infections that results in symptoms, and found their expression is significantly repressed in the asymptomatic infections, indicating that the ratio of sgRNAs to genomic RNA (sgRNA/gRNA) is highly correlated with the severity of the disease. Using long read sequencing technologies to characterize full-length sgRNA structures, we also observed widespread deletions in viral RNAs, and identified unique sets of deletions preferentially found primarily in symptomatic individuals, with many likely to confer changes in SARS-CoV-2 virulence and host responses. Furthermore, based on the sgRNA structures, the frequently occurred structural variants in SARS-CoV-2 genomes serves as a mechanism to further induce SARS-CoV-2 proteome complexity. Taken together, our results show that differential sgRNA expression and structural mutational burden both appear to be correlated with the clinical severity of SARS-CoV-2 infection. Longitudinally monitoring sgRNA expression and structural diversity could further guide treatment responses, testing strategies, and vaccine development.