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

Abstract

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 Delta's 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 Delta's 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.

Figure 1.. SARS-CoV-2 sequencing coverage and variant frequency tracking.

(A) Confirmed cases per 100K population (bars) and percent of cases sequenced (lines) by state (7 day rolling average), January-August 2021. The variability in percent of cases sequenced represents changing sample availability and suitability for sequencing. The drop in percent sequenced at the end of August does not reflect real decreases in sequencing coverage but instead (1) the 1–3 week delays between sample collection and sequence availability and (2) how the data are plotted using 7 day rolling average. (B) Weekly proportion of sequenced genomes belonging to each variant category with 95% confidence intervals, January-August 2021. A breakdown of the number of genomes by state and lineage is included in Tables S1–S3. Complete lists of genomes used from GISAID are available in Data S1–S7.

Figure 2.. Variant logistic growth rates during their respective emergence periods in the context of infections and vaccination.

(A) Estimated infections per 100K population (grey bars, left axis) and percent of the population fully vaccinated (black lines, right axis) (7 day rolling average), with the colored rectangles indicating the 90-day emergence periods for each variant. (B) We ran a binomial logistic regression with 95% confidence intervals for the variant category as the outcome and the number of days since the first detection as the predictor to estimate the logistic growth rate for Alpha versus Delta. Shown as the predicted probability of a given sequence belonging to each variant category over time. The analysis is restricted to the first 90 days of emergence in each state as shown in (A). (C) The regression coefficients (slopes) of the logistic growth rate from (B) with 95% confidence intervals. An initial exploration of the logistic growth rates for Delta compared to Alpha to the vaccination rates or estimated infections per state at the start of the Delta 90-day emergence period can be found in Figure S1.

Figure 3.. Comparison of variant effective reproductive numbers to estimate relative transmissibility.

(A) Estimated effective reproductive number Rt over time for each variant category. We multiplied estimated infections from Covidestim by variant sequencing frequencies over time (each using a 7 day rolling average), and then calculated the variant-specific Rt using EpiEstim (Chitwood et al., 2021; Cori et al., 2013). Note that the y-axis differs from that in (B). The upper limit of Delta’s confidence intervals in Maine and Vermont are not plotted but reach a maximum value of 3.57 and 4.51, respectively. The multiplicative increases in Rt versus the strength of support by variant category and state is shown in Figure S2. (B) Daily ratios of Rt values for Delta compared to Alpha from (A). (C) Daily estimated Rt ratios for Delta compared to Alpha over the period displayed in (B) with the mean and 95% confidence intervals.

Figure 4.. Cross-sectional PCR data from Alpha and Delta samples.

PCR CT values (inverted y-axis) plotted by institute and variant category, limiting Alpha samples to January-March 2021 and Delta samples to June-August 2021 to account for their respective emergence periods. Monthly CT values for Alpha are shown in Figure S3. For each institute, the means of the two variant categories were compared using a t-test, with statistical significance symbols corresponding to the following values: ns (p > 0.05), *** (p ≤ 0.001), and **** (p ≤ 0.0001). Data from the full month of August were not available for most of the institutes at the time of analysis. The ‘Yale University (Connecticut)’ data are from the N1 primer/probe set (originally from the “CDC assay”) of a research use only RT-PCR assay to discriminate among variants (Vogels et al., 2021) (data shown as virus RNA copies/mL in Figure S4). The ‘Jackson Laboratory (Connecticut)’ data are from the N gene primer/probe set of the TaqPath COVID-19 Combo Kit (ThermoFisher). The Mass General Brigham (Massachusetts) data are from the E gene of the Roche Cobas 6800 test (ORF1a data shown in Figure S5). The ‘Health and Environmental Testing Laboratory (Maine)’ data are from the N1 primer/probe set (originally from the “CDC assay”) of the OPTI SARS-CoV-2 RT-PCR Test (OPTI Medical Systems).