key: cord-0299599-0y4yj4qd
authors: Blaiszik, B.; Graziani, C.; Olds, J. L.; Foster, I.
title: The Delta Variant Had Negligible Impact on COVID-19 Vaccine Effectiveness in the USA
date: 2021-09-22
journal: nan
DOI: 10.1101/2021.09.18.21263783
sha: 613a1015cc808671bfef1a910b30a9a9476fc27c
doc_id: 299599
cord_uid: 0y4yj4qd

The SARS-CoV-2 Delta variant (B.1.617.2) was initially identified in India in December 2020. Due to its high transmissibility, its prevalence in the U.S.A. grew from a near-zero baseline in early May 2021 to nearly 100% by late August 2021, according to CDC tracking. We accessed openly available data sources from the public health authorities of seven U.S. states, five U.S. counties, and the District of Columbia on RT-PCR COVID-19 tests split by the COVID-19 vaccination status of individuals tested during this period. Together, these time series enable estimation and tracking of COVID-19 vaccine effectiveness (VE*) (against RT-PCR diagnosed infection) concurrently with the growth of Delta variant prevalence in those locations. Our analyses reveal that in each locality the VE* for the combined set of all three US vaccines remained relatively stable and quite well-performing, despite the dramatic concurrent rise of Delta variant prevalence. We conclude that the Delta variant does not significantly evade vaccine-induced immunity. The variations in our measured VE* appear to be driven by demographic factors affecting the composition of the vaccinated cohorts, particularly as pertains to age distribution. We report that the measured VE*, aggregated across the collected sites, began at a value of about 0.9 in mid-May, declined to about 0.76 by mid-July, and recovered to about 0.9 by mid-September.

In the first quarter of 2020, SARS-CoV-2, a novel beta coronavirus, disrupted global society by triggering a pandemic that, within a month of onset, locked down activity worldwide. That pandemic continues to the present (September 2021). The virus emulates its ancestor, SARS-CoV, by eliciting a high-affinity binding event between its spike envelope protein (S) and the human angiotensin converting enzyme II (ACE2) that allows it to gain entry into host epithelial cells in the host respiratory system and subsequently into other organs [1] . In contrast to SARS-CoV, SARS-CoV-2 is far more transmissible between its human hosts because its S protein binds to ACE2 with a much higher affinity [2] . The resultant disease, COVID-19, produces in patients a constellation of symptoms that vary widely but can result in hospitalization or death. JNJ-78436735 or Ad26.COV2.S) [13, 14] . To date, these vaccines have been administered to approximately 51% of the US population [15] . All three vaccines have proven to be effective in protecting fully vaccinated individuals from serious clinical complications of COVID-19.

At the beginning of the pandemic, it was believed that the virus mutation rate was too slow to be a concern [16] , but this view has since been altered by the emergence of new variant strains, aided by some fitness advantage gained by mutation. In general, the molecular substrate for these fitness advantages is in the binding interface between the viral S and human ACE2 [17] .

Among the variant strains, a few are considered of greater public health concern because of mutations that produce greater transmissibility, more severe disease, or decreased neutralization by human immune response, including by vaccine-induced immunity. In all such cases, viral load is a common factor affecting clinical outcomes [6] .

Four such "variants of concern" have emerged to-date: the B.1.1.7 lineage (also called 20I/501Y.V1 or variant of concern [VOC] 202012/01), identified in the U.K. in December 2020 [18] ; the B.1.351 lineage (20H/501Y.V2), first identified in the Republic of South Africa in December 2020 [19] ; the P.1 lineage (20J/501Y.V3), identified in December 2020 in travelers from Brazil [20] ; and the B.1.617.2 (Delta) variant identified in India in April 2021 [21] .

In the current work, we address the concerns about infections reported in vaccinated individuals, colloquially described as "breakthrough infections" [22] , in the context of the recent dominance of the Delta variant. The authors of [22] note that "Despite the high level of vaccine efficacy, a small percentage of fully vaccinated persons (i.e., those who have received all recommended doses of an FDA-emergency or fully authorized COVID-19 vaccine) will develop symptomatic or asymptomatic infections with SARS-CoV-2, the virus that causes COVID-19."

That is, breakthrough infections are expected from exposure to any strain of the virus, simply due to the fact that the protective efficacy and effectiveness of the vaccines is not 100%. This is a result of the immunological heterogeneity in phenotype described above, which leads to some individuals being more weakly stimulated by vaccines than others, and thus more likely to become infected upon exposure despite vaccination. Thus, some individuals are more weakly stimulated by vaccines than others, and more likely to become infected upon exposure despite vaccination. Hence, the real question about breakthrough infections with respect to Delta variant is not whether it causes them, but rather "How many more breakthrough infections per infectious exposure occur in consequence of the advent of the Delta variant than would have occurred otherwise?".

We have found that in the U.S.A., the data required to explore the relationship between the Delta variant and vaccine effectiveness exist and are public, although they are not easy to find and are not collected in one place by one authority. The required data are aggregated reverse transcription-polymerase chain reaction (RT-PCR) test data, broken out by subject vaccination status, and reported at a regular cadence: preferably daily, and no less frequently than weekly. A few U.S. county or state health department aggregate their test data in this way. Thus, we have attempted to capture time series of data reported from the reporting sites we have uncovered.

The sites are heterogeneous, both geographically and with respect to vaccination coverage.

These sources are the public health departments of seven U.S. states, five U.S. counties, and the District of Columbia (Washington D.C.). Their details are listed in Table 1 .

With such time-series of test data, it is possible to estimate a measure of vaccine effectiveness (VE * ). This is a measure of vaccine performance that is distinct from vaccine efficacy (VE). VE is measured in the course of controlled clinical trials [24] of nearly identical treatment and placebo groups, and is therefore as entirely a clinical property of the vaccine itself as the trial design can attain. VE * , on the other hand, is measured in the real world using unequal, potentially poorly-controlled groups of vaccinated and unvaccinated populations which evolve 5 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ; Table 1 : Web resources on COVID vaccine breakthrough infections in the U.S.A. considered in this study. Collection methods: (MC) manual collection, (AC) automated collection, (S) image snapshot. N is the number of data samples. All dates are in 2021. All data sources are provided and described in the publicly available GitHub repository [23] .

Collection It is hard to discern a consistent pattern in these studies. Undoubtedly there are demographic, social, and environmental effects that confound simple comparison and interpretation.

One observation that could be made is that such studies lack spatial and temporal resolution, and that the ability to follow vaccine effectiveness at a more fine-grained level enables one to shed more light on the question of whether or not the Delta variant reduces vaccine effectiveness. We believe this to be the main accomplishment of this work. We will return to this point in the Discussion, below. 7 for use under a CC0 license.

This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. We now describe the data and methods by which we have reached our conclusions.

The RT-PCR COVID-19 testing data, broken out by vaccination status, are available from the public sources described in Table 1 , although in many cases it is unfortunately necessary to screen-scrape the data from "dashboards" which do not offer the underlying data either as downloadable files or through an API or even from raw image files.

The result is a dataset of locations and date ranges, in which vaccinated and unvaccinated groups of tested individuals, of differing sizes, yield test-positive rates, from which VE * may be calculated. We consider only positive test rates in the present analysis, so that our VE * measures effectiveness against infection, as opposed to against severe disease or hospitalization.

We have also downloaded the U.S. SARS-CoV-2 prevalence data from the Covariants project [32] , which we use to display the rise of the Delta variant relative to others over the period studied. We also aggregated vaccination data from CovidActNow [33] to aid our analyses.

Finally, we have downloaded and transformed data from the CDC on daily vaccination demographics by state [34] , in order to attempt to interpret some of our results.

For the purpose of estimating local VE * , we adapt the simplified Bayesian method for the analysis of vaccine efficacy described in [35] . From the point of view of the method, the distinction between (on the one hand) the treatment and placebo group of a clinical trial, and (on the other hand) differently-sized vaccinated and unvaccinated populations at a place and time is immaterial-the result is a Bayesian posterior density over a risk-reduction parameter, an efficacy in the case of a clinical trial, an effectiveness in the case of uncontrolled case data.

The advantage of the procedure is that one may straightforwardly obtain uncertainty bounds-8 for use under a CC0 license.

This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ;  Bayesian credible regions-on the VE * estimates thus obtained; these uncertainty bounds help give a sense of what spatial and temporal variations in VE * may or may not be interpreted as mere statistical fluctuations.

To review and adapt the main result of [35] : suppose that the ratio of vaccinated population fraction to the unvaccinated population fraction is r; that the number of positive tests among the vaccinated population is N V ; and that the number of positive tests among the unvaccinated population is N U . We assume, for simplicity, a uniform prior distribution density π(VE * ) = 1. Finally, we also assume for simplicity a model in which there is no selection bias that preferentially tests either population more than the other. Then the posterior probability density

where κ is a normalization constant.

One may easily show that if N V = 0 then the posterior in Equation (1) has a maximum at VE * ≡ 1 − N V /r N U , as expected. In addition, the function in Equation (1) may be subjected to a numerical quadrature from which Bayesian credible intervals about the peak VE * may be obtained. In what follows, whenever we display an error bar for a VE * in a plot, it corresponds to a 90% Bayesian credible region.

Equation (1) can also be used to jointly fit the data from many localities and epochs, in order to obtain a weighted average VE * over space and time. This can be done by multiplying together the individual posterior densities for each observation, to obtain a "global" posterior density from which a peak and an uncertainty may be extracted. An example of this is shown below, in Figure 1 . While a technically-valid procedure, its result should only be interpreted as a convenient weighted average of sorts. The model that this operation represents is one in which all the data really have the same VE * , and the scatter is actually due to statistical noise. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 

It is worth discussing the interpretation of a "time-series of VE * ." As we will see below, our estimates of VE * show evidence of both geographic and temporal variation, with the latter noticeable on timescales as short as a week in some cases. One may well wonder what meaning is to be ascribed to a measure of infection risk reduction that has these kinds of properties.

It is helpful to cast the problem in slightly more formal terms. We can represent VE * in terms of probabilities as

where the symbol i represents the proposition "an individual is infected," e represents "the individual was exposed to the virus," V represents "the individual belongs to the vaccinated cohort,"

and V represents the complementary proposition "the individual belongs to the unvaccinated cohort."

This representation is potentially misleading because of the fraught meanings of V and V , which carry with them not only vaccination status, but also demographic information concerning the composition of the respective cohorts. That is, the two cohorts are represented by distributions in geography, age, health, race, and many other characteristics. Suppose that these demographic distributions are parametrizable, and that the distributional parameters are denoted by θ V , θ V for the vaccinated and unvaccinated cohort, respectively. Then we have a more precise notation to express participation in a particular cohort, to wit

for use under a CC0 license.

This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ; where v and v express the non-demographic proposition "this individual is (resp., is not) vaccinated". Now V means "this individual (a) is vaccinated, and (b) was drawn from a population with a demographic distribution characterized by the parameters θ V ," and similarly for V . With this interpretation of V and V , the representation of VE * in Equation (2) is no longer potentially misleading.

It is now possible to understand how VE * might be variable in time: it is because the cohort distribution parameters, θ V (t) and θ V (t) are subject to temporal variation. For example, early in the COVID-19 vaccination campaign (i.e., in the early months of 2021) the U.S. followed guidance from the Advisory Committee on Immunization Practices (ACIP), which rec-11 for use under a CC0 license.

This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ; https://doi.org/10.1101/2021.09.18.21263783 doi: medRxiv preprint ommended a phased introduction, with early vaccination phases prioritizing the elderly [36] .

This necessarily produced a demographic high age skew in θ V early on, which declined rapidly in the Spring of 2021, as younger populations began to receive vaccines.

To make the time-dependence evident, we may re-write Equation (2) as follows:

Note that while time-dependent behavior of a risk-reduction factor such as VE * may seem odd or disquieting, its root cause is the probabilistic conditioning on the demographic information in V , V , which is usually the same information that epidemiologists have at their disposal and must condition their risk-reduction calculations on. This is a feature, not a bug, of VE * , because in epidemiology, vaccines protect populations, not individuals, and it is demographic information that necessarily conditions questions of risk assessment.

A global view of all the VE * fits from all localities at all epochs considered is shown in Figure 1 . This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. In fact, Figure 1 shows a curious feature that is shared by nearly all locations, which is a slight drop in VE * between early and mid-July, followed by a recovery of VE * by early September. Since the vaccinated and unvaccinated populations are not controlled in these studies, the behavior in these time series almost certainly reflects demographic shifts in the constitution of the two groups. We will examine one such shift-average age of the vaccinated group-below.

In Figure 2 , we show time-resolved joint fits of VE * over all localities when using sevenday (left) and 14-day (right) data bins. We se again the mid-summer dip, although with an anomalous high point around 15 July. Comparison with Figure 1 suggests that this anomaly is due to the inhomogeneity of the distribution, with the VE * due to San Diego county strongly "dissenting" from VE * due to the rest of the U.S.A. The remaining data in Figure 1 appears to move together more harmoniously, suggesting that the average behavior displayed in Figure 2 is robust and informative of a national coherence in vaccine demographic trends. By both the 13 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. seven-day and 14-day bin time series, measured VE * started out at a value of about 0.9 in mid-May, declined slightly to about 0.76 by mid-July, only to recover to a value of about 0.9 by early September.

Local Behavior: VE * Versus Delta were happening concurrently in every region. It is evident from these figures that to the extent that the Delta variant had an impact on VE * , that impact was of minor importance. Had the impact of Delta on VE * been a dominant, or even an important, effect, the data would show a monotonic decrease in VE * corresponding to and correlated with the monotonic increase of Delta prevalence. Instead the data shows VE * fluctuations, some rising, some falling, some even oscillating, and these are presumably driven by demographic and environmental factors affecting vaccination statistics, and which are clearly overwhelming any effect on VE * that may be due to the rapid ascent of Delta prevalence. Figure 4 displays the same plots but for state-level data for the seven U.S. states for which we were able to obtain the necessary data. The data from the states tells the same story as the county-level data: there is no trend of systematically dropping VE * such as would be expected if the Delta variant were capable of strongly evading vaccine-stimulated immune response. We conclude from these data that there is no evidence that the Delta variant escapes immunity from the COVID-19 vaccines in use in the U.S.A., and that there is therefore no evidence that 14 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ; Figure 3 : In Washington DC, and five counties for which data are available, vaccine effectiveness was quite stable and high, while Delta prevalence grew from essentially zero to nearly 100% in three months. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ; Figure 4 : In U.S. states, there are fewer available data, but the behavior is similar to that of the counties.

for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ; 

The modulation of the VE * time series in Figures 1-4 is a noteworthy feature of the data. We spent some effort speculating about its origin, and investigating possible other data correlations that might explain it. Given the relatively fragmented and unsystematic state of present epidemic surveillance data infrastructure, it is not easy to investigate many possibilities. We noticed that a CDC dataset [34] makes it possible to crudely bin the vaccinated population into bins of ages 12-18, ages 18-65, and ages 65+. From these age bins, it is possible to construct a rough-and-ready measure of mean age of the vaccinated population. Since the dataset contains daily statistics by state, it is possible to view, on a state-by-state basis, the average age of the vaccinated cohort in the U.S.A., albeit according to a rather crude measure of "average age." This is an example of a time-dependent demographic distributional parameter in the set θ V (t),

for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ;  https://doi.org/10.1101/2021.09.18.21263783 doi: medRxiv preprint discussed above in the subsection of the Introduction on interpretation of time-series of VE * .

The time series of average age of the vaccinated cohort for the U.S. states that concern the present study are displayed in Figure 5 . We see that in every state studied, the mean age of the vaccinated cohort dropped significantly over the summer of 2021, in many cases by as much as four years over three months. During this time, younger and, on average, presumably healthier people left the unvaccinated cohort and entered the vaccinated cohort, which one would expect, in principle, to raise VE * . Possibly this effect may have had something to do with the "recovery" of VE * following the July 15 minimum. However, the root cause of the minimum and subsequent recovery is still a puzzle. Such changes in VE * may be consistent with changes in the vaccinated and unvaccinated cohort demographics, changes in social behavior, or environmental or seasonal effects, each of which is worth independent analysis and investigation. The clarity of the outlook afforded by the analysis presented here stands in contrast to the murky picture regarding the effect of Delta on vaccine-induced immunity to COVID-19 that emerged from the literature [25, 26, 27, 28, 29, 30, 31] reviewed in the Introduction. We suggest that the difference is that those analyses were necessarily coarse-grained, representing a snapshot (or a comparison of two snapshots) in time, and usually aggregating data into a single geographic unit, even in the case of widely-scattered studies such as [27, 29] . In contrast, and particularly in light of the interpretation of VE * time-series discussed above, the present study builds on a geographically-and temporally-resolved view of VE * , interpreted as an actionable risk-reduction factor, with error bars, and explicitly probabilistically conditioned on local, realtime vaccine demographics.

The power of this type of analysis is due in part to the simplicity of the data necessary to drive it (COVID-19 RT-PCR data, separated by vaccination status); in part to the informativeness of the time-series of VE * that are output (as illustrated by what could be learned by comparing them to the time-series of Delta prevalence); and in part to the fact that VE * , as estimated here, is precisely the demographic risk-reduction estimate that is of interest from an epidemiological point of view: an epidemiologist would want to know, for example, "how much better was the vaccinated population of DuPage County, IL, protected against COVID-19 than their unvaccinated peers on September 1 2021?" This kind of question is what our VE * estimates can answer.

Given these observations on the method presented in this article, we see great potential for transforming at least that aspect of epidemic surveillance that pertains to monitoring the effectiveness of COVID-19 vaccines in the face of the inevitable emergence of future new strains of SARS-CoV-2, both in the U.S.A and internationally. What is required for this to happen is some reform in the data-management and publication practices of public health authorities with respect to the availability of COVID-19 RT-PCR test data sorted by vaccination status.

In the first place, it would clearly be beneficial if all public health organizations that aggregate COVID-19 test data would gather vaccination status with each test, if they do not do so already, and publish their test-positive statistics broken out by vaccination status. Additionally, it would be helpful if testing volume data, i.e., the total number of tests performed for each site, were also broken out by vaccination status. This would permit checking (and generalizing) the model assumption that both the vaccinated and unvaccinated groups are tested at equal rates, which at the moment is unvalidated in our method.

Our experience in gathering such data suggests several opportunities for public health departments to improve their data reporting. First, data should be made available in open machinereadable formats, e.g., comma separated value (CSV) files, embedded Javascript object notation (JSON), or via programmatic interfaces such as REST APIs [37] to facilitate automated collection. Second, the data should be made available in formats and structures that are common with those used by other such departments. (Open source software can facilitate such sharing of approaches.) These first two improvements could be achieved, for example, by making vaccine effectiveness data available via simple export from a dashboard as a CSV, via download of the underlying raw data files (e.g., as done by Utah), or by embedding JSON documents in the HTML using a format such as the Schema.org Dataset type [38] used for Google Datasets.

Third, it may be important to create a central registry of such data that would enable researchers to easily discover the existence of such resources, and would enable searching, querying, and browsing by the geography, data types, and more. By realizing each of these improvements, the future collection and utilization of these critical data by researchers would become much simpler.

We note several potential sources of bias in the datasets analyzed in this work that may limit the effectiveness of the methodology described here. 1) We have observed retrospective data revisions in the collected data streams. These revisions change the calculated values, and neces-20 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ;  sitate recurring data collection and analysis methods. 2) The data streams that we have collected may not be fully representative of the nation as a whole. We found the datasets analyzed in this study by performing Google searches with key phrases (e.g. "Utah by vaccination status") and by monitoring and searching Twitter for mention of locations reporting cases or population risk by vaccination status. This methodology was inherently ad hoc, and has assuredly missed some data sources. To address this, a future data infrastructure should allow for simple addition of both new data streams and the scrapers needed to collect their data from their source. 3) Many of the data analyzed in this work were made available through the web in formats that are not conducive to automated extraction of the key values (e.g., as images, within natural language text, or via dashboards without export functionality). The processes used to collect such data (e.g., transcribing values by hand) are prone to human error. 4) Data presented on public websites are often available only temporarily, necessitating a snapshot, and precluding retrospective analyses (even using the Internet Archive, which caches website HTML and some images, but not dashboard content). These and other issues would be remedied if data were made available in machine-readable formats and if versioning was applied to the data files for each data stream.

for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 22, 2021. ;  

Phylogenetic Analysis and Structural Modeling of SARS-CoV-2 Spike Protein Reveals an Evolutionary Distinct and Proteolytically Sensitive Activation Loop

Evolutionary and structural analyses of SARS-CoV-2 D614G spike protein mutation now documented worldwide

COVID-19 and the human innate immune system

A single-cell atlas of the peripheral immune response in patients with severe COVID-19

Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity

SARS-CoV-2 viral load is associated with increased disease severity and mortality

Engineered ACE2 receptor traps potently neutralize SARS-CoV-2

Ethics and informatics in the age of COVID-19: Challenges and recommendations for public health organization and public policy

Moderna COVID-19 Vaccine VRBPAC Briefing Document

Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine

COVID-19 Vaccine VRBPAC Briefing Document

Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine

COVID-19) Vaccine VRBPAC Briefing Document

Safety and efficacy of single-dose Ad26.COV2.S vaccine against Covid-19

COVID-19 Vaccinations

Temporal signal and the phylodynamic threshold of SARS-CoV-2

Structural basis of receptor recognition by SARS-CoV-2

Preliminary genomic characterisation of an emergent SARS-CoV-2 lineage in the UK defined by a novel set of spike mutations

Emergence and rapid spread of a new severe acute respiratory syndromerelated coronavirus 2 (SARS-CoV-2) lineage with multiple spike mutations in South Africa

Genomic characterisation of an emergent SARS-CoV-2 lineage in Manaus: Preliminary findings

Neutralization of variant under investigation B.1.617 with sera of BBV152 vaccines

COVID-19 Vaccine Breakthrough Infections Reported to CDC-United States

A Collection of Web Resources Providing Information on COVID Vaccine Breakthrough Infections in the United States

Infectious Disease Epidemiology: Theory and Practice

Effectiveness of COVID-19 Vaccines against the B.1.617.2 (Delta) Variant

SARS-CoV-2 Delta VOC in Scotland: Demographics, risk of hospital admission, and vaccine effectiveness

Effectiveness of COVID-19 vaccines against variants of concern, Canada

BNT162b2 and mRNA-1273 COVID-19 vaccine effectiveness against the Delta (B. 1.617. 2) variant in Qatar

Effectiveness of COVID-19 vaccines in preventing SARS-CoV-2 infection among frontline workers before and during B.1.617.2 (Delta) variant predominance-Eight US locations

Comparison of two highly-effective mRNA vaccines for COVID-19 during periods of Alpha and Delta variant prevalence

Real-World Effectiveness of the mRNA-1273 Vaccine Against COVID-19: Interim Results from a Prospective Observational Cohort Study

CoVariants: SARS-CoV-2 Mutations and Variants of Interest

COVID-19 Vaccinations in the United States, Jurisdiction. https : / / data . cdc . gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-Jurisdi/unsk-b7fc

A Simplified Bayesian Analysis Method for Vaccine Efficacy

The Advisory Committee on Immunization Practices' updated interim recommendation for allocation of COVID-19 vaccine-United States

Big Data and Social Science: A Practical Guide to Methods and Tools

This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.

All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_ disclosure.pdf and declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have