22 April, 2025

Contributing authors:

Data analysis, code, and maintenance of this notebook: Justin Jia (current), Carmen Lia Murall, Raphaël Poujol, Susanne Kraemer, Arnaud N’Guessan, Sarah Otto, Art Poon, Jesse Shapiro, Fiona Brinkman, Zohaib Anwar, and Erin Gill. Input and direction by other members of CAMEO, which include: Caroline Colijn, Jorg Fritz, Morgan Langille, Paul Gordon, Julie Hussin, Jeff Joy, and William Hsiao.

Sequence collection, generation, release, and feedback on analyses: Canadian laboratories as part of the CPHLN and CanCOGeN are making these data publicly available and contribute feedback on analyses presented here. A complete list of lab authors is in this repository, and more details are below in the Acknowledgement section.

Citation

If you use the VirusSeq Data Portal or Duotang resource, please cite: Gill et al. (2024) Microbial Genomics. doi:10.1099/mgen.0.001293

SARS-CoV-2 In Canada

Introduction

This notebook was built to explore Canadian SARS-CoV-2 genomic and epidemiological data with the aim of investigating viral evolution and spread. It is developed by the CAMEO team (Computational Analysis, Modelling and Evolutionary Outcomes Group) for sharing with collaborators, including public health labs. These analyses are freely available and open source, enabling code reuse by public health authorities and other researchers for their own use.

Canadian genomic and epidemiological data will be regularly pulled from various public sources (see list below) to keep these analyses up-to-date. Only representations of aggregate data will be posted here.

Important limitations

These analyses represent only a snapshot of SARS-CoV-2 evolution in Canada. Only some infections are detected by PCR testing, only some of those are sent for whole-genome sequencing, and not all sequences are posted to public facing repositories. Furthermore, sequencing volumes and priorities have changed during the pandemic, specific variants or populations might be preferentially sequenced at certain times in certain jurisdictions. When possible, these differences in sampling strategies are mentioned but they are not always known. With the arrival of the Omicron wave, many jurisdictions across Canada reached testing and sequencing capacity mid-late December 2021 and thus switched to targeted testing of priority groups (e.g., hospitalized patients, health care workers, and people in high-risk settings). Currently, most jurisdictions are sequencing mainly hospitalized patients or outbreaks, with little population-level random sampling, underestimating case counts and viral diversity.

Thus, interpretation of these plots and comparisons between health regions should be made with caution, considering that the data may not be fully representative. These analyses are subject to frequent change given new data and updated lineage designations.

The last sample collection date is 22 March, 2025

Current SARS-CoV-2 situation

LP.8.1.1 and XEC are now widely prevalent in most regions. Some recently detected saltations (lineages with a sudden increase in number of mutations) are not spreading well. Some variants are showing notable growth and are being tracked, but currently there is no evidence of a notable new wave occurring.

Note that there has been a reduction in data, potentially causing delays in the detection of new variants and more uncertainty when estimating selection.

Variants of current interest (due to their current/potential growth advantage, mutations of potential functional significance, or spread in other countries):

Plus any other highly divergent variants (“saltation” lineages with a sudden increase in number of mutations) and sublineages with additional combinations of mutations identified through mutation scanning (see some references under List of Useful Tools below).

We thank the global team of those monitoring variants (such as those posting issues here: https://github.com/cov-lineages/pango-designation/issues), and other SARS-CoV-2 genome analysis tool providers (see List of Useful Tools below), which play a key role in identifying new variants of note.

Sublineages in Canada

There are 250 unique named variants currently circulating in Canada since 2024-11-12 (last 120 days). Please see Pango lineage table for number of sequences per lineage present.

Below is an interactive visualization showing frequencies of ciruclating lineages, sub-divided by major sub-lineages, currently circulating in Canada. A table of lineage frequencies can be downloaded by clicking on the (Frequency Table Download) button.

Tips: Click and drag to zoom, double click to reset. Clicking on an item in the legend will hide it, double clicking an item in legend will hide everything else but that item.

Last 120 days

Last 120 days sublineages starting from 2024-11-12 (Frequency Table Download)

BA.1

BA.1 sublineages (Frequency Table Download)

BA.2

BA.2 sublineages (Frequency Table Download)

BA.4

BA.4 sublineages (Frequency Table Download)

BA.5

BA.5 sublineages (Frequency Table Download)

Recombinants

Recombinants sublineages (Frequency Table Download)

## [1] -9

Selection on recent variants

Here we examine the relative rate of spread of the different sublineages of SARS-CoV-2 currently circulating in Canada. Specifically, we determine if a new or emerging lineage has a selective advantage (s), and by how much, against a previously common reference lineage (broad scale (and in the Fastest Growing Lineages section): XEC* and at the fine scale, against XEC; see methods for more details about selection and how it is estimated).

Currently, the major group of SARS-CoV-2 lineages circulating are BA.2.86* variants. At the broad scale, we are now tracking the frequencies of XEC* (as the reference), MC, LP.8 and other BA.2 lineages (mainly BA.2.86 lineages).

Left plot: y-axis is the proportion of these sub-lineages over time. Right plot: y-axis describes the logit function, log(freq(MC/LP.8/Others)/freq(XEC*)), which gives a straight line whose slope is the selection coefficient if selection is constant over time (see methods).

For comparison, Alpha had a selective advantage of s ~ 6%-11% per day over preexisting SARS-CoV-2 lineages, and Delta had a selective advantage of about 10% per day over Alpha.

Caveat: These selection analyses must be interpreted with caution due to the potential for non-representative sampling, lags in reporting, and spatial heterogeneity in prevalence of different sublineages across Canada. Provinces that do not have at least 20 sequences of a lineage during this time frame are not displayed.

Canada

Canada

BC

British Columbia

AB

Alberta

SK

Saskatchawan

MB

Manitoba

ON

Ontario

QC

Quebec

NS

Nova Scotia

NB

New Brunswick

NL

Newfoundland and Labrador

NULL

Fastest growing lineages

Here we show the selection estimates and their 95% confidence intervals for SARS-CoV-2 lineages with more than 10 sequences in present in a region since 2024-11-12, and with enough data to estimate the confidence interval. Each selection estimate measures the growth rate relative to XEC stricto (i.e., sequences designated as XEC and not its descendants). Plots showing the change in variant frequency over time in Canada as a whole are given below for lineages with more than 50 sequences. For Canada-wide plot, a dot with a circle border indicates lineages with a positive selection coefficient in multiple provinces. The most prevelant lineage in the last two weeks is highlighted in grey. A table of the selection estimates is available for download below.

Growth advantage of 0-5% corresponds to doubling times of more than two weeks, with 5-10% reflecting one to two week doubling times and over 10% representing significant growth of less than one week doubling time. Note that estimating selection of sub-variants with low sequence counts (points with less than 100 counts) is prone to error, such as mistaking one-time super spreader events or pulses of sequence data from one region as selection. Estimates with lower sequence counts in one region should be considered as very preliminary.

Plot (stricto)

This plot highlights single lineages that are growing fastest.

Canada

Plot single lineages in Canada *

BC

Plot single lineages in British Columbia

AB

Plot single lineages in Alberta

SK

Plot single lineages in Saskatchawan

MB

Plot single lineages in Manitoba

ON

Plot single lineages in Ontario

QC

Plot single lineages in Quebec

NS

Plot single lineages in Nova Scotia

NB

Plot single lineages in New Brunswick

NL

Plot single lineages in Newfoundland and Labrador

Plot (non stricto)

This plot highlights the groups of related lineages that are growing fastest (e.g., JN.1* is the monophyletic clade that includes JN.1.7 and all other JN.1 sublineages, excluding recombinants.

Canada

Plot single lineages in Canada

BC

Plot single lineages in British Columbia

AB

Plot single lineages in Alberta

SK

Plot single lineages in Saskatchawan

MB

Plot single lineages in Manitoba

ON

Plot single lineages in Ontario

QC

Plot single lineages in Quebec

NS

Plot single lineages in Nova Scotia

NB

Plot single lineages in New Brunswick

NL

Plot single lineages in Newfoundland and Labrador

Table of all the selection estimates

Download Table

Sublineages selection

BA.2 sublineages

Here we show the trends of the various BA.2.* sublineages over time, excluding any recombinants, relative to the frequency of XEC by itself (shown for sublineages with at least 50 (Canada) or 20 (provinces) cases). Proportions shown here are only among XEC (stricto) and the lineage illustrated. Note that these plots are not necessarily representative of trends in each province and that mixing of data from different provinces may lead to shifts in frequency that are not due to selection.

Canada

Canada

Only the three most strongly selected variants are displayed. Click here to see the rest.

BC

British Columbia

Only the three most strongly selected variants are displayed. Click here to see the rest.

AB

Alberta

Only the three most strongly selected variants are displayed. Click here to see the rest.

SK

Saskatchawan

MB

Manitoba

ON

Ontario

Only the three most strongly selected variants are displayed. Click here to see the rest.

QC

Quebec

NS

Nova Scotia

NB

New Brunswick

NL

Newfoundland and Labrador

NULL

VIRUS-MVP: Mutational composition of Omicron

The image below is a screenshot from VIRUS-MVP showing a snapshot of the mutations from lineages actively circulating in Canada. Please click on the link to scan the entire genome and examine the functional impact of the mutations. More details, click on the image below or see https://virusmvp.org/covid-mvp.

covid-mvp

Variants in Canada over time

This plot shows the changing composition of sequences for all Canadian data posted to the VirusSeq Portal according to Pango lineage designation, up to 22 March, 2025. Because sampling and sequencing procedures vary by region and time, this does not necessarily reflect the true composition of SARS-CoV-2 viruses in Canada over time.

Canadian trees

Below is an interactive visualization of a subsampled phylogenetic snapshot of SARS-CoV-2 genomes from Canada. Please see methods for details.

The x-axis of the time tree represents the estimated number of years from today for which the root emerged. The x-axis of the diversity trees shows the number of mutations from the outgroup.

Hovering over a node will display a tool tip with sequence metadata, clicking on a node with the tooltip shown will copy the isolate ID to your clipboard.

### metadata and trees
source("scripts/tree.r")

# load trees from files
mltree <- read.tree(paste0(params$datadir,"/aligned_nonrecombinant_sample1.rtt.nwk"))
ttree <- read.tree(paste0(params$datadir,"/aligned_nonrecombinant_sample1.timetree.nwk"))
recombTTree <- read.tree(paste0(params$datadir,"/aligned_recombinant_X_sample1.timetree.nwk"))
recombMLree <- read.tree(paste0(params$datadir,"/aligned_recombinant_X_sample1.rtt.nwk"))

#stopifnot(all(sort(mltree$tip.label) == sort(ttree$tip.label)))
dateseq <- seq(ymd('2019-12-01'), ymd('2022-12-01'), by='3 month')

# tips are labeled with [fasta name]_[lineage]_[coldate]
# extracting just the first part makes it easier to link to metadata
mltree$tip.label <- reduce.tipnames(mltree$tip.label)
ttree$tip.label <- reduce.tipnames(ttree$tip.label)
recombTTree$tip.label  <- reduce.tipnames(recombTTree$tip.label)


fieldnames<- c("fasta_header_name", "province", "host_gender", "host_age_bin",
               "sample_collected_by", "purpose_of_sampling",
               "lineage", "pango_group","week", "GID")
# extract rows from metadata table that correspond to ttree 
metasub1 <- meta[meta$fasta_header_name%in% ttree$tip.label, fieldnames]
# sort rows to match tip labels in tree
metasub1 <- metasub1[match(ttree$tip.label, metasub1$fasta_header_name), ]


#omi tree metadata
#metasub_omi <- metasub1[grepl("Omicron",metasub1$pango_group ), ]

#recomb tree metadata
mmetasub_recomb <- meta[meta$fasta_header_name%in% recombTTree$tip.label, fieldnames]
mmetasub_recomb <- mmetasub_recomb[match(recombTTree$tip.label, mmetasub_recomb$fasta_header_name), ]

#scale to number of mutations
mltree$edge.length <- mltree$edge.length*29903
mltree <- ladderize(mltree, FALSE)
recombMLree$edge.length <- recombMLree$edge.length*29903
recombMLree <- ladderize(recombMLree, FALSE)

#enforce a non zero branch length so lines can be drawn in javascript
###Time Tree
ttree$edge.length[ttree$edge.length == 0] <- 1e-4
#ttree <- ladderize(ttree, FALSE)
recombTTree$edge.length[recombTTree$edge.length == 0] <- 1e-4
#recombTTree <- ladderize(recombTTree, FALSE)

hab=unique(meta$host_age_bin)
hab=hab[order(hab)]
months=unique(meta$month)
months=as.character(months[order(months)])
weeks=unique(meta$week)
weeks=as.character(weeks[order(weeks)])
presetColors=data.frame(name=c("other",
                               VOCVOI$name,
                               hab,
                               months,
                               weeks), 
                        color=c("#777777",
                                VOCVOI$color,
                                rev(hcl.colors(length(hab)-1, "Berlin")),"#777777",
                                hcl.colors(length(months), "Berlin"),
                                hcl.colors(length(weeks), "Berlin")
                                ))

#suppressWarnings({
#  res <- ace(metasub1$pango.group, ttree2, type="discrete", model="ER")
#})
#idx <- apply(res$lik.anc, 1, which.max)[2:nrow(res$lik.anc)]  # exclude root edge
#anc <- levels(as.factor(metasub1$pango.group))[idx]
source("scripts/tree.r")

timeTreeJsonObj <- DrawTree(ttree, metasub1, "timetree", presetColors, fieldnames=fieldnames)
recombTimeTreeJsonObj <-  DrawTree(recombTTree, mmetasub_recomb, "recombtimetree", presetColors, "lineage", fieldnames= fieldnames)

#diversity ML tree
diversityTreeJsonObj <- DrawTree(mltree, metasub1, "mltree", presetColors, fieldnames=fieldnames)
recombDiversityTreeJsonObj <- DrawTree(recombMLree, mmetasub_recomb, "recombmltree", presetColors, "lineage", fieldnames=fieldnames)
#write(recombDiversityTreeJsonObj, "downloads/test.json")
### omicron diversity tree
#MLtree_omi<-keep.tip(mltree, metasub_omi$fasta_header_name)

#OmicrondiversityTreeJsonObj <- DrawTree(MLtree_omi, metasub_omi, "omimltree", presetColors, fieldnames=fieldnames)

Time Tree

XBB* time tree

Diversity Tree

XBB* Diversity tree

Root-to-tip analyses

The slope of root-to-tip plots over time provide an estimate of the substitution rate. A lineage with a steeper positive slope than average for SARS-CoV-2 is accumulating mutations at a faster pace, while a lineage that exhibits a jump up (a shift in intercept but not slope) has accumulated more than expected numbers of mutations in a transient period of time (similar to what we saw with Alpha when it first appeared in the UK).

Molecular clock estimates (based on three independent subsamples)

Here we show the estimate of the substitution rate for 3 independent subsamples of different variants of interest (VOI), with their 95% confidence interval. The average rate of substitution within VOI is given by a bamboo colored dashed line. For comparison, the average rate of substitution across all samples is much higher (grey line), indicating that about half of the substitutions occur through normal routes (i.e. not chronic infections) of transmission while the other half occur with the appearance of new, highly divergent VOI, likely due to different evolutionary processes occurring within chronic infections (see Neher 2022 for details). BA.2 (red) appears to have a higher rate of mutation because it includes highly divergent sub-groups (i.e., potential saltation lineages), including CM.* and sub-types related to BA.2.86.

Lineages

Here we present a searchable table that provides a short description of each PANGO lineage, it’s immediate ancestor, and the number of sequences in the last 120days.

Appendix

Future development

We are in the process of adding or would like to develop code for some of the following analyses:

  • dN/dS (by variant and by gene/domains)
  • Tajima’s D over time
  • clustering analyses
  • genomically inferred epidemiological parameters: R0, serial interval, etc.

With anonymized data on vaccination status, severity/outcome, reason for sequencing (e.g., outbreak, hospitalization, or general sampling), and setting (workplace, school, daycare, LTC, health institution, other), we could analyze genomic characteristics of the virus relative to the epidemiological and immunological conditions in which it is spreading and evolving. Studies on mutational correlations to superspreading events, vaccination status, or comparisons between variants would allow us to better understand transmission and evolution in these environments.

List of useful tools

A selection of bioinformatics, phylogenetic, and modelling tools that are useful for SARS-CoV-2 analyses:

We look for additional combinations of mutations identified through mutation scanning that are involved in binding or immune evasion. See for example:

  • Greaney, Starr, & Bloom, Virus Evolution, 8:veac021 (2022)
  • Cao et al, Nature, 614:521-529 (2023)
  • Yisimayi et al, bioRxiv, DOI 10.1101/2023.05.01.538516 (2023)
  • Dadonaite et al, bioRxiv, DOI 10.1101/2023.11.13.566961 (2023)
  • Bdeir et al, medRxiv, DOI 10.1101/2024.01.03.23300575 (2024)

Methodology

Genome data and metadata are sourced from the Canadian VirusSeq Data Portal. Pango lineage assignments are generated using the pangoLEARN algorithm. Source code for generating this RMarkdown notebook can be found in [https://github.com/CoVaRR-NET/duotang].

Trees

Phylogenetic trees

Canadian genomes were obtained from the VirusSeq data on the April 22, 2025 and down-sampled to two genomes per lineage, province and month before October 2021, and five genomes per lineage, province and month after October 2021 (about 10,000 genomes in total). We used a Python wrapper of minimap2 (version 2.17) to generate multiple sequence alignments for these genome samples. A maximum likelihood (ML) tree was reconstructed from each alignment using the COVID-19 release of IQ-TREE (version 2.2.0). Outliers were identified in by root-to-tip regression using the R package ape and removed from the dataset. TreeTime was used to reconstruct a time-scaled tree under a strict molecular clock model. The resulting trees were converted into interactive plots with ggfree and r2d3.

Selection

Selection Coefficents

To estimate selection, we used standard likelihood techniques. In brief, sublineages of interest were prespecified (e.g., JN.1) and counts by day tracked over time. If selection were constant over time, the frequency of sub-type \(i\) at time \(t\) would be expected to rise according to \[p_i(t) = \frac{p_i(0) \exp(s_i t)}{\sum_j p_j(0) \exp(s_j t)},\] where \(s_i\) is the selection coefficient favouring sub-type \(i\). A selection coefficient of \(s_i=0.1\) implies that sub-type \(i\) is expected to rise from 10% to 90% frequency in 44 days (in \(4.4./s_i\) days for other values of \(s_i\)).

At any given time \(t\), the probability of observing \(n_i\) sequences of sublineage \(i\) is multinomially distributed, given the total number of sequences from that day and the frequency of each \(p_i(t)\). Consequently, the likelihood of seeing the observed sequence data over all times \(t\) and over all sublineages \(j\) is proportional to \[L = \prod_t \prod_j p_i(t)^{n_i(t)}.\]

The BBMLE package in R was used to maximize the likelihood of the observed data (using the default optimization method, optim). For each selection coefficient, 95% confidence intervals were obtained by profile likelihood (using uniroot).

Graphs illustrating the rise in frequency of a variant over time are shown (left panels), with the area of each dot proportional to the number of sequences. 95% confidence bands were obtained by randomly drawing 10,000 sets of parameters (\(p_i\) and \(s_i\) for each sub-type) using RandomFromHessianOrMCMC, assuming a multi-normal distribution around the maximum likelihood point (estimated from the Hessian matrix, Pawitan 2001). At each point in time, the 2.5%-97.5% range of values for \(p_i(t)\) are then shown in the confidence bands.

Logit plots (right panels) show \[ln(\frac{p_i(t)}{p_{ref}(t)})\] relative to a given reference genotype (here BA.1), which gives a line whose slope is the strength of selection \(s_i\). Changes in slope indicate changes in selection on a variant (e.g., see Otto et al.).

These estimates of selection ignore heterogeneity within provinces and may be biased by the arrival of travel-related cases while frequencies are very low. Sampling strategies that oversample clustered cases (e.g., sequencing outbreaks) will introduce additional variation beyond the multinomial expectation, but these should lead to one-time shifts in frequency rather than trends over time. Provinces with sampling strategies that are variant specific are removed, unless explicit information about the variant frequencies is available.

Rates

Root-to-tip estimates of substitution rate

Substitution rates were obtained from the maximum likelihood tree made using IQ-TREE and a root-to-tip regression conducted, without forcing the intercept to zero (similar results were seen when forcing the intercept). Up to 10000 samples for non-XBB lineages and all samples for XBB lineages are used to construct this tree. For the estimation of each VOI’s substitution rates over time, all sequences of that VOI present in the tree are used. While this ignores pseudo-replication among the samples due to relatedness, the estimated slope is robust given the large sample sizes. Furthermore, we calculated SE bars from three different independent samples to reduce the influence of closely related viral samples. The global rate estimate was obtained by a regression over time of all the sequences present in the tree, as illustrated in the root-to-tip plot, ignoring variants classification.

Data notes by province

All analyses draw on the most recent publicly available viral sequence data on ViralSeq and should be interpreted with caution due to lags in reporting and sequencing priorities that can differ across provinces or territories. Note that the NCCID provides a timeline of Canadian events related to each variant: https://nccid.ca/covid-19-variants/.

As of 13 July 2024, the number of detected cases is obtained from the “Respiratory virus detection data” (PHAC; link).

Historical notes

From the beginning of the pandemic to the fall of 2021, Canadian sequences were mostly of the wildtype lineages (pre-VOCs). By the beginning of summer 2021, the VOCs Alpha and Gamma were the most sequenced lineages overall in Canada. The Delta wave grew during the summer of 2021 with sublineages AY.25 and AY.27 constituting sizeable proportions of this wave. Omicron arrived in November of 2021 and spread in three main waves, first BA.1* (early 2022), then BA.2* (spring 2022), then BA.5* (summer 2022). Current, multiple sublineages of Omicron persist, with emerging sublineages spreading, such as BQ.1.1 (a BA.5 sub-lineage).

There are two Pango lineages that have a Canadian origin and that predominately spread within Canada (with some exportations internationally): B.1.438.1 and B.1.1.176. Other lineages of historical interest in Canada:

This historical analysis is not being further updated, as we focus on more interactive data plots and the “Current situation” text above.

Session info

The version numbers of all packages in the current environment as well as information about the R install is reported below.

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sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Fedora Linux 37 (Server Edition)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libflexiblas.so.3.3
## 
## locale:
##  [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
##  [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
##  [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] stats4    grid      splines   parallel  stats     graphics  grDevices
##  [8] utils     datasets  methods   base     
## 
## other attached packages:
##  [1] HelpersMG_5.8   Matrix_1.5-1    coda_0.19-4     rlang_1.0.6    
##  [5] MASS_7.3-58.1   bbmle_1.0.25    plotly_4.10.1   DT_0.27        
##  [9] reshape2_1.4.4  forcats_1.0.0   stringr_1.5.0   dplyr_1.1.0    
## [13] purrr_1.0.1     readr_2.1.3     tibble_3.1.8    tidyverse_1.3.2
## [17] jsonlite_1.8.4  r2d3_0.2.6      ggfree_0.1.0    ape_5.6-2      
## [21] ggplot2_3.4.0   lubridate_1.9.1 knitr_1.42      tidyr_1.3.0    
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-160        fs_1.6.0            bit64_4.0.5        
##  [4] httr_1.4.4          numDeriv_2016.8-1.1 tippy_0.1.0        
##  [7] tools_4.2.2         backports_1.4.1     bslib_0.4.2        
## [10] utf8_1.2.2          R6_2.5.1            DBI_1.1.3          
## [13] lazyeval_0.2.2      colorspace_2.1-0    withr_2.5.0        
## [16] tidyselect_1.2.0    bit_4.0.5           compiler_4.2.2     
## [19] cli_3.6.0           rvest_1.0.3         xml2_1.3.3         
## [22] labeling_0.4.2      sass_0.4.5          scales_1.2.1       
## [25] mvtnorm_1.1-3       digest_0.6.31       rmarkdown_2.20     
## [28] pkgconfig_2.0.3     htmltools_0.5.4     highr_0.10         
## [31] dbplyr_2.3.0        fastmap_1.1.0       htmlwidgets_1.6.1  
## [34] readxl_1.4.1        rstudioapi_0.14     farver_2.1.1       
## [37] jquerylib_0.1.4     generics_0.1.3      crosstalk_1.2.0    
## [40] vroom_1.6.1         googlesheets4_1.0.1 magrittr_2.0.3     
## [43] Rcpp_1.0.10         munsell_0.5.0       fansi_1.0.4        
## [46] lifecycle_1.0.3     stringi_1.7.12      yaml_2.3.7         
## [49] plyr_1.8.8          bdsmatrix_1.3-6     crayon_1.5.2       
## [52] lattice_0.20-45     haven_2.5.1         hms_1.1.2          
## [55] pillar_1.8.1        reprex_2.0.2        glue_1.6.2         
## [58] evaluate_0.20       data.table_1.14.6   modelr_0.1.10      
## [61] vctrs_0.5.2         tzdb_0.3.0          cellranger_1.1.0   
## [64] gtable_0.3.1        assertthat_0.2.1    cachem_1.0.6       
## [67] xfun_0.36           broom_1.0.3         googledrive_2.0.0  
## [70] viridisLite_0.4.1   gargle_1.3.0        timechange_0.2.0   
## [73] ellipsis_0.3.2

Acknowledgements

We thank all the authors, developers, and contributors to the VirusSeq database for making their SARS-CoV-2 sequences publicly available. We especially thank the Canadian Public Health Laboratory Network, academic sequencing partners, diagnostic hospital labs, and other sequencing partners for the provision of the Canadian sequence data used in this work. Genome sequencing in Canada was supported by a Genome Canada grant to the Canadian COVID-19 Genomic Network (CanCOGeN).

We gratefully acknowledge all the Authors, the Originating laboratories responsible for obtaining the specimens, and the Submitting laboratories for generating the genetic sequence and metadata and sharing via the VirusSeq database, on which this research is based.

  • The Canadian VirusSeq Data Portal (https://virusseq-dataportal.ca) We wish to acknowledge the following organisations/laboratories for contributing data to the Portal: Canadian Public Health Laboratory Network (CPHLN), CanCOGGeN VirusSeq, Saskatchewan - Roy Romanow Provincial Laboratory (RRPL), Nova Scotia Health Authority, Alberta Precision Labs (APL), Queen’s University / Kingston Health Sciences Centre, National Microbiology Laboratory (NML), Institut National de Sante Publique du Quebec (INSPQ), BCCDC Public Health Laboratory, Public Health Ontario (PHO), Newfoundland and Labrador - Eastern Health, Unity Health Toronto, Ontario Institute for Cancer Research (OICR), Provincial Public Health Laboratory Network of Nova Scotia, Centre Hospitalier Universitaire Georges L. Dumont - New Brunswick, and Manitoba Cadham Provincial Laboratory. Please see the complete list of laboratories included in this repository.

  • Public Health Agency of Canada (PHAC) / National Microbiology Laboratory (NML) - (https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html)

  • Various provincial public health websites (e.g. INSPQ https://www.inspq.qc.ca/covid-19/donnees/)

  • Canadian Institutes of Health Research (CIHR) - Coronavirus Variants Rapid Response Network (CoVaRR-Net;https://covarrnet.ca/)