Genomic epidemiology of the first two waves of SARS-CoV-2 in Canada

  1. Angela McLaughlin  Is a corresponding author
  2. Vincent Montoya
  3. Rachel L Miller
  4. Gideon J Mordecai
  5. Canadian COVID-19 Genomics Network (CanCOGen) Consortium
  6. Michael Worobey
  7. Art FY Poon
  8. Jeffrey B Joy  Is a corresponding author
  1. British Columbia Centre for Excellence in HIV/AIDS, Canada
  2. Bioinformatics, University of British Columbia, Canada
  3. Department of Medicine, University of British Columbia, Canada
  4. Department of Ecology and Evolution, University of Arizona, United States
  5. Department of Pathology and Laboratory Medicine, Western University, Canada

Abstract

Tracking the emergence and spread of SARS-CoV-2 lineages using phylogenetics has proven critical to inform the timing and stringency of COVID-19 public health interventions. We investigated the effectiveness of international travel restrictions at reducing SARS-CoV-2 importations and transmission in Canada in the first two waves of 2020 and early 2021. Maximum likelihood phylogenetic trees were used to infer viruses’ geographic origins, enabling identification of 2263 (95% confidence interval: 2159–2366) introductions, including 680 (658–703) Canadian sublineages, which are international introductions resulting in sampled Canadian descendants, and 1582 (1501–1663) singletons, introductions with no sampled descendants. Of the sublineages seeded during the first wave, 49% (46–52%) originated from the USA and were primarily introduced into Quebec (39%) and Ontario (36%), while in the second wave, the USA was still the predominant source (43%), alongside a larger contribution from India (16%) and the UK (7%). Following implementation of restrictions on the entry of foreign nationals on 21 March 2020, importations declined from 58.5 (50.4–66.5) sublineages per week to 10.3-fold (8.3–15.0) lower within 4 weeks. Despite the drastic reduction in viral importations following travel restrictions, newly seeded sublineages in summer and fall 2020 contributed to the persistence of COVID-19 cases in the second wave, highlighting the importance of sustained interventions to reduce transmission. Importations rebounded further in November, bringing newly emergent variants of concern (VOCs). By the end of February 2021, there had been an estimated 30 (19–41) B.1.1.7 sublineages imported into Canada, which increasingly displaced previously circulating sublineages by the end of the second wave.Although viral importations are nearly inevitable when global prevalence is high, with fewer importations there are fewer opportunities for novel variants to spark outbreaks or outcompete previously circulating lineages.

Editor's evaluation

This study provides important observations about the transmission of SARS-CoV-2 lineages within Canada and the importation of lineages into Canada over the first year of the COVID-19 pandemic. This information is critical for understanding SARS-CoV-2 evolution and epidemiology, including the potential impacts of travel restrictions.

https://doi.org/10.7554/eLife.73896.sa0

Introduction

The COVID-19 pandemic has highlighted the importance of genomic epidemiology in deciphering the origin and spread of SARS-CoV-2 lineages across local and global scales to aid in directing responses (Deng et al., 2020; Geoghegan et al., 2020; Gonzalez-Reiche et al., 2020; du Plessis et al., 2021; Worobey et al., 2020). Studying evolutionary relationships between SARS-CoV-2 sequences over time and space allows estimation of the relative contributions of international and domestic transmission in association with public health interventions. Thus, phylogenetics can be highly informative in evaluating the effectiveness of non-pharmaceutical interventions at curbing importations and reducing transmission.

Phylogenetic analyses are dependent upon timely generation and sharing of publicly available genetic sequences and associated metadata, such as through the Global Initiative on Sharing All Influenza Data (GISAID) platform (Khare et al., 2021; Shu and McCauley, 2017). The global availability of SARS-CoV-2 genomes has been unprecedented, such that by 15 June 2021, just over a year and a half since the first whole genome sequence was shared (de Maio, 2020; Wu et al., 2020), there were 495,159 SARS-CoV-2 sequences on GISAID representing 108,301,802 COVID-19 diagnoses (Krispin and Byrnes, 2020). Nomenclature systems to partition viral sequences sharing common mutations and recent ancestry, such as Pango lineages, have provided a dynamic system for genomic SARS-CoV-2 surveillance (O’Toole et al., 2021; Rambaut et al., 2020). Tracking the emergence, dispersal, and genomic characteristics of lineages, particularly variants of concern (VOCs) and interest (VOIs), has become critical in light of their demonstrated increased transmissibility, potential increased clinical severity, and ability to circumvent host immune responses (Lopez Bernal et al., 2021; Faria et al., 2021; Meng et al., 2021; Planas et al., 2021; Tegally et al., 2020). Therefore, identifying and characterizing predominantly circulating lineages and sublineages is a cornerstone of global epidemiological surveillance and policy.

Phylogeographic methods to infer sampled viruses’ dispersal have been widely applied to quantify SARS-CoV-2 introductions into the UK (du Plessis et al., 2021), the USA (Deng et al., 2020; Gonzalez-Reiche et al., 2020; Worobey et al., 2020; Zeller et al., 2021), Brazil (Candido et al., 2020), New Zealand (NZ) (Douglas et al., 2021; Geoghegan et al., 2020), and Europe (Hodcroft et al., 2021; Worobey et al., 2020), among others, elucidating variable epidemic dynamics. A recent review expounds further on the approaches and application of phylodynamic models towards improving our understanding of SARS-CoV-2 transmission and control (Attwood et al., 2022). In the UK, where the most sequences have been generated per case globally (Furuse, 2021), there were an estimated 1179 introductions resulting in two or more sampled descendant cases in the early epidemic (du Plessis et al., 2021). By contrast, the first wave of COVID-19 in Louisiana, USA, was primarily attributable to a single domestic introduction several weeks prior to Mardi Gras, resulting in multiple superspreader events and wide dissemination across Southern USA (Zeller et al., 2021). In NZ, where stringent border closures and lockdown measures were enacted early on, there were estimated to have been 277 introductions up to 1 July 2020, among which 19% resulted in multiple downstream cases (Geoghegan et al., 2020). Large-scale SARS-CoV-2 genomic epidemiology analyses in Canada have thus far been limited to a study on the early epidemic in the province of Quebec in which they conservatively estimated at least 500 viral introductions into Quebec by June 2020 largely attributable to the province’s spring break (Murall et al., 2021). Our analyses elaborate upon their findings at a national scale for the first and second waves of COVID-19 in Canada.

Characterization of viral importations over time can also clarify the effectiveness of public health interventions by associating inflection points in importations with drastic changes in policies such as international travel restrictions (Magalis et al., 2020). Compartmental modelling approaches have also been applied to quantify the impact of COVID-19 control measures such as social distancing, informing decisions about when to relax or increase stringency (Anderson et al., 2020; Anderson et al., 2021). Evaluating the effectiveness of these interventions is key to reacting proportionally to the risk posed by future outbreaks of SARS-CoV-2 and other zoonotic pathogens. It is unclear how changes in intervention stringency, social and travel behaviour, and circulating viral diversity affected Canadian SARS-CoV-2 transmission dynamics, particularly during a time of negligibly low immunity prior to vaccine roll-out or widespread natural infections.

The first COVID-19 case in Canada was detected on 25 January 2020 in a traveller from Wuhan to Toronto and by 5 March, the first community transmission case was identified (Press, 2021; Figure 1C). In subsequent weeks, the stringency of Canadian interventions increased rapidly, summarized by the Oxford stringency index (Hale et al., 2021). On 14 March, a travel advisory warning against all non-essential travel outside Canada was issued; on 18 March, travel restrictions on the entry of all foreign nationals (except from the USA) were implemented; 21 March travel restrictions were extended to the USA; and 24 March, a mandatory 14-day self-isolation for those returning from international travel was implemented. The stringency index during the first wave increased most rapidly on 16 March 2020, reaching the highest stringency between 1 and 3 April 2020. The first wave reached a maximum of 1108 average daily new COVID-19 diagnoses across Canada on 3 May 2020. While many of the earliest cases in Canada were attributable to A.1 and B.4 lineages in British Columbia (Figure 1—figure supplement 1), cases in March through June 2020 were primarily attributable to B.1* lineages and B.1.1* lineages in Ontario and Quebec, as well as lineage B.1.279 in Alberta in March and April. By June, in response to reductions in daily new diagnoses country-wide, restrictions slowly relaxed, resulting in a reduction of the stringency index, albeit with varying rates by province (Cameron-Blake et al., 2021). Fewer than 186 average daily new diagnoses occurred across Canada throughout summer 2020, mostly in Quebec, Ontario, Alberta, and British Columbia. Then, with the rising second wave in fall 2020, the stringency index rebounded in November 2020, although with simultaneous easing of entry exceptions for foreign nationals and quarantine shortening (Cameron-Blake et al., 2021). The second wave entered exponential growth in November 2020, as VOCs and VOIs began to displace wild-type lineages.

Figure 1 with 2 supplements see all
A timeline of the first and second waves of the Canadian COVID-19 epidemic up to 1 March 2021.

(A) Rolling average daily new COVID-19 diagnoses in previous 7 days across Canadian provinces and territories, or the daily count where data was sparse prior to April 2020. (B) Rolling average daily clean SARS-CoV-2 sequences collected in previous 7 days, or daily sequences where data was sparse, uploaded to Global Initiative on Sharing All Influenza Data (GISAID) by 15 June 2021. Incomplete sample collection dates were inferred within time-scaled phylogenies. (C) The Oxford Stringency Index for Canada overlaid with key epidemiologic events and national-level public health restrictions. Figure 1—figure supplement 1 summarizes the frequencies of Pango lineages among daily Canadian sequences. Figure 1—figure supplement 2 compares monthly cases, sequences available, and sequences sampled globally and within Canada.

The VOCs and VOIs first detected in Canada were B.1.1.7 (Alpha) on 8 November 2020, followed by P.2 on 24 November, A.23.1 on 2 December, B.1.429 on 5 December, B.1.525 (Eta) on 14 December, B.1.351 (Beta) on 19 December, B.1.427 (Epsilon) on 30 December (dates from GISAID). The winter holidays were followed by the crest of the second wave on 11 January 2021 with 3555 average daily new diagnoses in Canada, likely dampened by restrictions on travel, personal gatherings, dining, and mask use. The new year also brought first Canadian detections of B.1.526.1/.2 (Iota) on 1 January 2021, P.1 (Gamma) on 1 February, and C.37 (Lambda) on 15 February. By March 2021, the trough of the second wave gave heed to the third wave, driven almost exclusively by B.1.1.7 and P.1, the latter in Western provinces primarily. Monitoring lineage frequencies alone does not inform how many individual importations accounted for the detected cases, nor the domestic spread dynamics of individual sublineages, warranting a genomic epidemiology analysis.

A phylogenetic maximum likelihood approach was applied to estimate the timing, origins, destinations, and transmission of SARS-CoV-2 introductions in Canada from the beginning of 2020 to the end of the second wave in March 2021 for Canadian sublineages (introductions with sampled descendants) and singletons (introductions with no sampled descendants). We tested the hypothesis that international travel restrictions enacted in March 2020 effectively reduced international importations of SARS-CoV-2 into Canada, yet ongoing introductions contributed to COVID-19 persistence into early 2021, exacerbated by highly transmissible B.1.1.7 and other VOC sublineages. These analyses help to elucidate the relative contributions made by sublineages introduced before and after enactment of travel restrictions towards persistence of the Canadian SARS-CoV-2 epidemic in 2020 and early 2021, prior to the global predominance of VOCs and vaccine roll-out. Evaluating travel restrictions’ effectiveness towards reducing importations could inform the stringency and timing of future public health interventions.

Results

Global SARS-CoV-2 phylogeny with a Canadian focus

The overrepresentation of sequences from countries with the highest sequencing efforts, such as the UK, was reduced in the dataset by subsampling (Figure 1—figure supplement 2; Figure 6—figure supplement 2). Cumulative sequence representation was increasingly comparable across provinces with fewer Canadian sequences subsampled, however excluding too many sequences resulted in missed introductions. There were no sequences available from the Yukon, Northwest Territories, Nunavut, or Prince Edward Island, which cumulatively had 603 COVID-19 cases (0.07% of Canada cases) prior to 1 March 2021.

Canadian viral genomes were dispersed throughout the global phylogeny and represented 345 unique Pango lineages (Figure 2). The average time to the most recent common ancestor (TMRCA) was estimated as 22 December 2019 (24 November–23 December) across 10 subsamples, consistent with the upper end of others’ credibility intervals (Lu et al., 2020; Worobey et al., 2020). The relaxed molecular clock model estimated in LSD2 had a mean rate of 2.86 × 10–4 (2.84 × 10–4–2.92 × 10–4) substitutions/site/year (s/s/y), lower than the mean strict clock rate inferred in TempEst of 7.15 × 10–4 (6.83 × 10–4–7.48 × 10–4) s/s/y (Figure 2—figure supplement 2) due to differing assumptions of rate variation. Increasing the representation of global samples relative to Canadian sequences generally resulted in earlier TMRCA estimates, higher estimated clock rates (sampling wider diversity), more temporal outliers removed, and lower R2 values associated with their clock models (Supplementary file 3c; Supplementary file 3d). The large majority of sublineages (75.1%) were supported by high likelihoods (>0.9) in both introduction and parental nodes across all bootstraps (Figure 2—figure supplement 3), consistent across subsampling strategies. The 14 sublineages with 500 or more Canadian descendants cumulatively accounted for 59.1% of Canadian sublineage descendants, despite representing only 2.3% of sublineages (Figure 2).

Figure 2 with 3 supplements see all
Key Canadian sublineages in a phylogenetic tree of SARS-CoV-2 in Canada and globally up to the end of the second wave on 28 February 2021.

(A) The highest likelihood bootstrap time-scaled phylogenetic tree inferred using a subsampling strategy where 75% of available Canadian sequences were retained and the remainder of sequences up to 50,000 were from global sources. (B) The timing and expansion of key Canadian SARS-CoV-2 sublineages with more than 500 sampled Canadian descendants. Height reflects the relative density of sampled Canadian descendants within each sublineage. Diamonds and dashed lines show the mean and 95% confidence interval of the time to the most recent common ancestor (TMRCA). Figure 2—figure supplement 1 zooms in on the subtrees for the four largest Canadian sublineages. Figure 2—figure supplement 2 summarizes the process of removing temporal outliers to improve molecular clock signal. Figure 2—figure supplement 3 summarizes sublineage introduction node and parent node likelihoods overlaid for all bootstraps.

Diverse origins of Canadian SARS-CoV-2 sublineages

Maximum likelihood discrete ancestral state reconstruction of the time-scaled phylogenies with 75% of Canadian sequences yielded a mean of 680 (658–703) Canadian sublineages (introductions with sampled descendants) by the end of February 2021 (Figure 3) and 1582 (1501–1663) singletons (introductions with no sampled descendants) (Figure 3—figure supplement 2). Although singletons accounted for 70% (69–71%) of introductions, some were likely representative of sublineages with no other sampled descendants. The proportion of importations resulting in domestic transmission did not vary significantly by province, origin, or month (Kruskal-Wallis test: p=0.53, p=0.47, p=0.37), but notably reached a maximum of 0.94 in February 2020 (Figure 6—figure supplement 3).

Figure 3 with 5 supplements see all
Flow of SARS-CoV-2 sublineages into Canadian provinces from global origins coloured by Pango lineage in (A) the first wave, before 1 August 2020, and (B) the second wave, from 1 August 2020 to 28 February 2021.

Locations’ relative size and lineage flows between location pairs represent the mean percent of sublineages across 10 subsamples for the 75% sampling strategy. Lineages by location pair associated with greater than 0.5% of sublineages and locations associated with more than 1% of sublineages were labelled. Sublineages’ relative sizes, based on the number of sampled unique Canadian descendants during the (C) first and (D) second wave. Canadian sublineages were named based on the predominant Pango lineage of descendants and a ‘can’ suffix with a numeric denoting their order of first Canadian sample date. Figure 3—figure supplement 1 shows the flow of singletons. Figure 3—figure supplement 2 summarizes the distribution of sublineage sizes. Figure 3—figure supplement 3 shows the distribution of all Canadian sublineages’ descendants’ sampling locations. Figure 3—figure supplement 4 stratifies second wave sublineage sizes by pre- and post-January 2021. Figure 3—figure supplement 5 stratifies sublineages’ overall size by global and Canadian descendants.

We estimated that during the first wave, from 1 January to 31 July 2020, there were a total of 384 (368–400) introductions resulting in domestic transmission, including 169 (155–182) B.1 sublineages, 53 (50–56) B.1.1 sublineages, 23 (20–25) A.1 sublineages, 9 (8–10) B.4 sublineages, 8 (7–9) B.1.36 sublineages, and 7 (6–7) B.40 sublineages (Figure 3A). Most first wave Canadian sublineages originated from the USA (49%, 46–52%), followed by Russia (12%, 11–13%), Italy (6%, 5–9%), India (6%, 5–7%), Spain (5%, 4–5%), and the UK (4%, 4–5%), among others. These sublineages were inferred to have been primarily imported into Quebec (39%, 38–40%) followed by Ontario (36%, 35–38%), and British Columbia (12%, 11–13%), followed by Manitoba (5%, 5–5%), the Maritimes (5%, 5–5%), Alberta (2%, 2–2%), and Saskatchewan (1%, 0–1%). The second wave, from 1 August 2020 to 28 February 2021, included 296 (280–313) newly introduced sublineages, primarily of B.1.2 (55, 54–56), B.1.1.7 (30, 19–40), B.1.36 (24, 22-25), B.1 (14, 12–15), and B.1.1.519 (8, 8–9) lineages (Figure 3B). The origins of sublineages introduced during the second wave remained predominantly from the USA (43%, 40–45%), alongside increased relative contributions from India (16, 15–17%), the UK (7%, 5–9%), Asia (6%, 5–6%), Europe (4%, 4–5%), and Africa (3%, 3–4%). There were relatively more sublineages introduced to Ontario (45%, 44–47%), British Columbia (23%, 22–24%), and Alberta (11%, 10–11%) during the second wave, and fewer to Quebec (16%, 16–17%) than in the first wave. Relative contributions of origins and destinations were mostly robust to subsampling strategy, although differed in their absolute estimates (Figure 6—figure supplement 4).

Sublineage sizes were overdispersed (Figure 3—figure supplement 2) with a mean of 53 (47–59) total unique sampled descendants (globally and within Canada) and a median of 4 (4–4). There were 46 (44–49) sublineages with more than 100 unique sampled descendants. Together, they represented 6.8% (6.4–7.2%) of sublineages and 0.7% (0.6–0.8%) of total introductions including singletons, yet their Canadian descendants accounted for 77% (74–81%) of Canadian sequences. Only 14 sublineages had 500 or more sampled Canadian descendants (Figure 2). The largest sublineages from the first wave, including B.1.279.can1, B.1.can1, B.1.128.can1, and B.1.1.176.can1, persisted through to the second wave (Figure 2—figure supplement 1), although their descendants no longer accounted for the majority of Canadian samples (Figure 3D). In the second wave, the most frequently sampled sublineages in Canada were AE.8.can1 (introduced in July 2020), B.1.36.18.can1 (July 2020), B.1.1.7.can1 (November 2020), and B.1.438.1.can1 (September 2020). As the second wave progressed into the new year, increasingly VOC sublineages, B.1.1.7.can2 and B.1.1.7.can3, outcompeted previously dominant sublineages, B.1.36.18.can1, B.1.128.can1, and AE.8.can1 (Figure 3—figure supplement 4). A total of 34,169 (31,337–37,001) sampled descendants of Canadian sublineages were identified (Figure 3—figure supplement 3). While the majority of descendants were in Canada (n=25,270, 25,071–26,369), the 8449 (5915–10,983) global descendants were vastly underestimated by subsampling.

Travel restrictions reduced sublineage importations

Sublineages’ TMRCA approximates the date of the first transmission event resulting in one or more sampled descendant following viral introduction. Although TMRCA is not equivalent to the date of a viral host crossing a border, they are associated by a sampling lag and possibly one or more unsampled generations.

By tracking sublineages’ TMRCA over time, the temporal dynamics of SARS-CoV-2 importations were modelled by global origin and Canadian destination (Figure 4). There may have been upwards of 14 (7–21) sublineages introduced prior to the first COVID-19 case in Canada identified on 25 January 2020, and 233 (218–248) sublineages prior to the implementation of travel restrictions for all foreign nationals on 21 March 2020. However, early sublineage introduction dates have wide uncertainty due to low phylogenetic diversity and large polytomies. On the day following imposition of travel restrictions (22 April 2020), the importation rate reached its maximum of 58.5 (50.4–66.5) sublineages per week, including 31.8 (27.7–35.9) sublineages per week originating from the USA and 31.2 (28.7–33.7) sublineages per week introduced into Quebec. Ontario had reached its maximum several days before on 19 March, culminating at 17.7 (15.0–20.5) sublineages per week. Over this period, many Canadians were repatriated from around the world, yet the mandatory 14-day at-home quarantine was not enacted until 25 March. By 5 April, 2 weeks after travel restrictions took effect, the overall sublineage importation rate had dropped 3.4-fold (3.2–3.8); and within 4 weeks, the rate had dropped 10.3-fold (8.3–15.0). This inferred inflection point in sublineage importation rate was robust to multiple subsampling strategies (Figure 6—figure supplement 5). The extent to which importations were reduced within 2 weeks of heightened stringency varied by province; importations were reduced by 9.9-fold (1.3–18.6) in the Maritimes, 5.3-fold (4.3–6.2) in Quebec, 2.2-fold (1.7–2.7) in Ontario, 1.6-fold (0.8–2.3) in British Columbia, and 0.94-fold (0.2–1.7) in Alberta. Importation dynamics for singletons mostly mirrored sublineage trends (Figure 4—figure supplement 1; Figure 3—figure supplement 1), although the USA contributed relatively more towards singletons than sublineages in both waves, and the maximum singleton importation rate was higher in the first wave than the second wave.

Figure 4 with 2 supplements see all
Weekly introduction rates of Canadian SARS-CoV-2 sublineages in the first two waves, in the context of changes in COVID-19 intervention stringency.

The weekly sublineage introduction rates were summarized as 7-day rolling means across bootstraps (A) by global origin and (B) by province of introduction. The background shading corresponds to periods of high and low stringency, based on Oxford COVID-19 Stringency Index. Figure 4—figure supplement 1 summarizes weekly singleton introduction rates. Figure 4—figure supplement 2 characterizes changes in sublineages’ size, detection lag, and lifespan over time.

Despite reductions, introductions were maintained at a low level (1.0–12.5 sublineages per week) until 1 August 2020, when a small spike in importations was detected leading into the second wave. Upon further relaxation of travel restrictions for incoming international students and Canadians’ family members in mid-October 2020 (Canadian Institute for Health Information, 2021), coinciding with a sustained low stringency, importation rates rebounded to 17.5 (13.5–21.5) sublineages per week on 2 November; then, with increased travel prior to the holidays, rates increased to (10.5–18.9) sublineages per week on 3 December. Prior to the new year, numerous VOC and VOI sublineages had been introduced to Canada, including 15 (10–21) B.1.1.7, 5 (4–5) B.1.351, 2 (1–2) B.1.429, 5 (4–5) P.2, and 4 (4-5) A.23.1 sublineages.

Sublineage size, lifespan, and detection lag over time

First, to elucidate the relative contributions of early and late sublineages, sublineage size (number of sampled descendants) over time was evaluated (Figure 4—figure supplement 2A). Sublineages were stratified by whether they were active (had any sampled Canadian cases in past 2 months before 1 March 2021) or extinct, to account for differences in sublineages’ time to accrue and sample cases. Sublineage size was significantly reduced over time among active and extinct sublineages (both p<0.001). Concurrently, sublineages’ transmission lifespan (days from TMRCA to most recent Canadian sample) decreased over time for both active and extinct sublineages (both p<0.001, Figure 4—figure supplement 2B).

Under a null hypothesis where both earlier and later sublineages were equally likely to be transmitted and sampled, we would expect the mean number of days since importation to be steady over time. While the average number of days since importation of Canadian samples (days from sublineage TMRCA to sample date) steadily increased over time during the first wave, it dipped several times in summer 2020 as new sublineages became more widespread, leveling off the age since importation in the second wave (Figure 4—figure supplement 2C). Average sublineage detection lag (days from TMRCA to first Canadian sample) did not change significantly over time (p=0.62; Figure 4—figure supplement 2D). There was insufficient evidence that detection lag was significantly associated with province of introduction (Kruskal-Wallis test, p-value = 0.23).

International, domestic, and provincial transmission sources

The inferred transmission source (most likely state at internal node directly preceding tip) of all sampled Canadian genomes was investigated to highlight the relative roles of domestic and international transmission by province over time. Transmission sources were categorized as within-province, between-province, the USA, or other international sources. Following the entry restriction for foreign nationals in mid-March, there was a reduction, but not an elimination, of the proportion of transmission events attributable to the USA or other international sources across all provinces (Figure 5A). Prior to the restrictions, provinces had a mean of 26% of transmission events with any international origins, ranging from 17% (14–20%) in Saskatchewan to 33% (31–35%) in British Columbia. In April 2020, following travel restrictions, all provinces had decreased proportions of international transmission sources, yet Ontario and Quebec retained relatively high mean number of transmission events attributable to international sources, at 115 (105–125) and 95 (82–109) (Figure 5—figure supplement 1). This may suggest slower implementation or compliance with quarantine guidelines in these provinces.

Figure 5 with 1 supplement see all
Relative contributions of international and domestic transmission sources.

(A) Proportional and total contributions of the USA, other international, between-province, and within-province transmission sources among all sampled tips by province and month between in March, April, and May 2020. (B) The proportional flow of lineages transmitted between-provinces among Canadian tips, stratified before and after August 2020. Values reflect the mean across bootstraps for the 75% subsampling strategy. Figure 5—figure supplement 1 depicts the total number of sampled transmission events with any international source across Canada in April 2020.

Between-province transmission was investigated by comparing transmission origins among Canadian sequences with an inferred Canadian origin from another province (Figure 5B). During the first wave, Ontario and Quebec were the greatest sampled sources of interprovincial transmission, with B.1, B.1.1, and B.1.350 accounting for the largest cumulative flows. In the second wave after August 2020, Ontario remained the greatest sources of interprovincial importations, followed by nearly equal contributions from British Columbia, Alberta, and Quebec. Dominant lineages transmitted interprovincially in the second wave were B.1.438.1 from Alberta to British Columbia, B.1.1.157 and B.1.1.121 from Ontario to Quebec, AE.8 from British Columbia to Quebec and Ontario to Quebec, and B.1.1.7 from Ontario to Quebec.

Subsampling sensitivity analysis

To render phylogenetic inferences computationally feasible, reduce overrepresentation of geographies with more sequences per case, and evaluate uncertainty in the effect of sampling on importation rates, sequences were subsampled proportionally to either provinces or countries’ contributions to monthly case counts in Canada and globally for multiple bootstraps. We compared four subsampling strategies where 25% (n=9184), 50% (n=18,368), 75% (n=27,552), or 100% (n=36,736) of Canadian sequences were retained and global sequences were sampled up to 50,000 total. Each subsampling strategy was repeated for 10 bootstraps with replacement.

While the 75% strategy identified the most sublineages, it also had the widest 95% confidence interval surrounding the number of sublineages of all the strategies (Figure 6). The median 95% confidence interval widths for number of sublineages for each location pair was comparable across strategies, and each strategy included several large widths for high estimates. Some variation is expected within a strategy across bootstraps as the sampling process is stochastic, that is, countries were sampled probabilistically, rather than proportionally. The 75% strategy also identified the most singletons, followed by the 50%, 100%, and 25% strategies. When too few Canadian sequences were included, sublineages and singletons went undetected more often; however, when too few global sequences were included as a result of high inclusion of Canadian sequences (under soft computational limit of 50,000 sequences), migration events went undetected due to underrepresented global genetic diversity. Therefore, to maximize our ability to identify domestically circulating sublineages, the 75% strategy was reported in the main figures.

Figure 6 with 5 supplements see all
Subsampling sensitivity analysis, with 25–100% of Canadian sequences retained.

(A) The estimated number of sublineages across 10 bootstraps. (B) The distribution of 95% confidence interval widths for number of sublineages attributable to each location pair across 10 bootstraps. (C) The estimated number of singletons identified across 10 bootstraps for each subsampling strategy. (D) The distribution of 95% confidence interval widths for number of singletons attributable to each location pair across 10 bootstraps. (E) The estimated total number of importations, that is, the sum of sublineages and singletons, and (F) the proportion of sublineages among all importations. Subsampling strategies are further compared in regard to the relationship between sequences and cases by strategy in Figure 6—figure supplement 1; subsampled sequence densities in Figure 6—figure supplement 2; mean proportion of importations resulting in a sublineage by month and region in Figure 6—figure supplement 3; relative sublineage contributions in Figure 6—figure supplement 4; and finally sublineage introduction rates in Figure 6—figure supplement 5.

As the percent of Canadian sequences retained increased and the number of global sequences sampled each month decreased, there was decreased overrepresentation of sequences with strong sampling efforts including the UK and USA (Figure 6—figure supplement 2). The 75% Canadian sequences subsampled strategy achieved the highest Pearson’s correlation coefficient between total cases and sequences for global regions (0.96), with a mean of 0.075 global sequences per 100 cases (Figure 6—figure supplement 1). Among Canadian provinces within the 75% strategy, the correlation coefficient was 0.87 with 4.5 sequences per 100 cases on average, which was surpassed by the strategies with fewer sequences retained. With fewer Canadian sequences sampled, provinces’ contributions of sequences over time were more normalized. For all subsampling strategies, China and the USA had higher than average representation on account of their contributions during early pandemic months when sequences were sparse and all included in the analysis to best reconstruct early lineage divergence. Iran and Saskatchewan had consistently lower than average sequence representation.

The relative ranking of the mean number of sublineages global regions and Canadian provinces were mostly robust to subsampling strategy, although there were subtle differences, particularly for regions associated with fewer sublineages (Figure 6—figure supplement 4). Notably, with only 25% of Canadian sequences retained, the estimated percent of sublineages introduced to British Columbia in the second wave was less than Quebec, whereas in the 75% and 100% strategies, British Columbia accounted for more sublineages than Quebec, partially as a result of less normalization of sequence representation. Spatiotemporal trends in sublineage importation rates were comparable between sampling strategies, although the amplitude of importations during the first wave varied, with the 75% subsampling strategy displaying the largest first wave crest, attributable primarily to the USA (Figure 6—figure supplement 5). The second wave amplitude and trends were relatively similar between strategies. The drastic reduction of importation rates following the implementation of travel restrictions in late March was robust across strategies, with comparable inflection points. Across all strategies, there were significant reductions of sublineage size and lifespan over time.

Discussion

Together, these analyses support the conclusion that travel restrictions and other non-pharmaceutical interventions imposed in March 2020 drastically reduced importations, preventing the expansion of subsequent sublineages. A more rapid and stringent public health response would have reduced the initial burden by preventing early sublineages from establishing widespread transmission chains that persisted throughout the first and second waves. However, wild-type (pre-VOC) sublineages introduced in summer 2020 when prevalence and immunity were low contributed the highest proportion of COVID-19 cases in the second wave, suggesting that even a low level of ongoing importations of similarly transmissible variants can contribute to viral persistence. Over time, improvements in quarantine, contact tracing, testing, and individual behavioural changes collectively contributed to smaller sublineages with shorter transmission lifespans, prior to the seeding of highly transmissible VOC sublineages. A moderate level of border porosity throughout 2020 and 2021 provided opportunities for importations of B.1.1.7 and other VOCs that outcompeted previously circulating sublineages to near extinction in the third wave.

Although the USA was highly represented in all of our subsamples as a result of its large contribution to COVID-19 cases in 2020 and high sequence availability during early months, our results suggest a greater effect than due to sampling alone. On average, the USA sequences represented 28.9% (28.7–29.2%) of total international sequences, yet accounted for 46.3% (44.0–48.7%) of all sublineages and 57.7% (55.6–59.8%) of singletons. Upon maximizing the number of Canadian sequences in the analysis, where global sequence representation was more normalized but less comprehensive, the USA sequences represented fewer of the international sequences (25.8%, 25.6–26.1%) and still accounted for 38.4% (37.0–39.8%) of sublineages and 46.4% (44.6–48.3%) of singletons. It was foreseeable that the USA would be the largest source of SARS-CoV-2 importations into Canada given its high COVID-19 prevalence throughout 2020 as well as the fact that these nations share the world’s longest land border, spanning 8890 km. Overall international arrivals into Canada declined 77.8% from 96.8 M in 2019 to 19.7 M in 2020 (Canada Statistics, 2021a). However, the number of truck drivers and crew members (air, ship, and train), only declined by 24.8%, accounting for 49% of all international arrivals after April 2020 (of whom 93% were truck drivers). Truck drivers supported the supply chain throughout COVID-19 disruptions, yet in doing so, may have inadvertently facilitated additional SARS-CoV-2 importations from the USA. International arrivals of Canadians and non-residents in 2020 were low, but not negligible, with 14.6 M and 5.1 M arrivals respectively in 2020 (Canada Statistics, 2021a). While essential workers were exempt from quarantine, all others were mandated a self-enforced 14-day quarantine upon re-entry. It was not until 22 February 2021 that a 3-day hotel quarantine was made mandatory for international arrivals by air (Government of Canada, 2021). The downstream effects of border porosity could have been better mitigated by earlier widespread availability of rapid antigen tests to inform quarantine duration (Centers for Disease Control and Prevention, 2020), and through increased resources for contact tracing, cluster response, and compliance oversight. While the majority of importations were inferred to have been into Quebec and Ontario, this could be partially attributable to differences in sequence generation and deposition. Based on provincial population sizes alone, one would expect more importations into Ontario and Quebec (populations of 14.8 M and 8.6 M) than into British Columbia (5.2 M), Alberta (4.5 M), or other provinces and territories with less than 1.5 M (Canada Statistics, 2021b).

During the first wave, early sublineages had the opportunity to become established and resulted in large transmission chains driving COVID-19 burden, consistent with findings from the UK (du Plessis et al., 2021). However, the second wave was mostly driven by cases from newly seeded sublineages. We accounted for potential confounding due to recent sublineages having been monitored for less time by stratifying sublineages by whether they were active or extinct in the previous 2 months; and by restricting the data to collection dates 3 months prior to the data download, we accounted for delay in depositing sequence data for more recent sublineages. Differences remained between early and late sublineages’ evolutionary dynamics in the presence of VOCs with heightened transmissibility and the roll-out of vaccines. The earliest introductions were typified by high sublineage lifespans (i.e. duration of transmission), which manifested as an initial increase in the mean time since importation, corroborating that outbreak control via sublineage detection and contract tracing improved over the course of the first wave. Sublineage TMRCAs typically lag importation by several days, but could precede them if multiple infections with subsequently sampled descendants occurred during travel. While accounting for sampling lag between symptoms and sample collection would get closer to the importation date, this assumes symptom onset occurred during travel, which might not always be the case. In contrast, if we assume that infected travellers who sparked domestic transmission chains were tested upon or soon after arrival, then this lag would be zero to a couple of days. If an infectious traveller was not sampled, but gave rise to an outbreak that was later sampled, then an additional lag must be considered regarding the length and number generations of unsampled viral predecessors since importation. A TMRCA could precede importation if the sublineage index case infected multiple people during travel. Where travel history is available, incorporating internal nodes or branch states for travel location would help to inform the timing as well as geographic origins of importations (Hong et al., 2021). In light of these additional sources of uncertainty, we abstained from applying an importation lag to TMRCAs, and emphasize their interpretation as the estimated time of the recent common ancestor of all sampled descendants.

Case burden in the second wave was primarily due to sublineages introduced in the summer of 2020 amid low prevalence, despite a low number of importations. This suggests that ongoing seeding events impacted viral persistence prior to the arrival of VOCs with increased transmissibility. Considering the global and temporal diversity of the SARS-CoV-2 pandemic, it is difficult to predict what variant may be introduced and subsequently disperse through a local population. While broad and longstanding restrictions against non-essential international travel is not necessarily an advisable policy in light of economic impacts, swift and stringent travel bans towards locations harbouring a high frequency of a VOC not yet identified domestically should be seriously considered to reduce the probability of seeding multiple, simultaneous outbreaks and potentially overwhelming the healthcare system. Dynamic travel bans can be detrimental from a socioeconomic perspective; therefore their implementation must be weighed relative to other non-pharmaceutical interventions to reduce transmission. As illustrated by Omicron, perhaps pervasive spread is inevitable with the most transmissible and immune evasive variants. However, reducing the number of importations sparking domestic outbreaks grants jurisdictions more time to prepare for the inevitable by ramping up vaccinations, scaling up testing and contact tracing, allocating primary care resources, and implementing non-pharmaceutical interventions. These advanced planning steps also serve to minimize the economic harm due to work absenteeism and supply chain disruption.

Ancestral trait reconstruction is sensitive to sampling bias as ancestral node states are more likely to reflect states predominant among tips (Baele et al., 2017; Hill et al., 2021; Lemey et al., 2009). By subsampling sequences proportionally to monthly relative case counts, a prior is assumed towards the probability of importation from each geography. While subsampling proportionally to cases reduces representation of geographies with the highest sequence contributions, it does not directly increase representation of geographies with sparse sequences, which may have relatively underestimated relative migration rates. Furthermore, countries and provinces differed in the extent of their contact tracing, testing, and case reporting, which adds uncertainty in the ascertainment rate underlying observed cases. Hospitalizations or COVID-related deaths could be used to impute or deconvolute the true number of people infected (Huisman et al., 2021), but this requires strong assumptions about the hospitalization and case fatality rate, both functions of the virulence of viral variants and prior immunological exposure of the population. In the present analysis, we make the simplifying assumption that case detection rates are comparable across geographies, justifying the use of case contributions to inform the subsampling process, however we recognize that additional data clarifying reason for testing could improve these estimates. The subsampling sensitivity analysis revealed how subsampling iteratively reduced overrepresentation in available provincial sequences and improves the correlation between cases and sequences. Across subsampling strategies, the relative spatiotemporal trends were robust, although exact estimates were variable, particularly for smaller values.

Incorporating individuals’ travel history or flight volume data could help to improve representation of undersampled geographies (Lemey et al., 2020; du Plessis et al., 2021). However, Bayesian phylogeography remains limited in its scalability despite improved efficiencies in the memory required (Didelot et al., 2018; du Plessis et al., 2021), and they are still limited to thousands, rather than tens or hundreds of thousand sequences. Therefore, the maximum likelihood approach that we have applied offers scalability, while taking into consideration the uncertainty of sampling bias by including multiple bootstraps and subsampling strategies. Stratified analyses of well-supported (sub)lineages in future analyses will facilitate direct comparisons of Bayesian and maximum likelihood phylogeography methodologies towards estimating importation rates.

Low sequence representation can lead to underestimates of total introductions if neither index case nor descendants were sampled, underestimates of sublineage size if not all descendants were sampled, and similarly, overestimates of the proportion of singletons, which may have been from unsampled transmission chains. Extrapolating an upper estimate of introductions is challenging in the absence of additional data. Clean genomes available in Canada prior to 1 March 2021 represented 4.2% of confirmed diagnoses (and 3.2% when 75% of Canadian sequences retained). Diagnoses were estimated to represent about 9% of total cases in Canada up to September 2020, while other geographies ranged from 5% in Italy to 99% in Qatar (Noh and Danuser, 2021). The probability of a case being detected is affected by geography (sociodemographic structure, testing capacity, and recommendations), by individual (age, contact-traced, political beliefs, co-morbidities), and by lineage (symptom severity, infectivity profile). Reason for sequencing is not always random – it could be for an outbreak investigation or to confirm VOC identity – and it varies over time by jurisdiction. As more sequences are generated and made available, we expect more descendants of previously identified sublineages than travellers or their recent contacts harbouring new sublineages or singletons. When sequencing efforts or resources are lower, travellers are a more efficient use of resources if prevalence is higher abroad than domestically, increasing the travel bias. Thus, importations do not scale linearly with sequence representation. In theory, the upper limit of importations by province could be estimated by adjusting for monthly sequence representation, case ascertainment rate, outbreak bias (ratio of probabilities of testing given infected for random versus outbreak-linked), and travel bias (ratio of probabilities of testing given infected for domestic versus travelling populations) over time, stratified by geography. More consistent inclusion of the reason for sequencing and testing in the publicly available metadata could facilitate better estimates of the extent of travel-related and outbreak-related bias. Additionally, prospective cohort studies or seroprevalence studies would ameliorate our estimate of the case ascertainment fraction.

These analyses shed light upon the natural epidemiological history of SARS-CoV-2 in the context of public health interventions and exemplify a sublineage-based method of genomic surveillance. Sharing viral genome sequences linked with the time and place of sampling in a timely manner is of utmost importance for epidemic surveillance of new and already described variants, analyses to support contact tracing, and inference of SARS-CoV-2 importation dynamics.

Materials and methods

Timeline of COVID-19 in Canada

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The spatiotemporal dynamics of average daily COVID-19 diagnoses and SARS-CoV-2 sequences available on GISAID in Canada during the first and second waves prior to 1 March 2021 were summarized in the context of the Oxford stringency index, key epidemic events, and national-level interventions (Figure 1). Number of new cases by Canadian province and territory over time was obtained from the Public Health Agency of Canada, 2021. The distribution of sampled Pango lineages over time was summarized as raw daily sequences and frequencies (Figure 1—figure supplement 1). Dates of national-level COVID-19 interventions were obtained from the Canadian Institute for Health Information, 2021 and key epidemiological events were obtained from a summary published by the Canadian Press via the National Post (Press, 2021). The Oxford Stringency Index for Canada was obtained from the Oxford COVID-19 Government Response Tracker Hale et al., 2021; it is a composite metric of national-level containment and closure policies including school and workplace closures, cancellations of public events, gathering restrictions, stay at home requirements, restrictions on internal movement, and international travel controls. Maps were made using NAD83 datum shapefiles from the Canada Statistics, 2019 Census (Canada Statistics, 2019) and from the United States Census Bureau, 2019.

Sequence cleaning

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1,999,711 SARS-CoV-2 sequences were downloaded from GISAID with metadata on 17 June 2021, of which 63,645 were sampled in Canada (Khare et al., 2021; Shu and McCauley, 2017). Contributing and submitting laboratories of all subsampled sequences are acknowledged in Supplementary file 1. An initial cleaning step was applied to remove sequences if they were listed in the Nextstrain exclude list on the day of data download (n=6272) (Bedford, 2021; Hadfield et al., 2018), duplicate entries (n=790), from a non-human host (n=2057), environmental samples (n=965), likely to contain sequencing errors based on previous temporal analyses (n=2) (Rambaut, 2020), or had incomplete sample collection dates (n=60,076). Canadian sequences with incomplete collection dates (n=19,915) were retained unless they lacked the month of collection (n=83). Remaining sequences were aligned to the Wuhan-Hu-1 reference sequence (GenBank MN908947.3) using the viralMSA python wrapper invoking minimap2 (Li, 2018; Moshiri and Robinson, 2021). A subsequent cleaning step was applied to remove sequences that contained more than 20% ambiguous sites (n=2370) or 10% gaps (n=123,174). Pango lineage designations were assigned using pangolin v3.0.5, pangoLEARN release 2021-06-05 (O’Toole et al., 2021). To focus on the first and second waves, as well as limit the right-censoring effect of sequence deposition delays described elsewhere (Kalia et al., 2021), the cleaned alignment of 1,825,297 sequences (60,143 Canadian) was filtered to only include sequences with collection dates preceding 1 March 2021, resulting in 953,242 sequences (36,736 Canadian). Problematic sites (v5) were masked in the alignment prior to phylogenetic inference (de Maio, 2020).

Subsampling sequences

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Although in an earlier pre-print version of this analysis using data downloaded in February 2021, all 9657 clean Canadian sequences were sampled alongside 40,333 global sequences (McLaughlin et al., 2021), in this updated analysis, 36,736 clean Canadian sequences in the study period necessitated subsampling Canadian in addition to global sequences. Alternative subsampling strategies with varying representation of Canadian sequences were compared in a subsampling sensitivity analysis described below. To maximize our ability to detect introductions, 75% (n=27,552) of available Canadian sequences were sampled in the primary analysis along with 22,448 global sequences, up to a total of 50,000 sequences for computational feasibility (Figure 1—figure supplement 2). Confirmed COVID-19 diagnoses by province from the Public Health Agency of Canada were aggregated by month in order to calculate each province’s contribution to monthly Canadian new diagnoses (PHAC, 2021). Canadian sequences were sampled with probabilities scaled by proportional case contributions, distributed as evenly as possible across all months. Global sequences were subsampled similarly. Country-specific daily new diagnoses from the R package coronavirus (Krispin and Byrnes, 2020) were aggregated by month, and used to calculate the proportion of total new diagnoses in each country among all new global diagnoses, which was applied as a sampling probability for sequences from that month. Since there were relatively few sequences available until March 2020, all clean sequences in the preceding months were included and the remainder of sequences were sampled equally among subsequent months (Supplementary file 3a). Subsampling was repeated for 10 bootstraps with replacement. Estimates are reported as the mean across bootstraps and 95% confidence intervals were calculated using the t-distribution, μ±t × σ/n.

Phylogeographic inference

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Approximate maximum likelihood phylogenies were inferred for every subsampled and masked alignment using FastTree v2.2.1 with a generalized time-reversible substitution model (Price et al., 2010). Trees were outgroup-rooted on Wuhan-hu-1 using R package ape (Paradis and Schliep, 2019). A linear regression of evolutionary distance over time was fit using TempEst v1.5 (Rambaut et al., 2016) and temporal outliers were excluded if residuals were more than three standard deviations from the mean or pendant edges represented more than 12 mutations (Figure 2—figure supplement 1; Supplementary file 3c). Phylogenies were time-scaled using LSD2 within IQ-TREE 2.1.2 (Minh et al., 2020; To et al., 2016), specifying a lognormal relaxed clock with 0.2 relative variance, a generalized time-reversible substitution model with gamma rate variation, invariant sites, three discrete rate categories (Lanfear, 2020), Wuhan-Hu-1 (GenBank MN908947.3) as outgroup, and branch lengths resampled 50 times to calculate confidence intervals on dates (Supplementary file 3d).

Maximum likelihood discrete ancestral state reconstruction was applied on bifurcating time-scaled trees with randomly resolved polytomies, where tip state was designated either Canadian province or country of sampling, using the ace function of R package ape (Paradis and Schliep, 2019). Since Iran and Italy were known to have low sequence representation in early 2020 (Worobey et al., 2020), seven Canadian sequences with travel history to those regions prior to June 2020 had their tip state re-assigned to their country of travel. Travel history was unavailable for the large majority of sequences. The highest likelihood state was pulled for each internal node. Canadian sublineages were designated where a Canadian internal node was preceded by a non-Canadian internal node, signifying an international introduction resulting in onward transmission. Singletons were defined as sampled Canadian sequences with an international parental origin and no sampled descendants. Countries were grouped by continent if they were the likely origin for an average of fewer than five sublineages and the Canadian provinces Nova Scotia, New Brunswick, and Newfoundland and Labrador were merged into the Maritimes. Sublineage importation rate was summarized as the 7-day rolling mean of importations per week by global region, province of introduction, and lineage. The sum of sublineages and singletons represents a lower limit for the total number of introductions.

Sublineage characterization

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Sublineages were named by the most common Pango lineage among unique descendants, followed by the suffix ‘can’ and a numeric based on the order of first Canadian sample dates among each Pango lineage. Early sublineages, such as B.1.can1, may consist of a mixture of Pango lineages and in some cases contained nested sublineages representing re-introductions. If a sequence was a descendant of multiple nested sublineages, they were only considered a unique descendant of the most recently introduced sublineage. Bootstraps differed in their sublineage identification and nomenclature; therefore, we summarized the highest likelihood bootstrap from the 75% subsampling strategy (Figure 3). Sublineages’ TMRCA, the approximate date of the first transmission event resulting in a sampled descendant following viral introduction, were estimated as the mean with 95% confidence intervals.

Sublineages were compared in regards to size, detection lag, and transmission lifespan over time (Figure 4—figure supplement 2). Sublineage size was defined as the number of uniquely sampled descendants within a subtree defined by the Canadian introductory node. A negative binomial model of sublineage size by TMRCA was generated, adjusted by whether a sublineage was active (had any sampled cases in the previous 2 months) or extinct, using the R package MASS (Venables and Ripley, 2002). Since the data was downloaded on 15 June, but restricted to dates before 1 March, the effect of sequence deposition delay on sublineage lifespan was partially mitigated. Models additionally adjusting by province were evaluated but unsupported by likelihood ratio tests and Bayesian information criterion (BIC). Sublineage detection lag was estimated as the number of days between the TMRCA and the first Canadian sample collection. Differences in the detection lags between provinces were compared using a non-parametric Kruskal-Wallis rank sum test, followed by a pairwise Dunn’s rank sum test with Bonferroni p-value adjustment (Dinno, 2017). Multiple linear regression was used to evaluate whether detection lag was associated with TMRCA. The inclusion of province as a confounder was evaluated using likelihood ratio tests and BIC. Sublineage transmission lifespan, that is, the calendar duration of a transmission chain’s persistence, was defined as the number of days from the TMRCA to the most recent Canadian descendant’s sample date. A negative binomial model was applied to evaluate whether sublineage lifespan was significantly reduced over time. To account for sublineages’ differing sizes, the days since importation was estimated for all Canadian sublineage descendants, calculated as the number of days between sampling date and the TMRCA. A 14-day centred rolling mean with 95% confidence interval was calculated to evaluate the changes in the average age since importation of SARS-CoV-2 sequences.

Transmission sources of Canadian sequences

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The parental geography of all Canadian tips in the phylogeny was estimated, which represent all sampled transmission events in which a Canadian was the recipient, including sublineage descendants and singletons. Transmission sources across Canadian tips were categorized as within-province, between-province, USA, or other international (Figure 5). The proportional contribution from all international sources to sampled transmission events in April 2020, following enactment of stringent travel restrictions, was mapped as a chloropleth (Figure 5—figure supplement 1). Between-province transmission was quantified among Canadian tips with a parent from another Canadian province and was reported as the relative number of sampled transmission events where a given province was the source or the recipient.

Subsampling sensitivity analysis

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To evaluate the effect of differing levels of Canadian sequence inclusion relative to global representation, a subsampling sensitivity analysis was conducted comparing four subsampling strategies with 25% (n=9184), 50% (n=18,368), 75% (n=27,552), or 100% (n=36,736) of available clean Canadian sequences subsampled proportionally to monthly provincial new diagnoses and the remainder of global sequences subsampled proportionally to monthly global new diagnoses up to 50,000 total (Figure 6). The temporal distribution of sequences sampled each month was equalized as best as possible, with all sparse months’ sequences included and the remainder of sequences distributed equally across months (Supplementary file 3a). For each strategy, 10 bootstraps were sampled with replacement. All 40 subsampled alignments were analysed through the remainder of the phylogeographic pipeline as described previously. The sensitivity of the results was assessed in regards to the 7-day rolling average of number of sublineages imported into provinces over time and the relative contributions of global origins in each wave.

The mean and 95% confidence intervals were calculated across bootstraps using the t-distribution and compared across strategies for metrics including number of sublineages, singletons, and total imports overall and by location pair; proportion of all imports that were sublineages; and sublineage importation rates over time. The sensitivity of the sublineage characterization models including detection lag over time, number of descendants over time stratified by active, and sublineage lifespan stratified by active were also compared across strategies.

R packages

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R packages used in the cleaning, subsampling, and phylogenetic analysis included ape 5.4–1 (Paradis et al., 2004), Biostrings 3.1.3 (Pagès et al., 2020), phytools 0.7–70 (Revell, 2012), phangorn 2.5.5 (Schliep et al., 2017), tidyverse 1.3.1 (Wickham et al., 2019), coronavirus 0.3.0.9000 (Krispin and Byrnes, 2020), lubridate 1.7.9.2 (Grolemund and Wickham, 2011), zoo 1.8–8 (Zeileis and Grothendieck, 2005), RColorBrewer 1.1–2 (Neuwirth, 2014), cowplot 1.1.1 (Wilke, 2020), ggstance 0.3.5 (Henry et al., 2020), ggalluvial 0.12.3 (Brunson, 2020), ggridges 0.5.3 (Wilke, 2021), ggtree 2.2.4 (Yu et al., 2016), ggplotify 0.0.5 (Yu, 2020), ggrepel 0.9.1 (Slowikowski, 2021), MASS 7.3–53 (Venables and Ripley, 2002), and treemapify 2.5.5 (Wilkins, 2021). Additional R packages used to generate the maps included rgeos 0.5–5 (Bivand and Rundel, 2018b), maptools 1.0–2 (Bivand and Lewin-Koh, 2018a), ggsn 0.5.0 (Baquero, 2017), broom 0.7.6 (Couch, 2021), and rgdal 1.5–18 (Bivand et al., 2017).

Data availability

All the viral genetic sequences analyzed in this study were sourced from the Global initiative on sharing all influenza data (GISAID) coronavirus (CoV) database and are subject to the GISAID EpiFlu Database Access Agreement. Within this agreement, we are disallowed to distribute data to any third party other than Authorized Users as contemplated by this Agreement. In lieu, a table of accession IDs was provided in Supplementary file 1, along with an acknowledgement of all originating and submitting laboratories. Submission of this table is also a condition of use of the data under the terms and conditions of GISAID. Individuals who would like to become Authorized Users may submit an application https://www.gisaid.org/registration/register/ and agree to the Database Access Agreement. Full and subsampled alignments can be shared to Authorized Users upon request. The scripts used for data curation, inferences, analyses, and visualization are available with a reproducible example at https://github.com/AngMcL/sars-cov-2_canada_2020 (copy archived at swh:1:rev:1cb49fa43c752b1ce1003c52e43b5c47d3b6789f).

The following previously published data sets were used
    1. Shu Y
    2. McCauley J
    (2017) GISAID
    GISAID: Global initiative on sharing all influenza data - coronovius (CoV).
    https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494

References

Decision letter

  1. Sarah E Cobey
    Reviewing Editor; University of Chicago, United States
  2. Sara L Sawyer
    Senior Editor; University of Colorado Boulder, United States
  3. Bernardo Gutierrez
    Reviewer

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Early introductions of SARS-CoV-2 sublineages into Canada drove the 2020 epidemic" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by David Serwadda as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Bernardo Gutierrez (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

All reviewers thought this study represented a potentially valuable contribution. They also all expressed concern about the impact of potentially biased sampling on the conclusions. We recognize that this is a general challenge in the field, and not all papers in it have necessarily faced this level of scrutiny. Because the conclusions of this paper are quantitative, it seems important to quantify the uncertainty (in this case, to model misspecification/sampling bias) accurately.

The reviewers outline nuances in their individual commentaries below, but there are effectively three general issues:

1) It seems particularly important to understand (e.g., as a sensitivity analysis) the impact of not downsampling the samples from Canada.

2) Reviewers also asked whether the case-based scaling of sequences is appropriate. This relates to a more general problem raised by the highly uneven sampling by country and region. Downsampling so that no region is overrepresented with respect to its relative infection load would leave zero samples for analysis, since so many areas are missing sequences entirely. Perhaps again performing some kind of sensitivity analysis or referencing a more thorough investigation of this problem, how does uneven representation impact estimated migration rates?

3) The reviewers were also concerned with the estimate of the total number of introductions.

Effectively, all the reviews are requesting a more careful treatment of potential problems with data collection (it is not random!), both in the analyses and in the text.

Reviewer #1 (Recommendations for the authors):

A few other statistical questions are raised in the recommendations for the authors below.

Statistics/methodology

1. p. 5, l. 3: Clearer logic in the "Subsampling" section would help. What is the justification for subsampling proportional to case number, which is obviously quite biased by surveillance effort, perhaps even more biased than the number of sequences? Why not subsample proportional to countries' population size or excess mortality, adjusted for age? It would be helpful to describe too how much subsampling occurred and that it was necessary for computational reasons. What kinds of biases are still present? For instance, it would be good to point out somewhere the regions/periods that are so poorly sampled (e.g., in many lower income countries) that the inference could be biased to favor endemic transmission, assuming my intuition is correct.

2. p. 8, ll. 6-8: Could you explain the logic of how the total number of introductions was inferred given the estimated rate at which cases were sequenced (and cases detected)? Should the estimated number of introductions scale linearly?

3. p. 12, ll. 6-9 (Figure 4C): Does this model properly adjust for the typical delay between sampling members of a sublineage? Sublineages with more recent tMRCA should have fewer descendants simply because less time has passed. It is not clear the stratification takes care of this. There might also be a decrease in the number of descendants with time if the total number of sublineages is increasing and surveillance does not scale with sublineage richness. The text reads "To elucidate the relative contributions of early and late sublineages,….," but it is unclear exactly what contribution is being measured here.

4. p. 10, ll. 10-11 (Figure 4D): I think we could see an increase in days since importation over time even if more recently imported lineages were generally more common ("dominant"). The maximum possible age of the oldest lineage is increasing with time. The sentence says this "suggests" that early introductions "increasingly dominated" the epidemic. Maybe it would be easier to demonstrate this through specific examples or a measure of persistence time and a proxy of prevalence.

5. p. 18, ll. 6-8: "Sequences from individuals with travel history to Iran or Italy before June 2020 were recategorized as having been sampled in the country of travel." Why was this not done for individuals with travel history from other countries?

Interpretation

1. Abstract: "Rapid implementation of stringent border controls and quarantine could have diminished the Canadian COVID-19 burden by curtailing the spread of early introductions." I question how much stringent border controls, unless defined very severely as prohibiting the introduction of, e.g., more than ten lineages, could have diminished the COVID-19 burden. Once any lineage is established and transmitting in the community, additional introductions have negligible impact on prevalence or the "final size" of an epidemic. Quarantine, by reducing the effective reproductive number, can reduce burden. I worry that lack of precision on the impact of introductions on burden can lead to the sort of knee-jerk travel bans we see with Omicron.

2. p. 3, ll. 7-8: "can illuminate"? Deciphering the rates of imported and domestic transmission is not guaranteed.

3. p. 8, l. 8: A 42% infection detection rate (as cases) struck me as rather high. The cited reference appears to be for something else.

4. p. 10, ll. 1-3: What is the purpose of mentioning the number of global descendants when it is known to be such an underestimate?

5. Figure 4. It would be useful to reference 4A and 4B specifically in the text.

6. p. 15, ll. 4-6: How can we separate the impacts of behavioral changes (NPIs) from seasonality on sublineage size and duration?

7. p. 15, ll. 22-24: Where is the mandatory 14-day quarantine coming from? Given the incubation period and duration of shedding, I know many who think quarantine should be shorter (if testing were not feasible). With sensitive testing, there is no reason to keep people past a week if they are consistently negative. I think it's important not to imply the traditional durations used for quarantine are especially grounded or optimized.

Potential enhancements

It would be interesting to see how the importation rates from different countries correlate with air travel to/from those countries. I realize this is difficult given connecting flights, but perhaps the data exist somewhere.

p. 16, ll. 11-14: Ideally these nonstationary sampling probabilities by province (and really for all countries) would be built directly into the likelihood.

Reviewer #2 (Recommendations for the authors):

1. Line 17-21 argue genomic epidemiology is useful to track and characterize VOI/VOC, but the sequences analyzed in this manuscript are largely pre-VOC so I think it makes sense to re-focus and expand this discussion to include commentary on the importance of evaluating the success of public health interventions, travel restrictions, etc.

2. I think outlining a hypothesis at the end of the intro is useful, but as it is currently written the hypothesis is very long and difficult to understand. Can the authors restate and clarify the hypothesis into a single sentence?

3. As the authors discuss, their results and those of all genomic epidemiological studies are very sensitive to sequence quality, availability, distribution, and sampling paradigms. They reduce the impact of unequal global sequencing efforts by subsampling global sequences proportionally to their monthly case counts, excluding Canadian sequences. I understand the rationale here, but worry that inclusion of all available Canadian sequences (which are not proportional to case count trends as is clear in Figure 1D and 1E) skewed their results. Perhaps one way to assess the robustness of the results would be to repeat the main analyses using a subsampled (proportion to case counts) Canadian dataset and compare results? Relatedly, even case counts could be skewed depending on testing availability and accuracy -- could the authors include a brief commentary on the availability, accessibility, and capacity of testing centers in Canada during the first year of the pandemic?

- it is also clear to see in Figure 1C and 1F that US sequences are overrepresented in this dataset compared to case counts -- do the authors think this is inflating the proportion of US imports? (I agree it is clear most imports came from the US)

- the Alberta and Maritimes outbreaks in spring of 2020 appear very striking (large outbreaks with lots of person-to-person spread and very few descendants) in Figure. However, this interpretation is fraught when you consider Figure 1E and 1D because there are lots of available sequences during this time compared to the total case count vs fall of 2020 where there is actually a higher case burden and very few available sequences. I think many readers will get hung up on this so maybe the authors could address it briefly in the text?

- While the color scheme is lovely, it is difficult to distinguish between some provinces particularly in Figure 2

4. Lines 6-10. I think it is a big leap to estimate the total possible introductions based on sequences representative of 1.1% of total cases.

5. It might be very informative to take a more careful look at rates of viral spread (R0) over time in addition to patterns of viral importation. This could help contextualize the effect of public health interventions on domestic spread. The authors bring up quarantine, contact tracing, testing, and individual behavior change in the discussion -- do the authors think the rate of international imports is the best measure to assess these interventions? I think R0 would be just as informative!

6. I am skeptical the results on reductions in sublineage size, increasing tMRC, and perhaps even sources of domestic transmission will persist if the Canadian sequences are subsampled proportionally to case counts. Can the authors please assess the robustness of these results against a subsampled dataset?

7. I agree with and appreciate the authors' discussion of the importance data sharing, including a limited metadata set to aid in the rapid interpretation and use of sequence data for important public health decisions.

8. Although this is a stylistic preference, I believe a cleaned up version of Figure 7 with the Canada's key public health interventions and COVID-19 outbreaks along with a map of Canada's provinces (already a supplemental) and case counts during the study period would be very useful to include as an early, primary figure.

Reviewer #3 (Recommendations for the authors):

My main concern lies on the explicit naming of sources of importation following the application of an epidemiologically-based approach to downsample the publicly available genome sequences from GISAID. While it is mentioned that ten subsamples were generated, it is not clear to me that the ancestral state reconstruction was evaluated on these ten subsamples and how the results compare between them. The uncertainty of the ASR itself was incorporated for the fixed phylogeny through an ML approach (ape::ace()), but I am missing the analysis of the robustness of the results across different subsamples.

Furthermore, even if these analyses where performed, it is unclear whether a different subsampling approach could lead to the identification of a different profile of source countries, and if so, which subsampling approach would be more accurate. The accuracy of the identified countries of origin (and therefore accuracy of the proposed downsampling approach) would be validated if a complementary data source showed similar patterns: an expected importation index (EII ~ Incidence * no. of travellers to Canada), self-reported travel histories of patients with confirmed COVID-19, etc. If these complementary data sources are unavailable, I would recommend reducing the emphasis on the identification of countries of origin (or constraining it to regions where you're confident that the monthly reported cases are reliable – the USA would be an obvious example of this), or adding a clear discussion on these limitations. I would argue that this goes beyond the already mentioned idea that downsampling does not allow to increase the numbers of sequences from some unsampled times/locations (page 16, lines 5-20), and that it can also reduce the probability of phylogenetically identifying true importations from countries with low case prevalence.

On other topics: I am unsure about the estimation of total number of introductions presented in page 8 (lines 6-10). Do you have any particular reason why you would expect that importations scale linearly with sampling/representation? Given the overall dynamics, wouldn't it be more probable that higher sampling intensities would add unsampled sequences to the larger sublineages in the country (proportional to the size of the lineages) as well as uncovering unsampled importations? The rationale behind these calculations requires some more detailed explanations.

Regarding the domestic transmission, I was wondering if you had access to finer geographical resolution beyond province-level? Provinces are particularly large geographical areas, and providing some insights (rather than a full, high-resolution phylogeographic re-analysis of all the data) into the sampling within provinces could add to the manuscript. This is particularly the case for the analysis of international importations to Quebec and Ontario: where these sublineages first detected in large urban areas or not necessarily? Is the sampling within provinces representative of the cases reported in urban and rural areas? Are there any differential dynamics between larger urban areas and more remote locations?

Finally, it is quite interesting to see that the peak of TMRCAs nicely coincide with the maximum stringency on March 21 (Figure 4A,B) – even more so given that the TMRCA of a sublineage is more likely representing something akin to the first transmission event within said sublineage rather than the importation event per se. It would be interesting if the TMRCAs could be contextualised to show their distribution after accounting for sampling lag (i.e., the time between symptom onset and sample collection). This could be estimated from sequence metadata or approximated from non-genomic epidemiological data, but it would give a better idea of the times during which the infections that lead to these sublineages started.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Genomic epidemiology of the first two waves of SARS-CoV-2 in Canada" for further consideration by eLife. Your revised article has been evaluated by Sara Sawyer (Senior Editor) and a Reviewing Editor.

The manuscript has been dramatically improved---the sensitivity analyses are immensely helpful---but there are some remaining issues that need to be addressed, as outlined below. The reviewing editor is concerned that some of the policy suggestions do not follow the evidence presented.

1. Abstract: "… suggesting travel restrictions and quarantine must be sustained to fully curtail COVID-19 burden." I am guessing most readers will likely interpret "to fully curtail COVID-19 burden" as to get the burden near zero. Is this what the authors intended? It affects the logic. The results here show that travel restrictions were usually associated with at least several importations per week. Mathematically, we know that the burden of COVID-19 does not really depend on the number of importations if R ~ 1 or more---all you need is at least one endemic lineage to maintain the prevalence/burden dictated by R. Maybe the authors are referring to conditions when R<1 and travel restrictions are even greater than what they have been historically, but this is unclear. It is also unclear precisely what the authors mean by "quarantine." Usually, it refers to the isolation of contacts of potential cases (not the cases themselves), and sometimes it refers more generally to lockdowns. Neither policy seems particularly worth calling out here. Why not say "sustained interventions to lower transmission (R<1)"? It seems unjustified for this study to single out quarantine (either def) as the necessary complementary intervention when so many other approaches have been shown more effective. I'm dwelling on the wording because these kinds of conclusions, especially in an abstract, can have an outsized impact on policy.

2. Abstract: "…restrictions that reduce the probability of importations are most effective during periods of low domestic prevalence and low or waning immunity." Waning immunity means R is rising, which, all things equal reduces extinction probabilities and increases the success of invading lineages (e.g., Otto and Whitlock 1997). Without more precision about R and how effectiveness is being defined, it's not clear restrictions should be more impactful under these conditions. I do see the positive correlation between "periods of waning immunity" and possible ban-assisted extinction, but only because immunity is most often waning when there has recently been an epidemic, and the recently boosted immune protection is relatively high. I worry again about the implications of these claims, which are not exactly supported by the research here.

3. Relatedly, the main text describes evaluating the relative contribution of (reducing) importations to (reducing) *burden* (e.g., ll. 190-192, ll. 605-607, 691-701), but this analysis is not really performed here. Such analysis would require careful estimation of R and prevalence and a good enough transmission model to evaluate extinction probabilities and outbreak sizes (in the case of R<1) over time, and it would have to account for clonal interference. Put differently, the fact that imported lineages caused x% of cases does not mean that the case count would've been (100-x)% without those importations. The fact that imported lineages displaced resident lineages does not mean the resident lineages could not have caused comparable infections had the imported lineages been stopped at the border, assuming the imported lineages had no fitness differences. Imperfect travel bans that allow some lineages to invade can at best slightly (at measured rates) change the timing of an epidemic (ll. 704-707), but usually not its size or severity. The authors write, "Although blind travel bans may not be beneficial from a socioeconomic perspective, ultimately governments need to protect citizens and dynamic travel bans are one of the few tools available." This is surprising considering the number of broader tools at our disposal (e.g., rapid testing, vaccination, ventilation/filtration) that can slow importations AND endemic spread, as well as the observed ineffectiveness of travel bans at stopping spread outside of islands and China. The authors IMO have not demonstrated how much bans could delay importation and allow time to "plan", or if investments to reduce transmission generally are more effective. I suggest these claims be carefully reconsidered.

I apologize for the wordiness. I would be happy to bolster these points (maybe with math) if they are unclear. I am concerned that too much is being extrapolated about the possible and actual epidemiological impacts of travel bans from the measured rates of importation.

You may wish to consider some suggestions to improve readability from another reviewer:

4. In line 424, I'm not entirely sure what the authors mean by "the first wave of singletons had a higher amplitude than the second wave". By 'amplitude', do they refer to the range of dates when singletons were identified between both waves? Is this relative to a feature of the importations of sublineages (given that this sentence talks about this comparison between sublineage and singleton dynamics)? Some clarification would be helpful.

5. The Discussion addresses a lot of the reviewer comments thoroughly and systematically, which was very helpful, but could be streamlined for the readers. I think that, for example, the two paragraphs that go from line 711 to line 741 could be reduced to a single paragraph discussing 1. sampling bias in phylogeography, 2. the specific effects of overrepresentation of some locations and how they addressed it with subsampling and the sensitivity analyses, 3. effects of upsampled lineages and under-sampled locations and how this can theoretically be overcome, 4. the broader challenges of scaling up Bayesian phylogenetics (this last point could be a separate paragraph, but unrelated to sampling bias).

https://doi.org/10.7554/eLife.73896.sa1

Author response

Essential revisions:

All reviewers thought this study represented a potentially valuable contribution. They also all expressed concern about the impact of potentially biased sampling on the conclusions. We recognize that this is a general challenge in the field, and not all papers in it have necessarily faced this level of scrutiny. Because the conclusions of this paper are quantitative, it seems important to quantify the uncertainty (in this case, to model misspecification/sampling bias) accurately.

We are very grateful to the reviewers and editors for their time in reviewing our manuscript and for the many helpful comments, suggestions and clarifying questions, which we have incorporated in the revised manuscript. Most notably, we have assessed the impact of sample bias on importation rate estimates though an empirical subsampling sensitivity analysis. Additionally, we have updated the entire analysis with a more recently downloaded dataset, which included about four times the number of Canadian sequences, and an expanded study period up until March 2021 to include the entirety of the second wave. To accommodate more Canadian sequences, we have extended the global subsampling strategy to subsample Canadian sequences proportionally to provincial case counts. To address the issues of potential sampling bias on relative importations into provinces over time, we explored multiple subsampling strategies in which 25%, 50%, 75%, or 100% of Canadian sequences were retained, with the remainder of sequences from the global set up to 50,000 total. We found that under all the subsampling strategies, our conclusions were robust with regards to the impact of the travel restrictions, the temporal dynamics of importation rates, and the relative contributions of geographies towards sublineages in the first and second waves.

The reviewers outline nuances in their individual commentaries below, but there are effectively three general issues:

1) It seems particularly important to understand (e.g., as a sensitivity analysis) the impact of not downsampling the samples from Canada.

We thank the reviewers for identifying the importance of this limitation. We have addressed the impact of subsampling the Canadian sequences relative to global context sequences in a sensitivity analysis.

Methods

“Subsampling sensitivity analysis

To evaluate the effect of differing levels of Canadian sequence inclusion relative to global representation, a subsampling sensitivity analysis was conducted comparing four subsampling strategies with 25% (n = 9,184), 50% (n = 18,368), 75% (n = 27,552), or 100% (n = 36,736) of available clean Canadian sequences subsampled proportionally to monthly provincial new diagnoses and the remainder of global sequences subsampled proportionally to monthly global new diagnoses up to 50,000 total (Figure 6). […] The sensitivity of the sublineage characterization models including detection lag over time, number of descendants over time stratified by active, and sublineage lifespan stratified by active were also compared across strategies.”

Results

“Subsampling sensitivity analysis

To render phylogenetic inferences computationally feasible, reduce overrepresentation of geographies with more sequences per case, and evaluate uncertainty in the effect of sampling on importation rates, sequences were subsampled proportionally to either provinces or countries’ contributions to monthly case counts in Canada and globally for multiple bootstraps. […] Across all strategies, there were significant reductions of sublineage size and lifespan over time.”

2) Reviewers also asked whether the case-based scaling of sequences is appropriate. This relates to a more general problem raised by the highly uneven sampling by country and region. Downsampling so that no region is overrepresented with respect to its relative infection load would leave zero samples for analysis, since so many areas are missing sequences entirely. Perhaps again performing some kind of sensitivity analysis or referencing a more thorough investigation of this problem, how does uneven representation impact estimated migration rates?

We thank the reviewers for acknowledging this key issue in interpreting phylogeographic migration rates. We believe this issue has been partially addressed with the empirical subsampling sensitivity analysis. Sampling bias has been further elaborated upon in the Discussion, with additional citations on the sensitivity of discrete trait analysis to sampling bias.

“Ancestral trait reconstruction is sensitive to sampling bias as ancestral node states are more likely to reflect states predominant among tips (Baele et al., 2016; Hill et al., 2021; Lemey et al., 2009). […] Stratified analyses of well-supported (sub)lineages in future analyses will facilitate direct comparisons of Bayesian and maximum likelihood phylogeography methods employed here.”

3) The reviewers were also concerned with the estimate of the total number of introductions.

We gratefully acknowledge the reviewers’ concern regarding the rough extrapolation of an upper limit on the total number of introductions. Rather than estimate the upper limit, we have elaborated upon the type of data required to attempt this with any shred of confidence.

“Low sequence representation can lead to underestimates of total introductions if neither index case or descendants were sampled, underestimates of sublineage size if not all descendants were sampled, and similarly, overestimates of the proportion of singletons, which may have been from unsampled transmission chains. […] Additionally, prospective cohort studies or seroprevalence studies would ameliorate our estimate of the case ascertainment fraction.”

Effectively, all the reviews are requesting a more careful treatment of potential problems with data collection (it is not random!), both in the analyses and in the text.

Reviewer #1 (Recommendations for the authors):

A few other statistical questions are raised in the recommendations for the authors below.

Statistics/methodology

1. p. 5, l. 3: Clearer logic in the "Subsampling" section would help. What is the justification for subsampling proportional to case number, which is obviously quite biased by surveillance effort, perhaps even more biased than the number of sequences? Why not subsample proportional to countries' population size or excess mortality, adjusted for age?

We acknowledge the reviewer’s concern regarding the effect of differences in surveillance upon case proportional subsampling, however we maintain our perspective that the extent of bias is greater among available sequences than it is among detected cases. The supplementary figures comparing sequences per case demonstrate the utility of this subsampling method in improving the correlation of cases to sequences. While it would be possible to sample proportionally to population size, this exerts a prior belief that infected cases should be equally distributed globally and that a larger population necessarily results in a larger risk at any given time. An example corroborating this would be China, which contributed most cases in January and February 2020, but very few relative cases in late 2020 and early 2021, despite a large population size. Alternatively, COVID deaths or excess mortality could have been used, but are subject to biases of their own. Definitions and identification of COVID deaths differed over time and space, and while excess mortality would account for missed deaths, it would also include excess deaths due to other indirect causes (delayed surgeries due to COVID, for instance). We have added to the Discussion elaborating upon the justification of this method and the sensitivity analyses we conducted regarding the assumptions.

“Ancestral trait reconstruction is sensitive to sampling bias as ancestral node states are more likely to reflect states predominant among tips (Baele et al., 2016; Hill et al., 2021; Lemey et al., 2009). […] The subsampling sensitivity analysis revealed how subsampling iteratively reduced overrepresentation in available provincial sequences and improves the correlation between cases and sequences.”

It would be helpful to describe too how much subsampling occurred and that it was necessary for computational reasons. What kinds of biases are still present? For instance, it would be good to point out somewhere the regions/periods that are so poorly sampled (e.g., in many lower income countries) that the inference could be biased to favor endemic transmission, assuming my intuition is correct.

We have clarified the extent of subsampling by geography by sampling strategy.

The general effect of lingering sampling biases was elaborated upon in the Discussion (see excerpt above) and more specifically in regards to the overrepresentation of the USA:

“Although the USA was highly represented in all of our subsamples as a result of its large contribution to COVID-19 cases in 2020 and high sequence availability during early months, our results suggest a greater effect than due to sampling alone. On average, the USA sequences represented 28.9% (28.7 – 29.2%) of total international sequences, yet accounted for 46.3% (44.0 – 48.7%) of all sublineages and 57.7% (55.6 – 59.8%) of singletons. Upon maximizing the number of Canadian sequences in the analysis, where global sequence representation was more normalized but less comprehensive, the USA sequences represented fewer of the international sequences (25.8%, 25.6 – 26.1%) and still accounted for 38.4% (37.0 – 39.8%) of sublineages and 46.4% (44.6 – 48.3%) of singletons.”

The reviewer posited that the inference could be biased to favor endemic transmission, by which we assume they meant the inference would be more likely to detect local or domestic transmission sources than international sources of sampled Canadian viral diversity. This is possible in instances where there was an absence of sequences sharing a recent common ancestor with Canadian sequences from the true sublineage origin location, which itself was seeded by a previous ancestor with descendants in Canada. In the subsampling sensitivity analysis, where Canadian sequences were differentially represented in the data, the proportion of transmission attributable to international sources was comparable, although with fewer total estimated importations when too few Canadian sequences were included (25% strategy) or too few global sequences were included (100% strategy). In the 75% strategy, there were nearly the same number of Canadian and global context sequences, which if we assume a well-mixed population, confers a similar probability of detecting domestic versus international transmission sources. In reality, the global sample is not randomly drawn from a well-mixed viral pool, despite our best efforts. Naturally, we will miss detecting an importation from a geography with no sequences available. We comment upon the potential of advances in methodology by Phillipe Lemey and colleagues towards incorporating unsampled nodes in phylogeographic inference to mitigating this issue.

“Incorporating individuals’ travel history or flight volume data could help to improve representation of undersampled geographies (Lemey et al., 2020; du Plessis et al., 2021). However, Bayesian phylogeography remains limited in its scalability despite improved efficiencies in the memory required (Didelot et al., 2018; du Plessis et al., 2021), and they are still limited to thousands, rather than tens or hundreds of thousand sequences. Therefore, the maximum likelihood approach that we have applied offers scalability, while taking into consideration the uncertainty of sampling bias by including ten bootstraps for four subsampling strategies. Random sampling permits an exploration of the effect of country and Canadian province representation over time on the results. In comparing strategies, the relative spatiotemporal trends were robust, while raw estimates were more variable, as were smaller rate estimates. Stratified analyses of well-supported (sub)lineages in future analyses will facilitate direct comparisons of Bayesian and maximum likelihood phylogeography methods employed here.”

2. p. 8, ll. 6-8: Could you explain the logic of how the total number of introductions was inferred given the estimated rate at which cases were sequenced (and cases detected)? Should the estimated number of introductions scale linearly?

We gratefully acknowledge the reviewer’s concern regarding the rough extrapolation of an upper limit on the total number of introductions. Rather than estimate the upper limit, we have elaborated upon the type of data required to attempt this with any shred of confidence.

“Low sequence representation can lead to underestimates of total introductions if neither index case or descendants were sampled, underestimates of sublineage size if not all descendants were sampled, and similarly, overestimates of the proportion of singletons, which may have been from unsampled transmission chains. […] Additionally, prospective cohort studies or seroprevalence studies would ameliorate our estimate of the case ascertainment fraction.”

3. p. 12, ll. 6-9 (Figure 4C): Does this model properly adjust for the typical delay between sampling members of a sublineage? Sublineages with more recent tMRCA should have fewer descendants simply because less time has passed. It is not clear the stratification takes care of this. There might also be a decrease in the number of descendants with time if the total number of sublineages is increasing and surveillance does not scale with sublineage richness. The text reads "To elucidate the relative contributions of early and late sublineages,….," but it is unclear exactly what contribution is being measured here.

In the updated analysis, we included data up to March 2021 that had been deposited by June 2021. This permitted us to include the entire first and second waves, reducing the effect of differential delays in observing descendants. While the stratification of sublineages on the basis of having any sampled descendants in the previous two months does not entirely adjust for the delay in deposition, it improves our confidence in asserting trends among extinct sublineages. In regards to whether we might observe a decrease in the number of descendants with time if the total number of sublineages is increasing but surveillance does not scale with sublineage richness, this assumes that the testing capacity is surpassed. Until the testing capacity in a given region is reached and surpassed, the proportion of a sublineage’s descendants that are tested and sequenced should remain relatively constant, notwithstanding differences in contact tracing implementation. Although testing capacity was exceeded later in 2021, this did not affect data from the first two waves as much.

“During the first wave, early sublineages had the opportunity to become established and resulted in large transmission chains driving COVID-19 burden, consistent with findings from the UK (du Plessis et al., 2021). However, the second wave was mostly driven by cases from newly seeded sublineages. We accounted for potential confounding due to recent sublineages having been monitored for less time by stratifying sublineages by whether they were active or extinct in the previous two months; and by restricting the data to collection dates three months prior to the data download, we accounted for delay in depositing sequence data for more recent sublineages. Differences remained between early and late sublineages’ evolutionary dynamics in the presence of VOCs with heightened transmissibility and the roll-out of vaccines.”

4. p. 10, ll. 10-11 (Figure 4D): I think we could see an increase in days since importation over time even if more recently imported lineages were generally more common ("dominant"). The maximum possible age of the oldest lineage is increasing with time. The sentence says this "suggests" that early introductions "increasingly dominated" the epidemic. Maybe it would be easier to demonstrate this through specific examples or a measure of persistence time and a proxy of prevalence.

We agree with the reviewer that this requires clarification and validation. Similar to persistence time or duration of transmission, we have modelled sublineage lifespan, the time period between the TMRCA and last sample date, and stratified sublineages activity based on whether there were any samples in the last two months or if they appear to have gone extinct. By updating the analysis with data available up to 15 June 2021, but only investigating data sampled prior to 1 March, the effect of deposition delay on right truncation was reduced.

5. p. 18, ll. 6-8: "Sequences from individuals with travel history to Iran or Italy before June 2020 were recategorized as having been sampled in the country of travel." Why was this not done for individuals with travel history from other countries?

There were very few Canadian sequences with travel histories available (n=10). Besides sequences associated with Iran or Italy, these included one of each with travel history from Wuhan, Hong Kong, and an international cruise ship. Although we could have re-assigned the state of Canadian sequences with travel history to Wuhan and Hong Kong, the majority of sequences available during January and February 2020 were from China anyways. Furthermore, re-assigning tip state prevents us from identifying these sequences as introductions, so it is only a practice we would recommend for the most severely undersampled countries, which Iran and Italy were during the early pandemic.

Interpretation

1. Abstract: "Rapid implementation of stringent border controls and quarantine could have diminished the Canadian COVID-19 burden by curtailing the spread of early introductions." I question how much stringent border controls, unless defined very severely as prohibiting the introduction of, e.g., more than ten lineages, could have diminished the COVID-19 burden. Once any lineage is established and transmitting in the community, additional introductions have negligible impact on prevalence or the "final size" of an epidemic. Quarantine, by reducing the effective reproductive number, can reduce burden. I worry that lack of precision on the impact of introductions on burden can lead to the sort of knee-jerk travel bans we see with Omicron.

Thank you for your perspective on this subject, which remains an active discussion. We have elaborated upon this dialogue in the Discussion.

“The burden of cases in the second wave was primarily due to sublineages introduced in the summer of 2020, despite a low number of importations and case burden at that time. […] These advanced planning steps also serve to minimize the economic harm due to work absenteeism and supply chain disruption.”

2. p. 3, ll. 7-8: "can illuminate"? Deciphering the rates of imported and domestic transmission is not guaranteed.

Thank you for this suggestion. We have changed the wording.

“Studying evolutionary relationships between SARS-CoV-2 sequences over time and space allows estimation of the relative contributions of international and domestic transmission in association with public health interventions.”

3. p. 8, l. 8: A 42% infection detection rate (as cases) struck me as rather high. The cited reference appears to be for something else.

Thank you for this point. We have identified a more appropriate and robust reference for the case ascertainment rate in Canada in 2020.

“Clean genomes available in Canada prior to 1 March 2021 represented 4.2% of confirmed diagnoses (and 3.2% when 75% of Canadian sequences retained). Diagnoses were estimated to represent about 9% of total cases in Canada up to September 2020, while other geographies ranged from 5% in Italy to 99% in Qatar (Noh & Danuser, 2021).”

4. p. 10, ll. 1-3: What is the purpose of mentioning the number of global descendants when it is known to be such an underestimate?

Although it will naturally be an underestimate as a consequence of subsampling and incomplete case ascertainment, we have included it as a way of showing that migration flows both ways and that Canadian sublineages have been exported out of the country. While absolute numbers cannot be extracted, the relative representation of countries among sampled descendants can be interpreted with some uncertainty.

5. Figure 4. It would be useful to reference 4A and 4B specifically in the text.

Please note that the figure numbering has changed during our revisions. What were previously figure 4A and B are now Figure 5A and Figure 5—figure supplement 1. They are referenced in the text here:

“Following the entry restriction for foreign nationals in mid-March, there was a reduction, but not an elimination, of the proportion of transmission events attributable to the USA or other international sources across all provinces (Figure 5A). Prior to the restrictions, provinces had a mean of 26% of transmission events with any international origins, ranging from 17% (14-20%) in Saskatchewan to 33% (31-35%) in British Columbia. In April 2020, following travel restrictions, all provinces had decreased proportions of international transmission sources, yet Ontario and Quebec retained relatively high mean number of transmission events attributable to international sources, at 115 (105-125) and 95 (82-109) (Figure 5—figure supplement 1). This may suggest slower implementation or compliance with quarantine guidelines in these provinces.”

6. p. 15, ll. 4-6: How can we separate the impacts of behavioral changes (NPIs) from seasonality on sublineage size and duration?

We agree with the reviewer that seasonality is an important confounding factor to consider in associating differences in transmission with the implementation of NPIs or behavioral changes –factors such as temperature, humidity, and socializing inside in poorly ventilated areas can all affect transmission, and thus sublineage size and duration. In this 2020 analysis, it was not possible to disentangle these effects over the span of one year. If might be tempting to compare to 2021, but the emergence and domination of VOCs make them hard to compare. Within 2020, one might consider comparing the effect of a given NPI, such as restrictions on international travel, on sublineage size and duration in different seasons in Northern and Southern hemisphere countries. However, the challenge here is that countries different in the timing and strength of their NPIs and had different dominant sublineages circulating at any given time. Although the relationship with sublineage size and duration with time may be confounded by seasonality, the reduction in importations during Spring 2020 was too rapid and drastic to have occurred as a result of differences in season.

7. p. 15, ll. 22-24: Where is the mandatory 14-day quarantine coming from? Given the incubation period and duration of shedding, I know many who think quarantine should be shorter (if testing were not feasible). With sensitive testing, there is no reason to keep people past a week if they are consistently negative. I think it's important not to imply the traditional durations used for quarantine are especially grounded or optimized.

We have reworded the discussion to not necessarily recommend a 14-day quarantine.

“The downstream effects of border porosity could have been better mitigated by earlier widespread availability of rapid antigen tests to inform quarantine duration (CDC, 2020), and through increased resources for contact tracing, cluster response, and compliance oversight.”

Potential enhancements

It would be interesting to see how the importation rates from different countries correlate with air travel to/from those countries. I realize this is difficult given connecting flights, but perhaps the data exist somewhere.

We agree with the reviewer that this would be an interesting and challenging comparison of expected versus observed importation rates. This is similar to what was undertaken by du Plessis et al., where both prevalence and flight volume were considered towards an estimated Importation Intensity. If we had access to volumes of arrival flights or number of passengers into Canada by country for 2020, we could make this comparison. If there were more importations than expected, this may suggest systematic differences in quarantine or testing oversight based on where people arrived from. However, flight data does not incorporate land crossings, which we expect accounted for a large number of importations from the USA. We discussed the potential role played by travel across the land border towards importations from the USA.

“It was foreseeable that the USA would be the largest source of SARS-CoV-2 importations into Canada given its high COVID-19 prevalence throughout 2020 as well as the fact that these nations share the world’s longest land border, spanning 8,890 km. Overall international arrivals into Canada declined 77.8% from 96.8M in 2019 to 19.7M in 2020 (Statistics Canada, 2021a). However, the number of truck drivers and other crew members only declined by 24.8%, accounting for 49% of all international arrivals after April 2020 (of whom 93% were truck drivers). Truck drivers supported the supply chain throughout COVID-19 disruptions, yet in doing so, may have inadvertently facilitated additional SARS-CoV-2 importations from the USA. International arrivals of Canadians and non-residents in 2020 were low, but not negligible, with 14.6M and 5.1M arrivals respectively in 2020 (Statistics Canada, 2021a). While essential workers were exempt from quarantine, all others were mandated a self-enforced 14-day quarantine upon re-entry. It was not until 22 February 2021 that a 3-day hotel quarantine was made mandatory for international arrivals by air (Government of Canada, 2021).”

p. 16, ll. 11-14: Ideally these nonstationary sampling probabilities by province (and really for all countries) would be built directly into the likelihood.

We agree with the reviewer that it would be interesting to incorporate sampling probabilities or proportions into either the likelihood of the internal node state or into the importation rates derived from them. We considered this, however decided against it for the following reasons. Pragmatically, the software used for ancestral character estimation is not flexible towards specifying priors on migration rates. We considered adjusting importation rates post hoc with the time-varying sampling probabilities by province and by country of origin, but doing so is subject to the same biases as our previous extrapolation of the upper estimate of the total number of introductions. We do not know the extent of travel bias or outbreak bias in the sequences, nor the case ascertainment rate, therefore we cannot simply assume that sequences were a random sample nor that importation rates scale linearly with available sequences. We will consider this suggestion for a future analysis using a software that can better accommodate extensions to the likelihood function.

Reviewer #2 (Recommendations for the authors):

1. Line 17-21 argue genomic epidemiology is useful to track and characterize VOI/VOC, but the sequences analyzed in this manuscript are largely pre-VOC so I think it makes sense to re-focus and expand this discussion to include commentary on the importance of evaluating the success of public health interventions, travel restrictions, etc.

We thank the reviewer for this recommendation and have changed the introduction to focus more on the precedence and importance of evaluating effectiveness of public health interventions.

“Characterization of viral importations over time can also clarify the effectiveness of public health interventions by associating inflection points in importations with drastic changes in policies such as international travel restrictions (Magalis et al., 2020). […] Evaluating travel restrictions’ effectiveness towards reducing burden could inform the stringency and timing of future public health interventions.”

The discussion also elaborated upon pragmatic policy changes that could be informed by these evaluations:

“The burden of cases in the second wave was primarily due to sublineages introduced in the summer of 2020, despite a low number of importations and case burden at that time. […] These advanced planning steps also serve to minimize the economic harm due to work absenteeism and supply chain disruption.”

2. I think outlining a hypothesis at the end of the intro is useful, but as it is currently written the hypothesis is very long and difficult to understand. Can the authors restate and clarify the hypothesis into a single sentence?

Thank you for the suggestion. We have distilled the scope into a single hypothesis:

“We tested the hypothesis that international travel restrictions enacted in March 2020 effectively reduced international importations of SARS-CoV-2 into Canada, yet ongoing introductions contributed to COVID-19 persistence into early 2021, exacerbated by highly transmissible B.1.1.7 and other VOC sublineages.”

3. As the authors discuss, their results and those of all genomic epidemiological studies are very sensitive to sequence quality, availability, distribution, and sampling paradigms. They reduce the impact of unequal global sequencing efforts by subsampling global sequences proportionally to their monthly case counts, excluding Canadian sequences. I understand the rationale here, but worry that inclusion of all available Canadian sequences (which are not proportional to case count trends as is clear in Figure 1D and 1E) skewed their results. Perhaps one way to assess the robustness of the results would be to repeat the main analyses using a subsampled (proportion to case counts) Canadian dataset and compare results? Relatedly, even case counts could be skewed depending on testing availability and accuracy -- could the authors include a brief commentary on the availability, accessibility, and capacity of testing centers in Canada during the first year of the pandemic?

We fully agree with the reviewer and have undertaken sensitivity analyses to address these concerns, as cited in text above. Additionally, in regards to the potential bias towards identifying USA importations across subsampling strategies, we added the following remark.

“Although the USA was highly represented in all of our subsamples as a result of its large contribution to COVID-19 cases in 2020 and high sequence availability during early months, our results suggest a greater effect than due to sampling alone. On average, the USA sequences represented 28.9% (28.7 – 29.2%) of total international sequences, yet accounted for 46.3% (44.0 – 48.7%) of all sublineages and 57.7% (55.6 – 59.8%) of singletons. Upon maximizing the number of Canadian sequences in the analysis, where global sequence representation was more normalized but less comprehensive, the USA sequences represented fewer of the international sequences (25.8%, 25.6 – 26.1%) and still accounted for 38.4% (37.0 – 39.8%) of sublineages and 46.4% (44.6 – 48.3%) of singletons.”

– The Alberta and Maritimes outbreaks in spring of 2020 appear very striking (large outbreaks with lots of person-to-person spread and very few descendants) in Figure. However, this interpretation is fraught when you consider Figure 1E and 1D because there are lots of available sequences during this time compared to the total case count vs fall of 2020 where there is actually a higher case burden and very few available sequences. I think many readers will get hung up on this so maybe the authors could address it briefly in the text?

We agree that the relative availability of sequences in the first and second waves is a limitation for several provinces, particularly Alberta. In the updated analysis, there was an increased representation of the second wave, improving this issue somewhat.

4. Lines 6-10. I think it is a big leap to estimate the total possible introductions based on sequences representative of 1.1% of total cases.

We agree with the reviewer that this upper estimate required further consideration of travel-related bias, outbreak-related bias, and case ascertainment rates. Since these values are not known across provinces, we have removed this upper estimate and replaced it with a discussion of the type of data needed to extrapolate the true number of introductions.

“Low sequence representation can lead to underestimates of total introductions if neither index case or descendants were sampled, underestimates of sublineage size if not all descendants were sampled, and similarly, overestimates of the proportion of singletons, which may have been from unsampled transmission chains. […] Additionally, prospective cohort studies or seroprevalence studies would ameliorate our estimate of the case ascertainment fraction.”

5. It might be very informative to take a more careful look at rates of viral spread (R0) over time in addition to patterns of viral importation. This could help contextualize the effect of public health interventions on domestic spread. The authors bring up quarantine, contact tracing, testing, and individual behavior change in the discussion -- do the authors think the rate of international imports is the best measure to assess these interventions? I think R0 would be just as informative!

We thank the reviewer for pointing out that showing differences in domestic viral spread over time would strengthen the conclusions regarding the effectiveness of NPIs. In regards to what is the best measure of the effectiveness of travel restrictions, we maintain that looking at importations over time is paramount. We experimented with modelling the effective reproduction number over time using national and provincial incidence, as well as sublineage descendants sampled over time, however there was considerable uncertainty in these estimates due to uncertainty in the serial interval and when occurrences were infrequent. We are developing more refined methods of deconvoluting the incidence data to improve these estimates for a follow-up publication. Instead, to address the reviewer’s request for a more thorough characterization of domestic spread, we have added a figure showing the distribution of sampled Canadian descendants of key sublineages (Figure 2B) and respective subtrees (Figure 2—figure supplement 1).

6. I am skeptical the results on reductions in sublineage size, increasing tMRC, and perhaps even sources of domestic transmission will persist if the Canadian sequences are subsampled proportionally to case counts. Can the authors please assess the robustness of these results against a subsampled dataset?

We have undertaken a Canada sequence subsampling sensitivity analysis and evaluated the effects on sublineage size, TMRCA, and relative sources of domestic transmission analysis. Please see subsampling sensitivity analysis.

7. I agree with and appreciate the authors' discussion of the importance data sharing, including a limited metadata set to aid in the rapid interpretation and use of sequence data for important public health decisions.

We thank the reviewer for sharing our opinion of how data sharing augments our collective knowledge of the epidemic and better positions us to respond effectively.

8. Although this is a stylistic preference, I believe a cleaned up version of Figure 7 with the Canada's key public health interventions and COVID-19 outbreaks along with a map of Canada's provinces (already a supplemental) and case counts during the study period would be very useful to include as an early, primary figure.

Thank you – we agreed with the reviewer’s recommendation to move the summary of interventions, stringency, cases, and sequences to be the first primary figure, as it gives an overview of the epidemic and sets the stage for the analysis. We also added the Canadian map in place of the legend in Figure 1A to provide geographic context for provincial names.

Reviewer #3 (Recommendations for the authors):

As mentioned in the Public Review, my main concern lies on the explicit naming of sources of importation following the application of an epidemiologically-based approach to downsample the publicly available genome sequences from GISAID. While it is mentioned that ten subsamples were generated, it is not clear to me that the ancestral state reconstruction was evaluated on these ten subsamples and how the results compare between them. The uncertainty of the ASR itself was incorporated for the fixed phylogeny through an ML approach (ape::ace()), but I am missing the analysis of the robustness of the results across different subsamples.

We thank the reviewer for noting that we could better show the uncertainty across bootstraps. All of the reported estimates in the Results are the mean across ten bootstraps. Including uncertainty in every figure was not feasible as there were already many features in the plot, which we did not want to detract attention from. In the subsampling sensitivity analysis undertaken, the uncertainty across bootstraps for each subsampling strategy was shown more explicitly.

Furthermore, even if these analyses where performed, it is unclear whether a different subsampling approach could lead to the identification of a different profile of source countries, and if so, which subsampling approach would be more accurate. The accuracy of the identified countries of origin (and therefore accuracy of the proposed downsampling approach) would be validated if a complementary data source showed similar patterns: an expected importation index (EII ~ Incidence * no. of travellers to Canada), self-reported travel histories of patients with confirmed COVID-19, etc. If these complementary data sources are unavailable, I would recommend reducing the emphasis on the identification of countries of origin (or constraining it to regions where you're confident that the monthly reported cases are reliable – the USA would be an obvious example of this), or adding a clear discussion on these limitations. I would argue that this goes beyond the already mentioned idea that downsampling does not allow to increase the numbers of sequences from some unsampled times/locations (page 16, lines 5-20), and that it can also reduce the probability of phylogenetically identifying true importations from countries with low case prevalence.

On other topics: I am unsure about the estimation of total number of introductions presented in page 8 (lines 6-10). Do you have any particular reason why you would expect that importations scale linearly with sampling/representation? Given the overall dynamics, wouldn't it be more probable that higher sampling intensities would add unsampled sequences to the larger sublineages in the country (proportional to the size of the lineages) as well as uncovering unsampled importations? The rationale behind these calculations requires some more detailed explanations.

We agree with the reviewer that the estimated total number of introductions was too simplistic and rife with assumptions. We have omitted this upper estimate and expanded upon why this is challenging to estimate in the discussion.

“Low sequence representation can lead to underestimates of total introductions if neither index case or descendants were sampled, underestimates of sublineage size if not all descendants were sampled, and similarly, overestimates of the proportion of singletons, which may have been from unsampled transmission chains. […] Additionally, prospective cohort studies or seroprevalence studies would ameliorate our estimate of the case ascertainment fraction.”

Regarding the domestic transmission, I was wondering if you had access to finer geographical resolution beyond province-level? Provinces are particularly large geographical areas, and providing some insights (rather than a full, high-resolution phylogeographic re-analysis of all the data) into the sampling within provinces could add to the manuscript. This is particularly the case for the analysis of international importations to Quebec and Ontario: where these sublineages first detected in large urban areas or not necessarily? Is the sampling within provinces representative of the cases reported in urban and rural areas? Are there any differential dynamics between larger urban areas and more remote locations?

Unfortunately, we did not have access to geography metadata for Canadian sequences at a finer scale than province. This information is not available in the public domain presumably for confidentiality reasons. Our group has collaborated with the BCCDC to model importations and transmission among the five health authorities in BC. We have also worked with Ontario public health colleagues to model relative contributions of international transmission using sequences available from over a dozen provincial health regions as part of an extensive sampling effort on 24 November 2020 for a point prevalence study. The manuscript for this work is still in preparation and we will take your suggestions into consideration therein, stratifying based on rural or urban geographic landscape. I would expect that generally urban centers act as major sources of viral transmission (based on density and travel proclivity) to more remote locations. However, we are not able to comment on this dynamic based on the data available in this study.

Finally, it is quite interesting to see that the peak of TMRCAs nicely coincide with the maximum stringency on March 21 (Figure 4A,B) – even more so given that the TMRCA of a sublineage is more likely representing something akin to the first transmission event within said sublineage rather than the importation event per se. It would be interesting if the TMRCAs could be contextualised to show their distribution after accounting for sampling lag (i.e., the time between symptom onset and sample collection). This could be estimated from sequence metadata or approximated from non-genomic epidemiological data, but it would give a better idea of the times during which the infections that lead to these sublineages started.

We thank the reviewer for distinguishing sublineage importation from TMRCAs. While we considered your suggestion to estimate and correct for sampling lag, we were hesitant to apply a fixed value or randomly draw from a distribution of lags in light of unknown variability described in the discussion below. Rather, we clarified the interpretation of sublineage TMRCAs.

“Sublineage TMRCAs typically lag importation by several days, but could precede them if multiple infections with subsequently sampled descendants occurred during travel. While accounting for sampling lag between symptoms and sample collection would get closer to the importation date, this assumes symptom onset occurred during travel, which might not always be the case. In contrast, if we assume that infected travellers who sparked domestic transmission chains were tested upon or near after arrival, then this lag would be zero to a couple days. If an infectious traveller was not sampled, but gave rise to an outbreak that was later sampled, then an additional lag must be considered regarding the length and number generations of unsampled viral predecessors since importation. A TMRCA could precede importation if the sublineage index case infected multiple people during travel. Where travel history is available, incorporating internal nodes or branch states for travel location would help to inform the timing as well as geographic origins of importations (Hong et al., 2021). In light of these additional sources of uncertainty, we abstained from applying an importation lag to TMRCAs, and emphasize their interpretation as the estimated time of the recent common ancestor of all sampled descendants.”

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been dramatically improved---the sensitivity analyses are immensely helpful---but there are some remaining issues that need to be addressed, as outlined below. The reviewing editor (also reviewer 1) is concerned that some of the policy suggestions do not follow the evidence presented.

We are highly grateful to the Senior Editor and Reviewing Editor for their comments and suggestions, which we have addressed below.

1. Abstract: "… suggesting travel restrictions and quarantine must be sustained to fully curtail COVID-19 burden." I am guessing most readers will likely interpret "to fully curtail COVID-19 burden" as to get the burden near zero. Is this what the authors intended? It affects the logic. The results here show that travel restrictions were usually associated with at least several importations per week. Mathematically, we know that the burden of COVID-19 does not really depend on the number of importations if R ~ 1 or more---all you need is at least one endemic lineage to maintain the prevalence/burden dictated by R. Maybe the authors are referring to conditions when R<1 and travel restrictions are even greater than what they have been historically, but this is unclear. It is also unclear precisely what the authors mean by "quarantine." Usually, it refers to the isolation of contacts of potential cases (not the cases themselves), and sometimes it refers more generally to lockdowns. Neither policy seems particularly worth calling out here. Why not say "sustained interventions to lower transmission (R<1)"? It seems unjustified for this study to single out quarantine (either def) as the necessary complementary intervention when so many other approaches have been shown more effective. I'm dwelling on the wording because these kinds of conclusions, especially in an abstract, can have an outsized impact on policy.

We thank the reviewer for identifying this statement as problematic. We have changed the wording as follows:

“Despite the drastic reduction in viral importations following travel restrictions, newly seeded sublineages in summer and fall 2020 contributed greatly to the persistence of COVID-19 cases in the second wave, highlighting the importance of sustained interventions to reduce transmission.”

2. Abstract: "…restrictions that reduce the probability of importations are most effective during periods of low domestic prevalence and low or waning immunity." Waning immunity means R is rising, which, all things equal reduces extinction probabilities and increases the success of invading lineages (e.g., Otto and Whitlock 1997). Without more precision about R and how effectiveness is being defined, it's not clear restrictions should be more impactful under these conditions. I do see the positive correlation between "periods of waning immunity" and possible ban-assisted extinction, but only because immunity is most often waning when there has recently been an epidemic, and the recently boosted immune protection is relatively high. I worry again about the implications of these claims, which are not exactly supported by the research here.

We agree that we have extrapolated our claims beyond what was supported by our findings, where we did not explicitly model changes in population-level immunity. This sentence has been modified accordingly:

“Although viral importations are nearly inevitable when global prevalence is high, with fewer importations there are fewer opportunities for novel variants to spark outbreaks in new communities or outcompete previously circulating lineages.”

3. Relatedly, the main text describes evaluating the relative contribution of (reducing) importations to (reducing) *burden* (e.g., ll. 190-192, ll. 605-607, 691-701), but this analysis is not really performed here. Such analysis would require careful estimation of R and prevalence and a good enough transmission model to evaluate extinction probabilities and outbreak sizes (in the case of R<1) over time, and it would have to account for clonal interference. Put differently, the fact that imported lineages caused x% of cases does not mean that the case count would've been (100-x)% without those importations. The fact that imported lineages displaced resident lineages does not mean the resident lineages could not have caused comparable infections had the imported lineages been stopped at the border, assuming the imported lineages had no fitness differences. Imperfect travel bans that allow some lineages to invade can at best slightly (at measured rates) change the timing of an epidemic (ll. 704-707), but usually not its size or severity. The authors write, "Although blind travel bans may not be beneficial from a socioeconomic perspective, ultimately governments need to protect citizens and dynamic travel bans are one of the few tools available." This is surprising considering the number of broader tools at our disposal (e.g., rapid testing, vaccination, ventilation/filtration) that can slow importations AND endemic spread, as well as the observed ineffectiveness of travel bans at stopping spread outside of islands and China. The authors IMO have not demonstrated how much bans could delay importation and allow time to "plan", or if investments to reduce transmission generally are more effective. I suggest these claims be carefully reconsidered.

I apologize for the wordiness. I would be happy to bolster these points (maybe with math) if they are unclear. I am concerned that too much is being extrapolated about the possible and actual epidemiological impacts of travel bans from the measured rates of importation.

We appreciate the reviewer’s perspective on the semantics and interpretation of our findings. We have not asserted that the case count correlates directly with importations, but that each importation increases the chance of seeding a new community and the chance of introducing a more transmissible variant. Wording has been adjusted as follows:

ll. 190-192: “Evaluating travel restrictions’ effectiveness towards reducing importations could inform the stringency and timing of future public health interventions.”

ll. 605-607: “A more rapid and stringent public health response would have reduced the initial burden by preventing early sublineages from establishing widespread transmission chains that persisted throughout the first and second waves.”

ll. 691-705: “Case burden in the second wave was primarily due to sublineages introduced in the summer of 2020 amid low prevalence, despite a low number of importations. This suggests that ongoing seeding events impacted viral persistence prior to the arrival of VOCs with increased transmissibility. Considering the global and temporal diversity of the SARS-CoV-2 pandemic, it is difficult to predict what variant may be introduced and subsequently disperse through a local population. While broad and longstanding restrictions against non-essential international travel is not necessarily an advisable policy in light of economic impacts, swift and stringent travel bans towards locations harbouring a high frequency of a VOC not yet identified domestically should be seriously considered to reduce the probability of multiple, simultaneous outbreaks being seeded and potentially overwhelming the healthcare system.”

ll. 704-707: “Dynamic travel bans can be detrimental from a socioeconomic perspective; therefore their implementation must be weighed relative to other non-pharmaceutical interventions to reduce transmission.”

You may wish to consider some suggestions to improve readability from another reviewer:

4. In line 424, I'm not entirely sure what the authors mean by "the first wave of singletons had a higher amplitude than the second wave". By 'amplitude', do they refer to the range of dates when singletons were identified between both waves? Is this relative to a feature of the importations of sublineages (given that this sentence talks about this comparison between sublineage and singleton dynamics)? Some clarification would be helpful.

Thank you for seeking clarification on the wording here. By amplitude, we meant the wave height of the weekly singletons introduction rate, or the total average number of singletons introduced per week. Wording changed as follows:

“Importation dynamics for singletons mostly mirrored sublineage trends (Figure 4—figure supplement 1; Figure 3—figure supplement 1), although the USA contributed relatively more towards singletons than sublineages in both waves, and the maximum singleton importation rate was higher in the first wave than the second wave.”

5. The Discussion addresses a lot of the reviewer comments thoroughly and systematically, which was very helpful, but could be streamlined for the readers. I think that, for example, the two paragraphs that go from line 711 to line 741 could be reduced to a single paragraph discussing 1. sampling bias in phylogeography, 2. the specific effects of overrepresentation of some locations and how they addressed it with subsampling and the sensitivity analyses, 3. effects of upsampled lineages and under-sampled locations and how this can theoretically be overcome, 4. the broader challenges of scaling up Bayesian phylogenetics (this last point could be a separate paragraph, but unrelated to sampling bias).

We are grateful for this feedback towards improving the flow of the Discussion and have modified the text as follows:

“Ancestral trait reconstruction is sensitive to sampling bias as ancestral node states are more likely to reflect states predominant among tips (Baele et al., 2016; Hill et al., 2021; Lemey et al., 2009). […] Stratified analyses of well-supported (sub)lineages in future analyses will facilitate direct comparisons of Bayesian and maximum likelihood phylogeography methodologies towards estimating importation rates.”

https://doi.org/10.7554/eLife.73896.sa2

Article and author information

Author details

  1. Angela McLaughlin

    1. British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
    2. Bioinformatics, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing, AM conceived of the project, conducted most of the data cleaning, analysis, and visualization, and wrote the original draft of the manuscript
    For correspondence
    amclaughlin@bccfe.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5606-9080
  2. Vincent Montoya

    British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
    Contribution
    Data curation, Formal analysis, Methodology, Writing – review and editing, VM helped to develop methodologies for data cleaning, alignment, and tree building
    Competing interests
    No competing interests declared
  3. Rachel L Miller

    1. British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
    2. Bioinformatics, University of British Columbia, Vancouver, Canada
    Contribution
    Visualization, Writing – review and editing, RLM contributed towards improving the graphics
    Competing interests
    No competing interests declared
  4. Gideon J Mordecai

    Department of Medicine, University of British Columbia, Vancouver, Canada
    Contribution
    Visualization, Methodology, Writing – review and editing, GJM provided feedback on the phylogeographic methods and graphics, as well as provided citations
    Competing interests
    No competing interests declared
  5. Canadian COVID-19 Genomics Network (CanCOGen) Consortium

    Contribution
    Funding acquisition, Writing – review and editing, Initial feedback on earlier versions of the analysis
    Competing interests
    No competing interests declared
    Additional information
    See supplementary materials for list of consortium members and affiliations.
  6. Michael Worobey

    Department of Ecology and Evolution, University of Arizona, Tucson, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Visualization, Methodology, Writing – review and editing, MW contributed suggestions on estimating uncertainty, visualizations, and interpretations
    Competing interests
    No competing interests declared
  7. Art FY Poon

    Department of Pathology and Laboratory Medicine, Western University, London, Canada
    Contribution
    Data curation, Supervision, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3779-154X
  8. Jeffrey B Joy

    1. British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
    2. Bioinformatics, University of British Columbia, Vancouver, Canada
    3. Department of Medicine, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Resources, Data curation, Software, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing, JBJ contributed to refining the research questions
    For correspondence
    jjoy@bccfe.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7013-1482

Funding

Canadian Institutes for Health Research (Canada Graduate Scholarship Doctoral Awards)

  • Angela McLaughlin

National Sciences and Engineering Research Council of Canada (CREATE Award)

  • Angela McLaughlin
  • Rachel L Miller

Liber Ero Foundation (Fellowship)

  • Gideon J Mordecai

David and Lucile Packard Foundation (Grant)

  • Michael Worobey

Canadian Institutes for Health Research (Project Grant PJT-156178)

  • Art FY Poon

Genome Canada (BCB 287PHY Grant)

  • Jeffrey B Joy

Canadian Institutes for Health Research (Coronavirus Rapid Response ProgrammeCoronavirus Rapid Response Operating Grant 440371)

  • Jeffrey B Joy

Canadian Institutes for Health Research (Variant of Concern Supplement Grant)

  • Jeffrey B Joy

British Columbia Centre for Excellence in HIV/AIDS (Funding)

  • Angela McLaughlin
  • Vincent Montoya
  • Rachel L Miller
  • Jeffrey B Joy

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are very grateful to all the contributing and submitting laboratories within Canada and internationally that generously deposited sequences and metadata on GISAID (Supplementary file 1). We are especially thankful to the members of the Canadian COVID-19 Genomics Network (CanCOGeN) Consortium (Supplementary file 2) and the Canadian Public Health Laboratory Network (CPHLN) for their contributions towards publicly available data. Funding Statement: AM was supported by a Canadian Institutes for Health Research (CIHR) Doctoral grant and a Natural Sciences and Engineering Research Council of Canada (NSERC) CREATE scholarship. RLM was supported by an NSERC CREATE scholarship. GM was supported by the Liber Ero Fellowship Programme. MW was supported by the David and Lucile Packard Foundation. AFYP was supported by a CIHR Project Grant PJT-156178. JBJ was supported by Genome Canada BCB 287PHY grant, an operating grant from the CIHR Coronavirus Rapid Response Programme number 440371, and a CIHR variant of concern supplement. The British Columbia Centre for Excellence in HIV/AIDS also provided support.

Senior Editor

  1. Sara L Sawyer, University of Colorado Boulder, United States

Reviewing Editor

  1. Sarah E Cobey, University of Chicago, United States

Reviewer

  1. Bernardo Gutierrez

Publication history

  1. Preprint posted: April 13, 2021 (view preprint)
  2. Received: September 15, 2021
  3. Accepted: July 12, 2022
  4. Version of Record published: August 2, 2022 (version 1)

Copyright

© 2022, McLaughlin et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Angela McLaughlin
  2. Vincent Montoya
  3. Rachel L Miller
  4. Gideon J Mordecai
  5. Canadian COVID-19 Genomics Network (CanCOGen) Consortium
  6. Michael Worobey
  7. Art FY Poon
  8. Jeffrey B Joy
(2022)
Genomic epidemiology of the first two waves of SARS-CoV-2 in Canada
eLife 11:e73896.
https://doi.org/10.7554/eLife.73896
  1. Further reading

Further reading

    1. Epidemiology and Global Health
    2. Evolutionary Biology
    Theo Sanderson
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    The COVID-19 pandemic has resulted in a step change in the scale of sequencing data, with more genomes of SARS-CoV-2 having been sequenced than any other organism on earth. These sequences reveal key insights when represented as a phylogenetic tree, which captures the evolutionary history of the virus, and allows the identification of transmission events and the emergence of new variants. However, existing web-based tools for exploring phylogenies do not scale to the size of datasets now available for SARS-CoV-2. We have developed Taxonium, a new tool that uses WebGL to allow the exploration of trees with tens of millions of nodes in the browser for the first time. Taxonium links each node to associated metadata and supports mutation-annotated trees, which are able to capture all known genetic variation in a dataset. It can either be run entirely locally in the browser, from a server-based backend, or as a desktop application. We describe insights that analysing a tree of five million sequences can provide into SARS-CoV-2 evolution, and provide a tool at cov2tree.org for exploring a public tree of more than five million SARS-CoV-2 sequences. Taxonium can be applied to any tree, and is available at taxonium.org, with source code at github.com/theosanderson/taxonium.

    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Amy L Roberts, Alessandro Morea ... Kerrin S Small
    Research Article

    Background:

    Ageing is a heterogenous process characterised by cellular and molecular hallmarks, including changes to haematopoietic stem cells and is a primary risk factor for chronic diseases. X chromosome inactivation (XCI) randomly transcriptionally silences either the maternal or paternal X in each cell of 46, XX females to balance the gene expression with 46, XY males. Age acquired XCI-skew describes the preferential selection of cells across a tissue resulting in an imbalance of XCI, which is particularly prevalent in blood tissues of ageing females, and yet its clinical consequences are unknown.

    Methods:

    We assayed XCI in 1575 females from the TwinsUK population cohort using DNA extracted from whole blood. We employed prospective, cross-sectional, and intra-twin study designs to characterise the relationship of XCI-skew with molecular and cellular measures of ageing, cardiovascular disease risk, and cancer diagnosis.

    Results:

    We demonstrate that XCI-skew is independent of traditional markers of biological ageing and is associated with a haematopoietic bias towards the myeloid lineage. Using an atherosclerotic cardiovascular disease risk score, which captures traditional risk factors, XCI-skew is associated with an increased cardiovascular disease risk both cross-sectionally and within XCI-skew discordant twin pairs. In a prospective 10 year follow-up study, XCI-skew is predictive of future cancer incidence.

    Conclusions:

    Our study demonstrates that age acquired XCI-skew captures changes to the haematopoietic stem cell population and has clinical potential as a unique biomarker of chronic disease risk.

    Funding:

    KSS acknowledges funding from the Medical Research Council [MR/M004422/1 and MR/R023131/1]. JTB acknowledges funding from the ESRC [ES/N000404/1]. MM acknowledges funding from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.