Epidemics 37 (2021) 100480
Available online 9 August 2021
1755-4365/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Quantication of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland
Chaoran Chen
a
,
b
, Sarah Ann Nadeau
a
,
b
,
1
, Ivan Topolsky
a
,
b
,
1
, Marc Manceau
a
,
b
,
1
,
Jana S. Huisman
a
,
b
,
c
,
1
, Kim Philipp Jablonski
a
,
b
, Lara Fuhrmann
a
,
b
, David Dreifuss
a
,
b
,
Katharina Jahn
a
,
b
, Christiane Beckmann
d
, Maurice Redondo
d
, Christoph Noppen
d
,
Lorenz Risch
e
, Martin Risch
e
, Nadia Wohlwend
e
, Sinem Kas
e
, Thomas Bodmer
e
,
Tim Roloff
b
,
f
,
g
, Madlen Stange
b
,
f
,
g
, Adrian Egli
f
,
g
, Isabella Eckerle
h
,
o
, Laurent Kaiser
h
,
p
,
q
,
Rebecca Denes
i
, Mirjam Feldkamp
i
, Ina Nissen
i
, Natascha Santacroce
i
, Elodie Burcklen
i
,
Catharine Aquino
j
, Andreia Cabral de Gouvea
j
, Maria Domenica Moccia
j
, Simon Grüter
j
,
Timothy Sykes
j
, Lennart Opitz
j
, Grifn White
j
, Laura Neff
j
, Doris Popovic
j
, Andrea Patrignani
j
,
Jay Tracy
j
, Ralph Schlapbach
j
, Emmanouil T. Dermitzakis
k
,
l
, Keith Harshman
k
,
m
,
n
,
Ioannis Xenarios
k
,
l
, Henri Pegeot
k
, Lorenzo Cerutti
k
, Deborah Penet
k
, Anthony Blin
k
,
Melyssa Elies
k
, Christian L. Althaus
m
, Christian Beisel
a
,
1
, Niko Beerenwinkel
a
,
b
,
1
,
Martin Ackermann
c
,
n
,
1
, Tanja Stadler
a
,
b
,
*
a
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
b
Swiss Institute of Bioinformatics, Switzerland
c
Department of Environmental Systems Science, ETH Zürich, Swiss Federal Institute of Technology, Zurich, Switzerland
d
Viollier AG, Allschwil, Switzerland
e
Dr Risch, Labormedizinisches Zentrum, Switzerland
f
Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
g
Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
h
Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
i
Genomic Facility Basel, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
j
Functional Genomics Center Zurich, ETH Zürich and University of Zurich, Zurich, Switzerland
k
Health 2030 Genome Center, Geneva, Switzerland
l
University of Geneva Medical School, Geneva, Switzerland
m
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
n
Department of Environmental Microbiology, Eawag, Dubendorf, Switzerland
o
Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
p
Division of Infectious Diseases, Geneva University Hospitals, Geneva, Switzerland
q
Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
ARTICLE INFO
Keywords:
Pandemic
SARS-CoV-2
COVID-19
B.1.1.7
Transmission advantage
ABSTRACT
Background: In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC)
which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant
was estimated to have a transmission tness advantage of around 40–80 % (Volz et al., 2021; Leung et al., 2021;
Davies et al., 2021).
Aim: This study aims to estimate the transmission tness advantage and the effective reproductive number of
B.1.1.7 through time based on data from Switzerland.
Methods: We generated whole genome sequences from 11.8 % of all conrmed SARS-CoV-2 cases in Switzerland
between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the
B.1.1.7 variant and quantify the variant’s transmission tness advantage on a national and a regional scale.
* Corresponding author at: Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
E-mail address: tanja.stadler@bsse.ethz.ch (T. Stadler).
1
These authors contributed equally to this work.
Contents lists available at ScienceDirect
Epidemics
journal homepage: www.elsevier.com/locate/epidemics
https://doi.org/10.1016/j.epidem.2021.100480
Received 1 April 2021; Received in revised form 30 May 2021; Accepted 15 June 2021
Epidemics 37 (2021) 100480
2
Results: We estimate B.1.1.7 had a transmission tness advantage of 43–52 % compared to the other variants
circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number
above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants.
Specically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07–1.41] from 01 January until 17
January 2021 and 1.18 [1.06–1.30] from 18 January until 01 March 2021 based on the whole genome
sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive
number was 1.14 [1.00–1.26] based on all conrmed cases. For reference, Switzerland applied more non-
pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on
01 March 2021.
Conclusion: The observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected
based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated
doubling time of around 2–3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.
1. Introduction
In mid-December 2020 a SARS-CoV-2 variant named B.1.1.7 was
rst reported as more transmissible than previously circulating strains
(Rambaut et al., 2020a; NERVTAG, 2021; Public Health England, 2021).
This variant, whose name comes from the pangolin lineage nomencla-
ture (Rambaut et al., 2020b) and which was rst identied in the UK,
carries the N501Y mutation in the spike protein which may increase
ACE2 receptor afnity (Starr et al., 2020). Within only a few months,
B.1.1.7 became the dominant variant in the UK epidemic.
Based on these rst reports, Switzerland began an intense effort to
detect and trace B.1.1.7 (Goncalves Cabecinhas et al., 2021). The rst
infections with B.1.1.7 in Switzerland were conrmed on 24 December
2020 and retrospective analyses identied B.1.1.7 in samples dating
back to October (Goncalves Cabecinhas et al., 2021). In total, 1370 in-
fections with B.1.1.7 were conrmed up to 05 February 2021 (Goncalves
Cabecinhas et al., 2021).
Observing a variant increase in frequency does not necessarily mean
it has a transmission tness advantage. For example, a variant named
20A.EU1 spread rapidly across Europe in summer 2020. However, data
suggests that extended travel and superspreading events caused that
spread, not a viral transmission tness advantage (Hodcroft et al.,
2020). Alternatively, partial immune escape may help a variant spread
compared to other variants.
In the case of B.1.1.7, Davies et al. (2021) concluded that the vari-
ant’s spread is poorly explained by a hypothesis of immune escape.
Instead, B.1.1.7’s rapid increase in frequency in many high-prevalence
regions across the UK in parallel is well-explained by a transmission
tness advantage (Davies et al., 2021). Indeed, several different analyses
based on UK data suggest B.1.1.7 has a transmission tness advantage
between 40 and 80 % (Volz et al., 2021; Leung et al., 2021; Davies et al.,
2021). Davies et al. (2021) also obtain similar estimates based on data
from Denmark. Finally, analysis of the spread of the N501Y mutation in
Switzerland suggests a similar transmission tness advantage (Trans-
mission of SARS-CoV, 2021).
In this study, we determine the frequency of B.1.1.7 through time in
Switzerland and calculate its transmission tness advantage based on
three different datasets. First, we generated whole-genome sequences
from randomly selected samples provided by the diagnostics company
Viollier AG. Second, we use data from the diagnostics company Dr Risch
AG which screens all their samples for B.1.1.7.
Finally, we use whole-genome sequences generated from all patients
who tested positive for SARSCoV-2 at the University Hospital Geneva
(HUG) with CT values below 32 beginning 23 December 2020. These
three datasets are differently representative of Switzerland at the na-
tional and regional levels. Based on these data, we quantied the
transmission tness advantage of B.1.1.7 for Switzerland as well as for
the seven Swiss economic regions (Grossregionen). We additionally
calculated the reproductive number for B.1.1.7 and show how the
number of B.1.1.7 infections developed through time.
The core plots presented here were regularly updated between mid-
January and mid-April 2021 on SARS-CoV (2021a) and, since then are
available on the CoV-Spectrum website (CoV-Spectrum, 2021). Some of
the results are additionally displayed on the Swiss National COVID-19
Science Task Force website as of May 2021 (SARS-CoV, 2021b). The
code and data used for this study are publicly available on Github
(Github et al., 2021). All sequences are available on GISAID (Elbe and
Buckland-Merrett, 2017) (section A.7).
2. Methods
2.1. Data
We analyze three different datasets. The primary dataset is a set of
whole-genome sequences generated from samples provided by Viollier
AG, a large Swiss diagnostics company that processes SARS-CoV2 sam-
ples from across Switzerland. Each week, a random selection of samples
from amongst all positive tests processed by the company were selected
for whole-genome sequencing. Each sample is associated with a test date
and the canton in which the test was performed. Whole-genome se-
quences were generated from selected samples according to the pro-
cedures described in the supplementary materials section A.1. We dene
a sequence to be a B.1.1.7 sample if at least 80 % of the lineagedening,
non-synonymous nucleotide changes according to Rambaut et al.
(2020a) are present.
The second dataset is daily counts of B.1.1.7 infections amongst tests
processed by Dr Risch AG, another Swiss diagnostics company. Each
count is associated with a test date but not a geographic location. The
screening procedures for B.1.1.7 used to generate these data are
described in the supplementary materials section A.2.
The third and nal dataset is a set of whole-genome sequences
generated from patients at the University Hospital Geneva (HUG).
Samples from all patients who tested positive there with a CT of below
32 were sent for whole-genome sequencing, which were generated ac-
cording to the procedures described in the supplementary materials
section A.1. As with the rst dataset, a sequence is dened to be a B.1.1.7
sample if at least 80 % of the lineage-dening, non-synonymous
nucleotide changes are present.
These three datasets differ in their size and geographic representa-
tion. The rst dataset includes 9772 sequences from the study period 14
December 2020 to 11 March 2021 (approximately 780 per week, status
current as of 23 March 2021). These data represent 5.3 % of the 184,165
conrmed infections in Switzerland during this period. The second
dataset includes 12,019 samples screened for B.1.1.7. Taken together,
these two datasets represent 11.8 % of all conrmed SARS-CoV-2 in-
fections in Switzerland during our study period. Finally, the third
dataset is specic to the Lake Geneva region. It covers most of the study
period, from 23 December 2020 to 04 March 2021, and includes 2074
sequences which represent 7% of all conrmed infections in the Lake
Geneva region during that period.
Regarding geographic location, the diagnostic companies Viollier AG
and Dr Risch AG both processes samples from all over Switzerland but
C. Chen et al.
Epidemics 37 (2021) 100480
3
the intensity varies across regions. The set of sequenced samples inherits
this uneven geographical distribution. For example, relative to the total
number of conrmed infections, the Viollier AG dataset includes over
eight times more sequenced infections from the region Nordwestschweiz
than from the region Ticino (Table S1). The Dr Risch AG data, on the
other hand, has much better coverage of Eastern Switzerland (Table S2).
In summary, these two national-level datasets differ in their geographic
biases.
In what follows, we analyze the frequency of identied B.1.1.7
samples per day in the three datasets and compare our results between
them. Both national-level datasets (Viollier AG and Dr Risch AG) are
used to generate estimates on the national level, and comparing between
them shows the effect of different geographic biases. Since only the
Viollier AG dataset is resolved at the regional level, all regional-level
estimates are based on this dataset. Finally, the HUG dataset specic
to the Lake Geneva region allows a more detailed view on B.1.1.7 spread
in this region and a validation of results generated using the other two
datasets.
2.2. Statistical inference
We t a logistic model to the frequency of B.1.1.7 samples per day to
estimate the logistic growth rate a and the sigmoid’s midpoint t
0
. From
that, we derive an estimate of the transmission tness advantage of
B.1.1.7 under a continuous (f
c
) and a discrete (f
d
) model. Each model
could plausibly describe the actual dynamics, so we present results from
both for comparison. Further, we estimate the reproductive number R
for the B.1.1.7 and non-B.1.1.7 infections. The mathematical derivations
are described in the supplementary materials in the sections A.3 and A.4.
Finally, we show the projected number of conrmed infections in the
future under the continuous model. We initialize the model on 01
January 2021 with the estimated number of B.1.1.7 and non-B.1.1.7
conrmed infections on that day. We assume a reproductive number
for the non-B.1.1.7 infections as estimated on the national level for 01
January-17 January 2021. Further, we assume that the expected gen-
eration time is 4.8 days and the tness advantage is the estimated f
c
for
the region and dataset of interest (Table 1).
3. Results
We estimate the logistic growth rate a and the sigmoid’s midpoint t
0
based on the two nationallevel datasets from Viollier AG and Dr Risch
AG (Table 1). Taking the estimates of both datasets together, we obtain a
growth rate a of 0.07− 0.09 per day for Switzerland. For each economic
region, the estimated uncertainty interval of a overlaps with the Swiss-
wide uncertainty. We have little data for two out of seven regions
(Ticino and Central Switzerland; <1100 sequences in total) resulting in
very wide uncertainty intervals. From the t
0
estimates, we observe that
the Lake Geneva region was about 2 weeks ahead of the rest of
Switzerland with respect to B.1.1.7 spread. This conrms estimates from
Transmission of SARS-CoV (2021). Our initial analyses of the data in
January 2021 projected that B.1.1.7 will become dominant in
Switzerland in March 2021. Indeed, our latest data points suggest a
frequency of B.1.1.7 in conrmed infections of around 80 % for 11
March 2021.
In Figs. 1 and 2, we graphically illustrate the logistic growth in fre-
quency of B.1.1.7 and show the daily data together with an estimate of
the proportion of B.1.1.7 under the logistic growth model.
As a validation of the logistic growth parameter estimates, we
additionally analyzed the third, Lake Geneva-specic dataset from HUG.
The estimates for the Lake Geneva region based on Viollier AG data
agree very well with these independent estimates based on HUG data
(Table 1 and Fig. S2).
Next, we estimate the reproductive number for B.1.1.7 and non-
B.1.1.7 infections on a national scale (Fig. 3). We note that non-
pharmaceutical interventions to combat SARS-CoV-2 spread in
Switzerland were strengthened on 18 January 2021 and then relaxed
on 01 March 2021. Between 01 January and 17 January 2021, the
reproductive number for B.1.1.7 was signicantly above 1 (Viollier AG
dataset: 1.24 [1.07–1.41], Dr Risch AG dataset: 1.46 [1.21–1.72]) while
the reproductive number of non-B.1.1.7 was below 1 (Viollier AG
dataset: 0.83 [0.65–1.00], Dr Risch AG dataset: 0.81 [0.67− 0.96]).
The reproductive number for B.1.1.7 calculated based on the Viollier
AG dataset did not drastically change after 18 January 2021 (1.18
[1.06–1.30]) and agrees well with the estimates based on the Dr Risch
AG dataset (1.15 [1.01,1.29]). The non-B.1.1.7 reproductive number
also did not drastically change after this timepoint (Viollier AG dataset:
0.80 [0.68− 0.91], Dr Risch AG dataset: 0.85 [0.76,0.93]).
As expected from assuming a constant B.1.1.7 transmission tness
advantage, the ratio of the reproductive numbers for B.1.1.7 and non-
B.1.1.7 based on the Viollier AG dataset was roughly constant
throughout January and February. However, based on the Dr Risch AG
dataset, the ratio of the reproductive numbers for B.1.1.7 and non-
B.1.1.7 unexpectedly dropped in January. We suspect a potential bias
in this dataset (such as preferential inclusion of B.1.1.7 in early January)
because all estimates for B.1.1.7 in early January agree except those
based on the Dr Risch AG dataset.
Our data does not allow us to estimate the variant-specic repro-
ductive number for March yet. However, for the time period 10 March-
16 March 2021, we can estimate the reproductive number based on all
conrmed infections (Real-time estimates, 2021) (1.14 [1.00–1.26]).
Since this estimate is based on conrmed cases from the second part of
March when we project around 90–95 % of all conrmed infections are
B.1.1.7, this estimate should only slightly underestimate the reproduc-
tive number of B.1.1.7. In summary, we estimate the reproductive
number for B.1.1.7 was above 1 since January 2021 while the repro-
ductive number for non-B.1.1.7 variants was below 1.
Table 1
Estimates of the growth rate a and the sigmoid’s midpoint t
0
(measured in days
after Dec. 14)as well as the transmission tness advantages f
d
and f
c
are reported.
In the f
c
calculation, the Swiss-wide estimate of the reproductive number for the
time period 01 January 2021-17 January 2021 is assumed for the R
c
. mismatch
of the total number of infections and the Viollier AG-based projections.
Region Dataset a t
0
f
d
= exp
(ag) − 1
f
c
= ag/R
c
Switzerland Viollier 0.08
[0.08;
0.09]
65
[64;
66]
0.49
[0.47;
0.52]
0.48
[0.46;
0.50]
Switzerland Risch 0.08
[0.07;
0.08]
69
[68;
70]
0.45
[0.43;
0.48]
0.45
[0.43;
0.47]
Central Switzerland Viollier 0.09
[0.07;
0.11]
73
[67;
79]
0.51
[0.37;
0.66]
0.50
[0.38;
0.62]
Espace Mittelland Viollier 0.07
[0.07;
0.08]
64
[62;
66]
0.41
[0.37;
0.45]
0.41
[0.38;
0.44]
Grossregion
Nordwestschweiz
Viollier 0.09
[0.08;
0.10]
67
[65;
68]
0.53
[0.48;
0.59]
0.52
[0.47;
0.56]
Grossregion Tessin Viollier 0.07
[0.04;
0.11]
77
[69;
86]
0.42
[0.20;
0.64]
0.43
[0.24;
0.61]
Grossregion Zurich Viollier 0.09
[0.08;
0.10]
68
[65;
70]
0.55
[0.47;
0.62]
0.53
[0.47;
0.58]
Lake Geneva region Viollier 0.10
[0.09;
0.11]
54
[52;
56]
0.61
[0.51;
0.71]
0.57
[0.50;
0.65]
Lake Geneva region HUG 0.09
[0.09;
0.10]
52
[51;
53]
0.56
[0.50;
0.62]
0.54
[0.49;
0.59]
Ostschweiz Viollier 0.10
[0.08;
0.11]
65
[62;
68]
0.58
[0.46;
0.69]
0.55
[0.46;
0.64]
C. Chen et al.
Epidemics 37 (2021) 100480
4
Next, we calculate the transmission tness advantage f
c
of B.1.1.7
under our continuous-time model using the average reproductive
number estimated between 01 January 2021− 17 January 2021.
Further, we calculate the transmission tness advantage f
d
under a
discrete-time model. In both cases, we assume a generation time g of 4.8
days (the same as the mean generation time used to estimate the
reproductive number). Table 1 shows the estimated tness values under
both methods. On a national level, we estimate a tness advantage of
43–52 % across methods and datasets. The regional estimates overlap
with this interval. We note that we use the national reproductive number
for the regional f
c
estimates. Since Ticino had a lower reproductive
number averaged over all variants (Real-time estimates, 2021), the f
c
for
Ticino may be an underestimate. Similarly, the Lake Geneva region had
a higher reproductive number so the f
c
for Lake Geneva may be an
overestimate.
Finally, we show the projected dynamics of the epidemic under the
continuous model using parameter values based on epidemic conditions
in the rst half of January (Figs. 4 and 5). We show how the number of
B.1.1.7 infections develops over time in blue and how the number of
non-B.1.1.7 infections develops in green. In particular, in January 2021,
the model projects a decline in overall infections due to a decline in non-
B.1.1.7 variants. However, the number of B.1.1.7 variants increases.
Under this model, the total number of infections will increase again
once B.1.1.7 becomes dominant. It is important to note that this simple
model is intended to highlight whether epidemic dynamics change
compared to early January 2021 due to B.1.1.7, and thus it assumes the
transmission dynamics are constant. In particular, the model does not
include effects of non-pharmaceutical interventions introduced on 18
January 2021 or removed on 01 March 2021, vaccination, immunity
after infection, and population heterogeneity.
In our projections, we assume that dynamics remained unchanged
since early January. In order to explore to what extend this assumption
Fig. 1. Logistic growth of frequency of B.1.1.7
in Switzerland. Green points are the empirical
proportions of B.1.1.7 for each day (i.e. number
of B.1.1.7 samples divided by the total number
of samples). Blue vertical lines are the esti-
mated 95 % uncertainty of this proportion for
each day, assuming a simple binomial sampling
and Wilson uncertainty intervals. A logistic
growth function t to the data from all of
Switzerland is shown in black with the 95 %
uncertainty interval of the proportions in gray
(i.e. p(t) from Eqn. 2 and 5 in the supplemen-
tary material section A.3).
Fig. 2. Logistic growth of frequency of B.1.1.7 in the seven economic regions of Switzerland. For details see legend of Fig. 1.
C. Chen et al.
Epidemics 37 (2021) 100480
5
Fig. 3. Estimates of the effective reproductive number R of the B.1.1.7 variant and non-B.1.1.7 variants. Results in the top row are based on Viollier data, and in the
bottom row based on Risch data. Within each panel, the top row shows the results of the continuously varying R estimation, and the bottom of the piecewise constant
R estimation. The left column shows the R estimates, whereas the right shows the ratio between R estimated for B.1.1.7 and R estimated for all nonB.1.1.7 variants.
The condence intervals for the R of non-B117 variants show a 7-day periodicity due to lower case reporting on weekends. The R value was allowed to change on 18
January 2021 in the statistical inference as measures were tightened on that day.
Fig. 4. Change in the number of B.1.1.7 vari-
ants and in the number of all cases through time
for Switzerland. Based on the average repro-
ductive number R
c
for Switzerland estimated
for the time period 01 January 2021-17
January 2021 (i.e. prior to the tightening of
measures on 18 January 2021) and the trans-
mission tness advantage f
c
for the same time
period, we plot the expected number of B.1.1.7
variants (blue) and the expected number of
non-B.1.1.7 variants (green) under the contin-
uous model. The model is initialized on Jan. 1
with the total number of cases and the esti-
mated number of B.1.1.7 cases on that day. This
model is compared to data: The dark green line
is the total number of conrmed cases (7-day
average). The dark blue line is the estimated
number of conrmed B.1.1.7 cases (7-day average); this number is the product of the total number of conrmed cases for a day by the proportion of the B.1.1.7
variant for that day. If the empirical data develops as the model, the dark blue line follows the upper end of the blue area and the dark green line follows the upper
end of the green area.
C. Chen et al.