Genomic epidemiology of a densely sampled COVID-19
outbreak in China
Lily Geidelberg,
1,
*
,§
Olivia Boyd,
1
David Jorgensen,
1
Igor Siveroni,
1,
**
Fabrı´cia F. Nascimento,
1
Robert Johnson,
1
Manon Ragonnet-Cronin,
1
Han Fu,
1
Haowei Wang,
1
Xiaoyue Xi,
2
Wei Chen,
3
Dehui Liu,
3
Yingying Chen,
3
Mengmeng Tian,
3
Wei Tan,
4
Junjie Zai,
5
Wanying Sun,
6
Jiandong Li,
6
Junhua Li,
6
Erik M. Volz
1,
*
,††
Xingguang Li
7,
*
,†,‡‡
and
Qing Nie
3,
*
,‡,‡‡
1
Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis,
Imperial College London, Norfolk Place W2 1PG, UK,
2
Department of Mathematics, Imperial College London,
London SW7 2AZ, UK,
3
Department of Microbiology, Weifang Center for Disease Control and Prevention,
Weifang 261061, China,
4
Department of Respiratory Medicine, Weifang People’s Hospital, Weifang 261061,
China,
5
Immunology Innovation Team, School of Medicine, Ningbo University, Ningbo 315211, China,
6
Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China and
7
Department of Hospital Office, The First People’s Hospital of Fangchenggang, Fangchenggang, 538021, China
*Corresponding author: l.geidelberg@imperial.ac.uk (LG); e.volz@imperial.ac.uk (EV); nieqing0454@163.com (QN); xingguanglee@hotmail.com (XL)
†
Present address: Department of Hospital Office, The First People’s Hospital of Fangchenggang, No. 23, Fangqin Road, Fangchenggang, 538021, China.
‡
Present address: Hubei Engineering Research Center of Viral Vector, Wuhan University of Bioengineering, Wuhan, 430415, China.
§
https://orcid.org/0000-0002-8057-1844
**https://orcid.org/0000-0003-2595-3062
††
https://orcid.org/0000-0001-6268-8937
‡‡
These authors contributed equally to this work.
Abstract
Analysis of genetic sequence data from the SARS-CoV-2 pandemic can provide insights into epidemic origins, worldwide
dispersal, and epidemiological history. With few exceptions, genomic epidemiological analysis has focused on geographi-
cally distributed data sets with few isolates in any given location. Here, we report an analysis of 20 whole SARS- CoV-2
genomes from a single relatively small and geographically constrained outbreak in Weifang, People’s Republic of China.
Using Bayesian model-based phylodynamic methods, we estimate a mean basic reproduction number (R
0
) of 3.4 (95% high-
est posterior density interval: 2.1–5.2) in Weifang, and a mean effective reproduction number (R
t)
that falls below 1 on 4
February. We further estimate the number of infections through time and compare these estimates to confirmed diagnoses
by the Weifang Centers for Disease Control. We find that these estimates are consistent with reported cases and there is un-
likely to be a large undiagnosed burden of infection over the period we studied.
V
C
The Author(s) 2021. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
1
Virus Evolution, 2021, 7(1): veaa102
doi: 10.1093/ve/veaa102
Research Article
Downloaded from https://academic.oup.com/ve/article/7/1/veaa102/6170691 by Imperial College London Library user on 29 March 2021
Key words: SARS-CoV-2; phylodynamics; phylogenetics; genetic epidemiology; structured coalescent; modelling.
1. Introduction
We report a genomic epidemiological analysis of one of the first
geographically concentrated community transmission samples
of SARS-CoV-2 genetic sequences collected outside of the initial
outbreak in Wuhan, China. These data comprise 20 whole-
genome sequences from confirmed COVID-19 cases in Weifang,
Shandong Province, People’s Republic of China. The data were
collected over the course of several weeks up to 10 February
2020, and overlap with a period of intensifying public health
and social distancing measures. These interventions included
public health messaging, establishing phone hot-lines, encour-
aging home isolation for recent visitors from Wuhan (January
23–26), optimising triage of suspected cases in hospitals
(January 24), travel restrictions (January 26), extending school
closures, and establishing ‘fever clinics’ for consultation and di-
agnosis (January 27) (Mao 2020). In contrast to the early spread
of COVID-19 in Hubei Province of China, most community trans-
missions within Weifang took place after these measures were
put in place.
Model-based phylodynamic methods have been previously
used to analyse sequence data from Wuhan and exported inter-
national cases (Volz et al. 2020). Using an adaptation of these
methods, and based on the local genetic data available, the ob-
jective of this study is to evaluate the growth rate and reproduc-
tion number in Weifang after seeding events that took place in
mid to late January, 2020. A secondary aim is to provide esti-
mates of the epidemiological trajectory of the Weifang outbreak
and comparing them to confirmed diagnosed COVID-19 cases
reported by Weifang Centers for Disease Control (CDC), to ex-
plore whether there was a significant unmeasured burden of in-
fection due to imperfect case ascertainment from mild or
asymptomatic illness.
2. Methods and materials
2.1 Epidemiological investigation, sampling and genetic
sequencing
As of 10 February 2020, 136 suspected cases and 214 close con-
tacts were diagnosed by Weifang Center for Disease Control and
Prevention; of these, 38 cases were confirmed positive with
SARS-CoV-2. The median age of patients was 36 (range: 6–75).
Two of twenty patients suffered severe or critical illness.
Viral RNA was extracted using the Maxwell 16 Viral Total
Nucleic Acid Purification Kit (Promega AS1150) with the mag-
netic bead method, and the RNeasy Mini Kit (QIAGEN 74104)
with the column method. Quantitative reverse transcription po-
lymerase chain reaction (RT-qPCR) was carried out using the
2019 novel coronavirus nucleic acid detection kit (BioGerm,
Shanghai, China) to confirm the presence of SARS-CoV-2 viral
RNA with cycle threshold (Ct) values ranging from 17 to 34, tar-
geting the highly conservative region (ORF1ab/N gene) in the
SARS-CoV-2 genome.
Concentration of RNA samples was measured by the Qubit
RNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA,
USA). The enzyme DNase was used to remove host DNA. The
remaining RNA was used to construct the single-stranded circu-
lar DNA library with the MGIEasy RNA Library preparation re-
agent set (MGI, Shenzhen, China). Purified RNA was then
fragmented. Using these short fragments as templates, random
hexamers were used to synthesise the first-strand cDNA and
then the second strand. Using the short double-strand DNA, a
DNA library was constructed through end repair, adaptor liga-
tion and PCR amplification. PCR products were transformed into
a single-strand circular DNA library through DNA-denaturation
and circularisation. DNA nanoballs (DNBs) were generated with
the single-strand circular DNA library by rolling circle replica-
tion. The DNBs were loaded into the flow cell and pair-end
100 bp sequencing was performed on DNBSEQ-T7 platform 8
(MGI, Shenzhen, China). Twenty genomes were assembled with
length from 26,840 to 29,882 nucleotides.
Total reads were first processed using Kraken v0.10.5 (de-
fault parameters) with a self-built database of Coronaviridae
genomes (including SARS, MERS, and SARS-CoV-2 genome
sequences downloaded from GISAID, NCBI, and CNGB) to iden-
tify Coronaviridae-like reads. To remove low-quality reads,
duplications and adaptor contaminations, fastp v0.19.5 (param-
eters: -q 20-u 20 -n 1 -l 50) and SOAPnuke v1.5.6 (parameters: -l
20 -q 0.2 -E 50 -n 0.02 -5 0 -Q 2 -G -d) were used. The
Coronaviridae-like reads of samples with <100 average se-
quencing depth were directly assembled de novo with SPAdes
v3.14.0 using default settings. The Coronaviridae-like reads of
samples with >100 average sequencing depth across the
SARS-CoV-2 genome were subsampled to achieve 100 se-
quencing depth before being assembled.
The 20 Weifang sequences have mean 1.1 per cent N content
and are deposited in GISAID (gisaid.org).
2.2 Mathematical model
The phylodynamic model is designed to account for 1, nonlin-
ear epidemic dynamics in Weifang with a realistic course of in-
fection (incubation and infectious periods), 2, variance in
transmission rates that can influence epidemic size estimates,
and 3, migration of lineages in and out of Weifang.
2.2.1 Nonlinear epidemiological dynamics in Weifang
The maximum number of daily confirmed COVID-19 cases oc-
curred on February 5, but it is unknown when the maximum
prevalence of infection occurred. To capture a nonlinear de-
crease in cases following epidemic peak, and to account for a re-
alistic distribution of generation times, we use an extension of
the susceptible- exposed-infectious-recovered (SEIR) model
(Keeling and Rohani 2011) for epidemic dynamics in Weifang,
shown in Equations (1–5).
2.2.2 Variance in transmission rates
To estimate total numbers infected, the phylodynamic model
must account for epidemiological variables which are known to
significantly influence genetic diversity (Lloyd-Smith et al.
2005). Foremost among these is the variance in offspring distri-
bution (number of transmissions per primary case). We draw on
previous evidence based on the previous SARS epidemic, which
indicates that the offspring distribution is highly over-
dispersed. High variance of transmission rates will reduce ge-
netic diversity of a sample and failure to account for this factor
will lead to highly biased estimates of epidemic size (Li et al.
2017). Recent analyses of sequence data drawn primarily from
Wuhan have found that high over-dispersion was required for
estimated cases to be consistent with the epidemiological
2|Virus Evolution , 2021, Vol. 7, No. 1
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record (Volz et al. 2020). Models assuming low variance in trans-
mission rates between people would generate estimates of
cases that are lower than the known number of confirmed
cases. Separately, Endo (2020) found that high over-dispersion
is required to reconcile estimated reproduction numbers with
the observed frequency of international outbreaks. We there-
fore elaborate the SEIR model with an additional compartment J
which has a higher transmission rate (s -fold higher) than the I
compartment.
The variance of the implied offspring distribution is cali-
brated to give a similar over-dispersion to that of the SARS epi-
demic. Upon leaving the incubation period, individuals progress
to the J compartment with probability p
h,
or otherwise to I. The
model is implemented as a system of ordinary differential
equations:
_
St
ðÞ
¼ bIt
ðÞ
þ bsJt
ðÞ
SðtÞ
St
ðÞ
þ Et
ðÞ
þ It
ðÞ
þ Jt
ðÞ
þ RðtÞ
1
_
Et
ðÞ
¼ bIt
ðÞ
þ bsJt
ðÞ
St
ðÞ
St
ðÞ
þ Et
ðÞ
þ It
ðÞ
þ Jt
ðÞ
þ Rt
ðÞ
c
0
Et
ðÞ
2
_
It
ðÞ
¼ c
0
1 p
h
ðÞ
Et
ðÞ
c
1
IðtÞ 3
_
Jt
ðÞ
¼ c
0
p
h
Et
ðÞ
c
1
JðtÞ 4
_
Rt
ðÞ
¼ c
1
It
ðÞ
þ Jt
ðÞðÞ
5
2.2.3 Importation of lineages from Wuhan
The outbreak in Weifang was seeded by multiple lineages
imported at various times from the rest of China. We therefore
account for location of sampling in our model. Migration is
modelled as a bi-directional process with rates proportional to
epidemic size in Weifang. The larger reservoir of COVID-19
cases outside of Weifang (Y (t)) serves as a source of new infec-
tions and is assumed to be growing exponentially (at rate q)
over this time period.
The equation governing this population is:
_
Yt
ðÞ
¼ q l
ðÞ
YðtÞ 6
Migration only depends on the size of variables in the
Weifang compartment and thus does not influence epidemic
dynamics; it will only influence the inferred probability that a
lineage resides within Weifang. For compartment X (E, I,orJ) , g
is the per-lineage rate of migration out of Weifang, and the total
rate of migration in and out of Weifang is gX.
2.2.4 Model fitting
During phylodynamic model fitting g, b and q are estimated.
Additionally, we estimate initial sizes of Y, E, and S. Initial val-
ues of I, J, and R are fixed at 0. Other parameters are fixed based
on prior information. We fix 1/c
0
¼ 4.1 days and 1/c
1
¼ 3.8 days
(Volz et al. 2020). We set p
h
¼ 0.20 and s ¼ 74 which yields a dis-
persion of the reproduction number that matches a negative bi-
nomial distribution with k ¼ 0.124 for any value of R
0
between 2
and 5. This dispersion is similar to values estimated for the 2003
SARS epidemic (Lloyd-Smith et al. 2005).
The phylodynamic model is illustrated in Fig. 1A as a flow-
chart. The SEIR model dynamics begin on 10 January.
It is important to note that the S compartment does not ex-
plicitly represent the number of susceptibles in Weifang, but
rather it is used as a simple parameterisation to permit R
t
to de-
crease, and for epidemic control to be achieved. An exploration
of this parameterisation is discussed in Supplementary
Information Section 4.1.
3. Phylogenetic analysis
Using, MAFFT (Katoh and Standley 2013), we aligned the 20
Weifang sequences with a previous alignment of 57 non-
identical SARS-CoV 2 sequences from outside of Weifang,
hereby the ’reference set’ (Volz et al. 2020). The reference set
was sampled from the GISAID database (Elbe and Buckland-
Merrett 2017) downloaded on June 7, 2020, and explicitly in-
cluded close genetic matches to sequences from Weifang. An
upper bound at 1 May on the date of sampling was placed. The
distribution of sample dates from inside and outside of Weifang
is shown in Fig. 1B. Of the 57 sequences in the reference set,
20 (35%) were sampled from China.
Maximum likelihood analysis was carried using IQTree
(Minh et al. 2019) with a HKYþG4 substitution model, and a
time-scaled tree was estimated using treedater 0.5.0 (Volz and
Frost 2017).
Bayesian phylogenetic analysis was carried out using BEAST
2.6.1 (Bouckaert et al. 2019) with a HKYþG4 substitution model
and a strict molecular clock. The phylodynamic model was
implemented using the PhyDyn package v1.3.7 (Volz and
Siveroni 2018) using the QL likelihood approximation and the
RK ODE solver. The model was fitted by running 8 MCMC chains
of 30 million steps in parallel, and combining chains after re-
moving 50 per cent burn-in. In order to demonstrate the added
utility of the sequence data, the analysis was repeated assum-
ing a constant likelihood, that is sampling only from the prior
probability distributions.
A
B
Figure 1. Epidemiological model and sample times. (A) A diagram representing
the structure of the epidemiological SEIR model which was fitted in tandem
with the time scaled phylogeny. Infected and infectious individuals may occupy
a low (I) or a high (J) transmission rate state to account for high dispersion of the
reproduction number. (B) Sampling density of sequences from inside (yellow)
and outside (grey) of Weifang respectively through time.
L. Geidelberg et al. | 3
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The ggtree package was used for all phylogeny visualisations
(Yu et al. 2017).
Code to replicate this analysis and BEAST XML files can be
found at https://github.com/emvolz/weifang-sarscov2.
4. Results
Despite an initial rapid increase in confirmed cases in Weifang
in late January and early February, the number of confirmed
cases by Weifang CDC show that the outbreak peaked early and
the maximum number of cases occurred on 5 February.
Phylodynamic analysis supports the interpretation that control
efforts reduced epidemic growth rates and contributed to even-
tual control. Estimates of the epidemiological parameters are
summarised in Table 1.
The estimated cumulative and daily number of infections
are shown in Fig. 2A and B, respectively. We estimate the peak
of daily infections in late January, preceding the time series of
confirmed cases by about a week; this is expected due to delays
from infection to appearance of symptoms and delays from
symptoms to diagnosis. The genetic data are strongly informa-
tive about timing and size of the epidemic peak: trajectories
sampled from the Bayesian prior distribution have a smaller
and later epidemic peak (c.f. Fig. 2) with much less precision.
Our central estimate for the cumulative number infected on 10
February is 365 (highest posterior density (HPD) 102–1174), com-
pared with 38 cumulative confirmed cases. We therefore esti-
mate that around 10 per cent of infections were diagnosed
(Supplementary Fig. S5), an unknown proportion of infections
will be missed by the surveillance system due to very mild, sub-
clinical or asymptomatic infection. This supports the hypothe-
sis that there was a modest (but not large) burden of infection in
Weifang over the period that the sequence data were sampled.
Effective reproduction number over time is shown in Fig. 2C.
We estimate R
0
¼ 3.4 (95% HPD: 2.1–5.2) and the initial growth
rate in cases was approximately 22 per cent per day, consistent
with those estimated in other settings and during the early epi-
demic in Wuhan (Alimohamadi et al. 2020). Sampling from the
prior yields a much higher estimate for R
0
with an unrealistic
HPD upper bound over 10. We detect a significant decrease in ef-
fective reproduction number as the epidemic progressed, during
a period (late January) when Weifang was implementing a vari-
ety of public health interventions and contact tracing to limit
epidemic spread. Our central estimate of R
t
drops below 1 on
the 4th of February.
Although previous studies have shown the significance of
realistic modelling for fidelity of phylogenetic inference (M€oller
et al. 2018), our analysis has found that the phylodynamic prior
did not greatly influence estimated molecular clock rate or in-
ferred time to most recent common ancestors (TMRCAs). This is
likely due to our choice of reference sequence set, which com-
prised sequences spanning several months of the epidemic,
and therefore reflecting a range of transmission dynamics.
In this analysis, there is a mean of three pairwise differences
among sequences from Weifang; the corresponding number
among the sequences outside of Weifang is eight.
Figure 3 shows the estimated time-scaled maximum clade
credibility (MCC) tree including 20 lineages sampled from dis-
tinct patients in Weifang and 57 genomes sampled from Wuhan
and internationally.
There is correspondingly low confidence in tree topology
(Supplementary Fig. S3), and only two monophyletic Weifang
clades had greater than 50 per cent posterior probability, neither
of which are larger than two samples.
The earliest Weifang sequence was sampled on 25 January
from a patient who first showed symptoms on 16 January.
These dates cover a similar range as the posterior TMRCA of all
Weifang sequences (Supplementary Fig. S4).
5. Discussion
Our analysis of 20 SARS-CoV-2 genomes has confirmed inde-
pendent observations regarding the rate of spread and burden
of infection in Weifang, China. Surveillance of COVID-19 is ren-
dered difficult by high proportions of illness with mild severity
and an unknown proportion of asymptomatic infection (Guan
2019). The extent of under-reporting and case ascertainment
rates has been widely debated. Analysis of genetic sequence
data provides an alternative source of information about epi-
demic size. We do not find evidence for a large hidden burden
of infection within Weifang, with an estimated total number of
cases around 365 (102–1174) at the date of last sample, towards
the end of the outbreak.
Our decreasing central estimate of R
t
over time, falling below
1 on 4 February, suggests a slower rate of spread outside of
Wuhan and effective control strategies implemented in late
January. It is consistent with a previous modelling study of
Shandong province (Zhang et al. 2020), which showed that R
t
fell below 1 on 29 January. Our posterior molecular clock rate
shown in Table 1 is consistent with previous estimates of SARS-
CoV-2 phylogenetic analyses (Nie et al. 2020).
The modest number of sequences from Weifang (twenty) is
a limitation of this study. However, this represents a significant
proportion of the total number of cases reported; there were
Table 1. Summary of primary epidemiological and evolutionary parameters, including Bayesian prior distributions and estimated posteriors.
Parameter Prior Posterior mean 95% HPD
Initial infected Exponential (mean ¼ 1) 4.8 1.3–10.1
Initial susceptible Exponential (mean ¼ 500) 550 117–1501
Migration rate
a
Exponential (mean ¼ 10) 1.68 1.03–1.99
Transmission rate Log-normal (mean log ¼ 3.21, SD log ¼ 0.5) 21.5 13.0–32.1
Reproduction number Log-normal (mean log ¼ 1.03, SD log ¼ 0.5) 3.4 2.1–5.2
Molecular clock rate
b
Uniform (0.0007,0.003) 0.0013 0.00098–0.0017
Transition/transversion Log-normal (mean log ¼ 1, SD log ¼ 1.25) 4.6 3.3–6.5
Gamma shape Exponential (mean ¼ 1) 0.29 0.0070–1.50
Posterior uncertainty is summarised using a 95 per cent HPD interval.
a
Units: Migrations per lineage per year.
b
Units: Substitutions per site per year.
4|Virus Evolution , 2021, Vol. 7, No. 1
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thirty-eight confirmed cases at the date of the last genetic sam-
ple (10 February), rising no further than forty-four from 16
February onwards (Fig. 2A). Despite relatively few sequences,
our estimated trajectories display uncertainties that are signifi-
cantly reduced and more realistic, compared with sampling
only from the prior.
Further, it is possible that the outbreak observed in Weifang
could be due not to community transmission, but rather multi-
ple importations. However, given that we sampled the reference
set from a GISAID database downloaded in June, it is reasonable
to assume close genetic matches would have been chosen. A
maximum-likelihood tree of the entire alignment
(Supplementary Fig. S1) shows that lineages from Weifang have
common ancestry with other Chinese lineages at two distinct
polytomies and the phylogeny alone gives no information about
location of these nodes (Weifang or exogenous). We therefore
conclude that the MCC in Fig. 3, which reflects significant clus-
tering of the Weifang samples, is reasonable.
Community transmission is further supported by the fact
that cases were identified via contact tracing. This forms an-
other limitation, as it suggests non-random sampling of cases
in Weifang. This could lead to an underestimate of the total
number of cases in Weifang. However, as a large proportion of
reported cases were included in this analysis, the bias is un-
likely to be too significant.
Finally, the SEIR model structure also presents some limita-
tions. As b has a constant value, R
t
can decrease only as a result
of depleting susceptibles. The decrease in R
t
is therefore a
Figure 2. Epidemiological trajectory of the Weifang SARS-CoV-2 epidemic in 2020 when fitting the SEIR model to genetic data (blue) and sampling only from prior
(grey). Solid lines and shaded area reflect posterior median and 95 per cent HPD. The vertical dashed line represents the date of the last sequence sampled in Weifang.
(A) Cumulative estimated infections through time compared with cumulative cases (yellow points) reported by Weifang CDC. (B) Daily estimated infections through
time compared to daily reported cases (yellow points). (C) Effective reproduction number through time R
t
. The horizontal dotted line indicates R
t
¼ 1.
L. Geidelberg et al. | 5
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