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SARS-CoV-2 reinfection trends in South Africa: analysis of routine surveillance data

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Objective: To examine whether SARS-CoV-2 reinfection risk has changed through time in South Africa, in the context of the emergence of the Beta and Delta variants Design: Retrospective analysis of routine epidemiological surveillance data Setting: Line list data on SARS-CoV-2 with specimen receipt dates between 04 March 2020 and 30 June 2021, collected through South Africa's National Notifiable Medical Conditions Surveillance System Participants: 1,551,655 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days prior to 30 June 2021. Individuals having sequential positive tests at least 90 days apart were considered to have suspected reinfections. Main outcome measures: Incidence of suspected reinfections through time; comparison of reinfection rates to the expectation under a null model (approach 1); empirical estimates of the time-varying hazards of infection and reinfection throughout the epidemic (approach 2) Results: 16,029 suspected reinfections were identified. The number of reinfections observed through the end of June 2021 is consistent with the null model of no change in reinfection risk (approach 1). Although increases in the hazard of primary infection were observed following the introduction of both the Beta and Delta variants, no corresponding increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the estimated hazard ratio for reinfection versus primary infection was lower during waves driven by the Beta and Delta variants than for the first wave (relative hazard ratio for wave 2 versus wave 1: 0.75 (95% CI: 0.59-0.97); for wave 3 versus wave 1: 0.70 (95% CI: 0.55-0.90)). Although this finding may be partially explained by changes in testing availability, it is also consistent with a scenario in which variants have increased transmissibility but little or no evasion of immunity. Conclusion: We conclude there is no population-wide epidemiological evidence of immune escape and recommend ongoing monitoring of these trends.
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SARS-CoV-2 reinfection trends in South Africa: analysis of
routine surveillance data
Juliet R.C. Pulliam1,*, Cari van Schalkwyk1, Nevashan Govender2, Anne von Gottberg2,3,
Cheryl Cohen2,4, Michelle J. Groome2,3, Jonathan Dushoff1,5, Koleka Mlisana6,7,8, Harry
Moultrie2
1 SACEMA, Stellenbosch University, South Africa
2 National Institute for Communicable Diseases, Division of the National Health Laboratory
Service, South Africa
3 School of Pathology, Faculty of Health Sciences, University of the Witwatersrand,
Johannesburg, South Africa
4 School of Public Health, Faculty of Health Sciences, University of the Witwatersrand,
Johannesburg, South Africa
5 McMaster University, Canada
6 National Health Laboratory Service, South Africa
7 School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, South
Africa
8 Centre for the AIDS Programme of Research in South Africa (CAPRISA), South Africa
* corresponding author: pulliam@sun.ac.za
Abstract
Objective To examine whether SARS-CoV-2 reinfection risk has changed through time in
South Africa, in the context of the emergence of the Beta and Delta variants
Design Retrospective analysis of routine epidemiological surveillance data
Setting Line list data on SARS-CoV-2 with specimen receipt dates between 04 March 2020
and 30 June 2021, collected through South Africa’s National Notifiable Medical Conditions
Surveillance System
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Participants 1,551,655 individuals with laboratory-confirmed SARS-CoV-2 who had a
positive test result at least 90 days prior to 30 June 2021. Individuals having sequential
positive tests at least 90 days apart were considered to have suspected reinfections.
Main outcome measures Incidence of suspected reinfections through time; comparison of
reinfection rates to the expectation under a null model (approach 1); empirical estimates of
the time-varying hazards of infection and reinfection throughout the epidemic (approach 2)
Results 16,029 suspected reinfections were identified. The number of reinfections
observed through the end of June 2021 is consistent with the null model of no change in
reinfection risk (approach 1). Although increases in the hazard of primary infection were
observed following the introduction of both the Beta and Delta variants, no corresponding
increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the
estimated hazard ratio for reinfection versus primary infection was lower during waves
driven by the Beta and Delta variants than for the first wave (relative hazard ratio for wave 2
versus wave 1: 0.75 (CI
!"
: 0.59-0.97); for wave 3 versus wave 1: 0.70 (CI
!"
: 0.55-0.90)).
Although this finding may be partially explained by changes in testing availability, it is also
consistent with a scenario in which variants have increased transmissibility but little or no
evasion of immunity.
Conclusion We conclude there is no population-wide epidemiological evidence of immune
escape and recommend ongoing monitoring of these trends.
Box 1
What is already known on this topic
Prior infection with SARS-CoV-2 is estimated to provide at least an 80% reduction in
infection risk (1,2).
Laboratory-based studies indicate reduced neutralization by convalescent serum for
the Beta and Delta variants relative to wild type virus (3–6); however, the impact of
these reductions on risk of reinfection is not known.
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What this study adds
We provide two methods for monitoring reinfection trends to identify signatures of
changes in reinfection risk.
We find no evidence of increased reinfection risk associated with circulation of Beta
or Delta variants compared to the ancestral strain in routine epidemiological data
from South Africa.
Introduction
As of 30 June 2021, South Africa had more than two million cumulative laboratory-
confirmed cases of SARS-CoV-2, concentrated in three waves of infection. The first case
was detected in early March 2020 and was followed by a wave that peaked in July 2020
and officially ended in September. The second wave, which peaked in January 2021 and
ended in February, was driven by the Beta (B.1.351 / 501Y.V2 / 20H) variant, which was
first detected in South Africa in October 2020 (7). The third wave, which peaked in July and
ended in September 2021, was dominated by the Delta (B.1.617.2 / 478K.V1 / 21A) variant
(8).
Following emergence of the Beta and Delta variants of SARS-CoV-2 in South Africa,
a key question remains of whether there is epidemiologic evidence of increased risk of
SARS-CoV-2 reinfection with these variants (i.e., immune escape). Laboratory-based
studies suggest that convalescent serum has a reduced neutralizing effect on these
variants compared to wild type virus in vitro (3–6); however, this finding does not
necessarily translate into immune escape at the population level.
To examine whether reinfection risk has changed through time, it is essential to
account for potential confounding factors affecting the incidence of reinfection: namely, the
changing force of infection experienced by all individuals in the population and the growing
number of individuals eligible for reinfection through time. These factors are tightly linked to
the timing of epidemic waves. We examine reinfection trends in South Africa using two
approaches that account for these factors to address the question of whether circulation of
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the Beta or Delta variants was associated increased reinfection risk, as would be expected
if their emergence was driven by immune escape.
Methods
Data sources
Data analysed in this study come from two sources maintained by the National Institute for
Communicable Diseases (NICD): the outbreak response component of the Notifiable
Medical Conditions Surveillance System (NMC-SS) deduplicated case list and the line list of
repeated SARS-CoV-2 tests. All positive tests conducted in South Africa appear in the
combined data set, regardless of the reason for testing or type of test (PCR or antigen
detection).
Civil unrest during July 2021 severely disrupted testing in Gauteng and KwaZulu-
Natal, the two most populous provinces in the country. As a result, case data became
unreliable and a key assumption of our models - that the force of infection is proportional to
the number of positive tests - was violated. Increasing vaccination rates from August 2021
could also introduce bias. We therefore limited the anlysis to data with specimen receipt
dates between 04 March 2020 and 30 June 2021.
A combination of deterministic (national ID number, names, dates of birth) and
probabilistic linkage methods were utilized to identify repeated tests conducted on the same
person. In addition, provincial COVID-19 contact tracing teams identify and report repeated
SARS-Cov-2 positive tests to the NICD, whether detected via PCR or antigen tests. The
unique COVID-19 case identifier which links all tests from the same person was used to
merge the two datasets. Irreversibly hashed case IDs were generated for each individual in
the merged data set.
Primary infections and suspected repeat infections were identified using the merged
data set. Repeated case IDs in the line list were identified and used to calculate the time
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between consecutive positive tests for each individual, using specimen receipt dates. If the
time between sequential positive tests was at least 90 days, the more recent positive test
was considered to indicate a suspected new infection. We present a descriptive analysis of
suspected third infections, although only suspected second infections (which we refer to as
“reinfections”) were considered in the analyses of temporal trends. Incidence time series for
primary infections and reinfections are calculated by specimen receipt date of the first
positive test associated with the infection, and total observed incidence is calculated as the
sum of first infections and reinfections. The specimen receipt date was chosen as the
reference point for analysis because it is complete within the data set.
All analyses were conducted in the R statistical programming language (R version
4.0.5 (2021-03-31)).
Data validation
To assess validity of the data linkage procedure and thus verify whether individuals
identified as having suspected reinfections did in fact have positive test results at least 90
days apart, we conducted a manual review of a random sample of suspected second
infections occurring on or before 20 January 2021 (n=585 of 6017; 9.7%). This review
compared fields not used for linkages (address, cell-phone numbers, email addresses,
facility, and health-care providers) between records in the NMC-SS and positive test line
lists. Where uncertainty remained and contact details were available, patients or next-of-kin
were contacted telephonically to verify whether the individual had received multiple positive
test results.
Descriptive analysis
We calculated the time between successive positive tests as the number of days between
the last positive test associated with an individual’s first identified infection (i.e., within 90
days of a previous positive test, if any) and the first positive test associated with their
suspected second infection (i.e., at least 90 days after the most recent positive test).
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We also compared the age, gender, and province of individuals with suspected
reinfections to individuals eligible for reinfection (i.e., who had a positive test result at least
90 days prior to 30 June 2021).
We did not calculate overall incidence rates by wave because the force of infection is
highly variable in space and time, and the period incidence rate is also influenced by the
temporal pattern of when people become eligible for reinfection. Incidence rate estimates
would therefore be strongly dependent on the time frame of the analysis and not
comparable to studies from other locations or time periods.
Statistical analysis of reinfection trends
We analysed the NICD national SARS-CoV-2 routine surveillance data to evaluate whether
reinfection risk has changed since emergence of the Beta or Delta variants. We evaluated
the daily numbers of suspected reinfections using two approaches. First, we constructed a
simple null model based on the assumption that the reinfection hazard experienced by
previously diagnosed individuals is proportional to the incidence of detected cases and fit
this model to the pattern of reinfections observed before the emergence of the Beta variant
(through 30 September 2020). The null model assumes no change in the reinfection hazard
coefficient through time. We then compared observed reinfections after September 2020 to
expected reinfections under the null model.
Second, we evaluated whether there has been a change in the relative hazard of
reinfection versus primary infection, to distinguish between increased overall transmissibility
of the variants and any additional risk of reinfection due to potential immune escape. To do
this, we calculated an empirical hazard coefficient at each time point for primary infections
and reinfections and compared their relative values through time.
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Approach 1: Catalytic model assuming a constant reinfection hazard coefficient
Model description For a case testing positive on day
!
(by specimen receipt date), we
assumed the reinfection hazard is
"
for each day from
! # $
to
! # %"
and
&'
(
#
for each day
) * ! # %"
, where
'
(
#
is the 7-day moving average of the detected case incidence (first
infections and reinfections) for day
)
. The probability of a case testing positive on day
!
having a diagnosed reinfection by day
+
is thus
,-!.+/ 0 $ 12$
%
&
!"#
!"$%&' '
(
!
, and the expected
number of cases testing positive on day
!
that have had a diagnosed reinfection by day
+
is
')
*,-!.+/
., where
')
*
is the detected case incidence (first infections only) for day
!
. Thus, the
expected cumulative number of reinfections by day
+
is
3+0
4
')
*,-!.+/
),+
),-
. The expected
daily incidence of reinfections on day
+
is
5+0 3+13+$*
.
Model fitting The model was fitted to observed reinfection incidence through 30
September 2020 assuming data are negative binomially distributed with mean
5+
. The
reinfection hazard coefficient (
&
) and the inverse of the negative binomial dispersion
parameter (
6
) are fitted to the data using a Metropolis-Hastings Monte Carlo Markov Chain
(MCMC) estimation procedure implemented in the R Statistical Programming Language.
We ran 4 MCMC chains with random starting values for a total of 1e+05 iterations per
chain, discarding the first 2,000 iterations (burn-in). Convergence was assessed using the
Gelman-Rubin diagnostic (9).
Model-based projection We used 1,500 samples from the joint posterior distribution
of fitted model parameters to simulate possible reinfection time series under the null model,
generating 100 stochastic realizations per parameter set. We then calculated projection
intervals as the middle 95% of daily reinfection numbers across these simulations.
We applied this approach at the national level, as well as to Gauteng, KwaZulu-
Natal, and Western Cape Provinces, which were the only provinces with a sufficient number
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Page 8 of 28
of reinfections during the fitting period to permit estimation of the reinfection hazard
coefficient.
Approach 2: Empirical estimation of time-varying infection and reinfection hazards
We estimated the time-varying empirical hazard of infection as the daily incidence per
susceptible individual. This approach requires reconstruction of the number of susceptible
individuals through time. We distinguish between three “susceptible” groups: naive
individuals who have not yet been infected (
7*
), previously infected individuals who had
undiagnosed infections (
7.
/
), and previously infected individuals who had a prior positive
test at least 90 days ago (
7.
). We estimate the numbers of individuals in each of these
categories on day
!
as follows:
7*-!/ 0 8 1
9
'0
,123
0,)
0,-
7.
/-!/ 0 -$ 1 ,123/
,123
9
'0
0,)
0,-
7.-!/ 0
9
'0
0,)$!-
0,-
1
9
:0
,123(
0,)
0,-
where
8
is the total population size,
'0
is the number of individuals with their first positive
test on day
;
,
,123
is the probability of detection for individuals who have not had a
previously identified infection,
,123(
is the probability of detection for individuals who have
had a previously identified infection, and
:0
is the number of individuals with a detected
reinfection on day
;
. For the main analysis, we assume
,123 0 "<$
and
,123(0 "<=
, although
the conclusions are robust to these assumptions (see Sensitivity Analysis).
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Page 9 of 28
Individuals in
7.
/
and
7.
are assumed to experience the same daily hazard of
reinfection, estimated as
>.-!/ 0 4
5
$67)*+(
8(9):
. The daily hazard of infection for previously
uninfected individuals is then estimated as
>*-!/ 0 '
(
$67)*+$;(!8(
,9):
8-9):
.
If we assume that the hazard of infection is proportional to incidence (
?)
),
>*-!/ 0
&*-!/?)
and
>.-!/ 0 &.-!/?)
, we can then examine the infectiousness of the virus through
time as:
&*-!/ 0 >*-!/
-')
@
A,123 #:)
B
A,123(/
&.-!/ 0 >.-!/
-')
@
A,123 #:)
B
A,123(/
We also used this approach to construct a data set with the daily numbers of individuals
eligible to have a suspected second infection (
7.-!/
) and not eligible for suspected second
infection (
7*-!/ # 7.
/-!/
) by wave. Wave periods were defined as the time surrounding the
wave peak for which the 7-day moving average of case numbers was above 15% of the
wave peak. We then analyzed these data using a generalized linear mixed model to
estimate the relative hazard of infection in the population eligible for suspected second
infection, compared to the hazard in the population not eligible for suspected second
infection.
Our primary model was a Poisson model with a log link function, groupinc
0
Poisson
-C/
:
DEF-C/ G
group
H
wave
#
offset
-DEF-
groupsize
//# -
day
/
The outcome variable (groupinc) was the daily number of observed infections in the
two groups. Our main interest for this analysis was in whether the relative hazard was
higher in the second and third waves, thus potentially indicating immune escape. This effect
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is measured by the interaction term between group and wave. The offset term is used to
ensure that the estimated coefficients can be appropriately interpreted as per capita rates.
We used day as a proxy for force of infection and reporting patterns and examined models
where day was represented as a random effect (to reflect that observed days can be
thought of as samples from a theoretical population) and as a fixed effect (to better match
the Poisson assumptions). As focal estimates from the two models were indistinguishable,
we present only the results based on the random effect assumption. Both versions of the
model are included in the code repository.
Results
We identified 16,029 individuals with at least two suspected infections (through 30 June
2021) and 80 individuals with suspected third infections (Figure 1).
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Figure 1. Daily numbers of detected primary infections, individuals eligible to be considered for reinfection,
and suspected reinfections in South Africa. A: Time series of detected primary infections. Black line indicates
7-day moving average; black points are daily values. Colored bands represent wave periods, defined as the
period for which the 7-day moving average of cases was at least 15% of the corresponding wave peak (purple
= wave 1, pink = wave 2, orange = wave 3). B: Population at risk for reinfection (individuals whose most
recent positive test was at least 90 days ago and who have not yet had a suspected reinfection). C: Time
series of suspected reinfections. Blue line indicates 7-day moving average; blue points are daily values.
Data validation
Of the 585 randomly selected individuals with possible reinfections in the validation sample,
562 (96%) were verified as the same individual based on fields not used to create the
linkages; the remaining 23 (4%) were either judged not a match or to have insufficient
evidence (details captured by the clinician or testing laboratory) to determine whether the
records belonged to the same individual.
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Descriptive analysis
Time between successive positive tests
The time between successive positive tests for individuals with suspected reinfections was
bimodally distributed with peaks near 180 and 360 days (Figure 2A). The shape of the
distribution was strongly influenced by the timing of South Africa’s epidemic waves. The first
peak corresponds to individuals initially infected in wave 1 and reinfected in wave 2 or
initially infected in wave 2 and reinfected in wave 3, while the second peak corresponds to
individuals initially infected in wave 1 and reinfected in wave 3.
Figure 2. Descriptive analysis of suspected reinfections. A: Time in days between infections for individuals
with suspected reinfection. Note that the time since the previous positive test must be at least 90 days. B:
Percentage of eligible primary infections with suspected reinfections, by province. C: Age distribution of
individuals with suspected reinfections (blue) versus eligible individuals with no detected reinfection (yellow),
by sex. Solid lines indicate females; dashed lines indicate males.
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Distribution of suspected reinfections by province
Suspected reinfections were identified in all nine provinces (Figure 2B). The reinfection rate
was highest in Gauteng, where 5,872 of 415,291 eligible primary infections (1.41%) had
suspected reinfections and lowest in Eastern Cape (1,226 of 195,481; 0.63%). For
comparison, the national reinfection rate was 195,481; 1.03% (16,029 of 1,551,655 eligible
primary infections). Numbers for all provinces are provided in Table S1.
Breakdown of suspected reinfections by sex and age group
Among 1,518,044 eligible primary infections with both age and sex recorded, 9,413 of
877,676 females (1.07%) and 6,573 of 640,368 males (1.03%) had suspected reinfections.
Relative to individuals with no identified reinfection, reinfections were concentrated in adults
between the ages of 20 and 55 years (Figure 2C). Numbers for all age group-sex
combinations are provided in Table S2.
Individuals with multiple suspected reinfections
80 individuals were identified who had three suspected infections. Most of these individuals
initially tested positive during the first wave, with suspected reinfections associated with
waves two and three (Figure S1). No individual had more than two suspected reinfections.
Further details are given in the Supplementary Material (Table S1, Table S2, Figure S1).
Reinfection trends
The first individual became eligible for reinfection on 2020-06-02 (i.e., 90 days after the first
case was detected). No suspected reinfections were detected until 23 June 2020, after
which the number of suspected reinfections increased gradually. The 7-day moving
average of suspected reinfections reached a peak of 162.4 during the second epidemic
wave and a maximum of 304.6 during the third wave, as of 30 June 2021 (Figure 1).
Approach 1: Comparison of data to projections from the null model
Under the null model of no change in the reinfection hazard coefficient through time, the
number of incident reinfections was expected to be low prior to the second wave and to
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increase substantially during the second and third waves, peaking at a similar time to
incident primary infections. The observed time series of suspected reinfections closely
follows this pattern (Figure 3), although it falls slightly below the prediction interval toward
the end of the time series. Provincial-level analyses suggest that this deviation is driven
primarily by the Western Cape, where the observed time series of suspected reinfections
falls below the prediction interval near the peak of both waves two and three (Figure S3). In
contrast, the observed time series of suspected reinfections consistently falls within the
prediction interval for Gauteng and KwaZulu-Natal (Figure S3). This pattern may result from
policies implemented only in the Western Cape that limited testing during the wave peaks.
Figure 3. Observed and expected temporal trends in reinfection numbers. Blue lines (points) represent the 7-
day moving average (daily values) of suspected reinfections. Grey lines (bands) represent mean predictions
(95% projection intervals) from the null model. A: The null model was fit to data on suspected reinfections
through 2021-09-30, prior to the emergence of the Beta vairant. B: Comparison of data to projections from the
null model over the projection period.
Approach 2: Empirical estimation of time-varying infection and reinfection hazards
The estimated hazard coefficient for primary infection increases steadily through time, as
expected under a combination of relaxing of restrictions, behavioural fatigue, and
introduction of variants with increased transmissibility. The estimated hazard coefficient for
reinfection, in contrast, remains relatively constant, with the exception of an initial spike in
mid-2020, when reinfection numbers were very low. The mean ratio of reinfection hazard to
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primary infection hazard has decreased slightly with each subsequent wave, from 0.15 in
wave 1 to 0.12 in wave 2 and 0.1 in wave 3. The absolute values of the hazard coefficients
and hazard ratio are sensitive to assumed observation probabilities for primary infections
and reinfections; however, temporal trends are robust (Figure S5).
These findings are consistent with the estimates from the generalized linear mixed
model based on the reconstructed data set. In this analysis, the relative hazard ratio for
wave 2 versus wave 1 was 0.75 (CI
!"
: 0.59-0.97) and for wave 3 versus wave 1 was 0.70
(CI
!"
: 0.55-0.90).
Figure 4. Empirical estimates of infection and reinfection hazards. A: Estimated time-varying hazard
coefficients for primary infection (black) and reinfections (green). Colored bands represent wave periods,
defined as the period for which the 7-day moving average of cases was at least 15% of the corresponding
wave peak (purple = wave 1, pink = wave 2, orange = wave 3). B: Ratio of the empirical hazard for
reinfections to the empirical hazard for primary infections
Discussion
Our analyses suggest that the cumulative number of reinfections observed through June
2021 is consistent with the null model of no change in reinfection risk through time.
Furthermore, our findings suggest that the relative hazard of reinfection versus primary
infection has decreased with each wave of infections, as would be expected if the risk of
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primary infection increased without a corresponding increase in reinfection risk. Based on
these analyses, we conclude there is no population-level evidence of immune escape at
this time. We recommend ongoing monitoring of these trends.
Differences in the time-varying force of infection, original and subsequent circulating
lineages, testing strategies, and vaccine coverage limit the usefulness of direct
comparisons of rates of reinfections across countries or studies. Reinfection does however
appear to be relatively uncommon. The PCR-confirmed reinfection rate ranged from 0% –
1.1% across eleven studies included in a systematic review (10). While none of the studies
included in the systematic review reported increasing risk of reinfection over time, the
duration of follow-up was less than a year and most studies were completed prior to the
identification of the Beta and Delta variants of concern. Our findings are consistent with
results from the PHIRST-C community cohort study conducted in two locations in South
Africa, which found that infection prior to the second wave provided 84% protection against
reinfection during the second (Beta) wave (11), comparable to estimates of the level of
protection against reinfection for wild type virus from the SIREN study in the UK (1).
A preliminary analysis of reinfection trends in England suggested that the Delta
variant may have a higher risk of reinfection compared to the Alpha variant (12); however,
this analysis did not take into account the temporal trend in the population at risk for
reinfection, which may have biased the findings.
Our findings are somewhat at odds with in vitro neutralization studies. Both the Beta
and Delta variants are associated with decreased neutralization by some anti-receptor
binding-domain (anti-RBD) and anti-N-terminal domain (anti-NTD) monoclonal antibodies
though both Beta and Delta each remain responsive to at least one anti-RBD (4,5,13). In
addition, Beta and Delta are relatively poorly neutralized by convalescent sera obtained
from unvaccinated individuals infected with non-VOC virus (3–5,13). Lastly sera obtained
from individuals after both one and two doses of the BNT162b2 (Pfizer) or ChAdOx1
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Page 17 of 28
(AstraZeneca) vaccines displayed lower neutralization of the Beta and Delta variants when
compared to non-VOC and Alpha variant (5); although this does not have direct bearing on
reinfection risk it is an important consideration for evaluating immune escape more broadly.
Non-neutralizing antibodies and T-cell responses could explain the apparent disjuncture
between our findings and the in vitro immune escape demonstrated by both Beta and Delta.
Strengths of this study
Our study has two major strengths. Firstly, we analyzed a large routine national data set
comprising all confirmed cases in the country, allowing a comprehensive analysis of
suspected reinfections in the country. Secondly, we found consistent results using two
different analytical methods, both of which accounted for the changing force of infection and
increasing numbers of individuals at risk for reinfection.
Limitations of this study
The primary limitation of this study is that changes in testing practices, health-seeking
behavior, or access to care have not been accounted for in these analyses. Estimates
based on serological data from blood donors suggests substantial geographic variability in
detection rates (14), which may contribute to the observed differences in reinfection
patterns by province. Detection rates likely also vary through time and by other factors
affecting access to testing, which may include occupation, age, and socioeconomic status.
In particular, rapid antigen tests, which were introduced in South Africa in late 2020, may be
under-reported despite mandatory reporting requirements. If under-reporting of antigen
tests was substantial and time-varying it could influence our findings. However, comparing
temporal trends in infection risk among those eligible for reinfection with the rest of the
population, as in approach 2, mitigates against potential failure to detect a substantial
increase in risk.
Reinfections were not confirmed by sequencing or by requiring a negative test
between putative infections. Nevertheless, the 90-day window period between consecutive
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Page 18 of 28
positive tests reduces the possibility that suspected reinfections were predominantly the
result of prolonged viral shedding. Furthermore, due to data limitations, we were unable to
examine whether symptoms and severity in primary episodes correlate with protection
against subsequent reinfection.
Lastly, while vaccination may increase protection in previously infected individuals
(15–18), vaccination coverage in South Africa was very low during the time of the study
(e.g., <3% of the population was fully vaccinated by 30 June 2021 (19)). Vaccination is
therefore unlikely to have substantially influenced our findings. Increased vaccination
uptake may reduce the risks of both primary infection and reinfection moving forward and
would be an important consideration for application of our approach to other locations with
higher vaccine coverage.
Conclusion
To date, we find no evidence that reinfection risk is higher as a result of the emergence of
Beta or Delta variants of concern, suggesting the selective advantage that allowed these
variants to spread derived primarily from increased transmissibility, rather than immune
escape. The discrepancy between the population-level evidence presented here and
expectations based on laboratory-based neutralization assays highlights the need to
identify better correlates of immunity for assessing immune escape in vitro.
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Ethics statements
Ethical approval
Ethical approval: This study has received ethical clearance from University of the
Witwatersrand (Clearance certificate number M160667) and approval under reciprocal
review from Stellenbosch University (Project ID 19330, Ethics Reference Number
N20/11/074_RECIP_WITS_M160667_COVID-19).
Data availability statement
Data and code are available at https://github.com/jrcpulliam/reinfections. The following data
are included in the repository:
Counts of reinfections and primary infections by province, age group (5-year bands),
and sex (M, F, U)
Daily time series of primary infections and suspected reinfections by specimen
receipt date (national)
Model output: posterior samples from the MCMC fitting procedure and simulation
results
Acknowledgements
The authors wish to acknowledge the members of the NICD Epidemiology and Information
Technology teams which curate, clean, and prepare the data utilized in this analysis.
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Page 21 of 28
Epidemiology team: Andronica Moipone Shonhiwa, Genevie Ntshoe, Joy Ebonwu,
Lactatia Motsuku, Liliwe Shuping, Mazvita Muchengeti, Jackie Kleynhans, Gillian Hunt,
Victor Odhiambo Olago, Husna Ismail, Nevashan Govender, Ann Mathews, Vivien Essel,
Veerle Msimang, Tendesayi Kufa-Chakezha, Nkengafac Villyen Motaze, Natalie Mayet,
Tebogo Mmaborwa Matjokotja, Mzimasi Neti, Tracy Arendse, Teresa Lamola, Itumeleng
Matiea, Darren Muganhiri, Babongile Ndlovu, Khuliso Ravhuhali, Emelda Ramutshila,
Salaminah Mhlanga, Akhona Mzoneli, Nimesh Naran, Trisha Whitbread, Mpho Moeti,
Chidozie Iwu, Eva Mathatha, Fhatuwani Gavhi, Masingita Makamu, Matimba Makhubele,
Simbulele Mdleleni, Bracha Chiger, Jackie Kleynhans
Information Technology team: Tsumbedzo Mukange, Trevor Bell, Lincoln Darwin,
Fazil McKenna, Ndivhuwo Munava, Muzammil Raza Bano, Themba Ngobeni
We also thank Carl A.B. Pearson, Shade Horn, Youngji Jo, Belinda Lombard, Liz S.
Villabona-Arenas, and colleagues in the SARS-CoV-2 variants research consortium in
South Africa for helpful discussions during the development of this work.
Footnotes
Author contributions
Conceptualization - JP, CvS, JD, HM
Data collection, management, and validation - NG, KM, AvG, CC
Data analysis - JP, CvS, JD
Interpretation - JP, AvG, CC, MJG, JD, HM
Drafting the manuscript - JP
Manuscript review, revision, and approval - all authors
Guarantor: HM
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Page 22 of 28
Funding
JRCP and CvS are supported by the South African Department of Science and Innovation
and the National Research Foundation. Any opinion, finding, and conclusion or
recommendation expressed in this material is that of the authors and the NRF does not
accept any liability in this regard. This work was also supported by the Wellcome Trust
(grant number 221003/Z/20/Z) in collaboration with the Foreign, Commonwealth and
Development Office, United Kingdom.
Competing interests
All authors have completed the ICMJE uniform disclosure form. CC and AvG have received
funding from Sanofi Pasteur in the past 36 months. JRCP and KM serve on the Ministerial
Advisory Committee on COVID-19 of the South African National Department of Health. The
authors have declared no other relationships or activities that could appear to have
influenced the submitted work.
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Supplementary Material
Distribution of suspected reinfections by province, South Africa, March
2020 to June 2021
Province
No reinfection
Two reinfections
Total
EASTERN CAPE
194,255
2
195,481
FREE STATE
82,769
3
83,614
GAUTENG
409,419
40
415,291
KWAZULU-NATAL
332,074
11
334,522
LIMPOPO
62,800
1
63,304
MPUMALANGA
74,744
4
75,494
NORTH WEST
63,200
7
64,099
NORTHERN CAPE
36,126
1
36,540
WESTERN CAPE
280,237
11
283,308
UNKNOWN
2
0
2
Total
1,535,626
80
1,551,655
Breakdown of suspected reinfections by sex and age group (years),
South Africa, March 2020 to June 2021
Sex
Age group
No reinfection
One reinfection
Two reinfections
Total
F
(0,20]
84,241
506
3
84,750
F
(20,40]
359,483
4,776
27
364,286
F
(40,60]
303,546
3,366
8
306,920
F
(60,80]
104,507
620
6
105,133
F
(80,Inf]
16,486
101
0
16,587
M
(0,20]
67,956
369
2
68,327
M
(20,40]
247,359
3,039
15
250,413
M
(40,60]
230,546
2,496
17
233,059
M
(60,80]
79,777
588
2
80,367
M
(80,Inf]
8,157
45
0
8,202
Total
1,502,058
15,906
80
1,518,044
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Individuals with multiple suspected reinfections
Figure S1. Timing of infections for individuals with multiple suspected reinfections. Circles represent the first
positive test of the first detected infection; triangles represent the first positive test of the suspected second
infection; squares represent the first positive test of the suspected third infection. Colored bands represent
wave periods, defined as the period for which the 7-day moving average of cases was at least 15% of the
corresponding wave peak (purple = wave 1, pink = wave 2, orange = wave 3).
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Timing of primary infections and reinfections by province
Figure S2. Number of detected primary infections (black) and suspected reinfections (blue), by province.
Lines represent 7-day moving averages. The y-axis is shown on a log scale.
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Province-level comparison of data to projections from the null model
Figure S3. Observed and expected temporal trends in reinfection numbers, for provinces with sufficient
numbers of suspected reinfections. Blue lines (points) represent the 7-day moving average (daily values) of
suspected reinfections. Grey lines (bands) represent mean predictions (95% projection intervals) from the null
model. A and B: Gauteng. C and D: KwaZulu-Natal. E and F: Western Cape.
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Approach 1: Convergence diagnostics
Figure S4. Convergence diagnostics and density of the posterior distribution for MCMC fits. A and B: MCMC
chains for each parameter. C: Gelman-Rubin values (a.k.a. potential scale reduction factors) for each
parameter; values less than 1.1 indicate sufficient mixing of chains to suggest convergence. D, G, I: posterior
density for each parameter and the log likelihood. E, F, H: 2-D density plots showing correlations between
parameters and the log likelihood.
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Approach 2: Sensitivity analysis
Figure S5. Sensitivity analysis of empirical hazard ratio estimates to assumed observation probabilities for
primary infections and reinfections. Estimates are shown for the full range of probabilities for which the overall
mean relative hazard is between 0 and 1. The white polygon encloses the most plausible estimates
(i.e. consistent with relative reinfection risk observed in the SIREN study (1) and observation probabilities for
primary infection consistent with estimates based on seroprevalence data (14)). Top: Mean relative empirical
hazard for reinfections versus primary infections in each wave, as a function of assumed observation
probabilities for primary infections (
𝑝./0
) and reinfections (
𝑝./0!
). A: wave 1, B: wave 2, C: wave 3. Bottom:
Percent change in the mean relative empirical hazard for reinfections versus primary infections in waves 2 (D)
and 3 (E) relative to wave 1, as a function of assumed observation probabilities for primary infections (
𝑝./0
)
and reinfections (
𝑝./0!
).
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In the 21st century, several emergent viruses have posed a global threat. Each of these pathogens has emphasized the value of rapid and scalable vaccine development programs. The ongoing SARS-CoV-2 pandemic has made the importance of such efforts especially clear. In particular, new biotechnological advances in vaccine design allowed for new advances in vaccines that provide only the nucleic acid building blocks of an antigen, eliminating many safety concerns. During the COVID-19 pandemic, these DNA and RNA vaccines have facilitated the development and deployment of vaccines at an unprecedented pace. This success was attributable at least in part to broader shifts in scientific research relative to prior epidemics. For example, the genome of SARS-CoV-2 was available as early as January 2020, allowing for global efforts in the development of DNA and RNA vaccines to begin within two weeks of the international community becoming aware of the new viral threat. Additionally, these technologies that were previously only theoretical were found to be not only safe but also to have high vaccine efficacy. Although historically a slow process, the rapid development of vaccines during the COVID-19 public health crisis reveals a major shift in vaccine technologies. In this review, we provide historical context for the emergence of these paradigm-shifting vaccines. Further context is provided in a companion review exploring more established vaccines platforms. We describe several DNA and RNA vaccines and in terms of their efficacy, safety, and approval status. We also discuss patterns in worldwide distribution. The advances made since early 2020 provide an exceptional illustration of how rapidly vaccine development technology has advanced in the last two decades in particular, and suggest a new era in vaccines against emerging pathogens.
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The global pandemic caused by SARS-CoV-2 is a major public health problem. Virus entry occurs via binding to ACE2. Five SARS-CoV-2 variants of concern (VOCs) were reported so far, all having immune escape characteristics. Infection with the current VOC Omicron were noticed in immunized and recovered individuals, therefore development of new treatments against VOC infections are urgently needed. Most approved mAbs treatments against SARS-CoV-2 are directed against the spike protein of the original virus and are therefore inefficient against Omicron. Here, we report on the generation of hACE2.16, an anti-ACE2 antibody that recognize and blocks ACE2-RBD binding without affecting ACE2 enzymatic activity. We demonstrate that hACE2.16 binding to ACE2 does not affect its surface expression and that hACE2.16 blocks infection and virus production of various VOCs including Omicron BA.1 and BA.2. hACE2.16 might therefore be an efficient treatment against all VOCs, the current and probably also future ones.
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Given the emergence of the SARS-CoV-2 Omicron BA.1 and BA.2 variants and the roll-out of booster COVID-19 vaccination, evidence is needed on protection conferred by primary vaccination, booster vaccination and previous SARS-CoV-2 infection by variant. We employed a test-negative design on S-gene target failure data from community PCR testing in the Netherlands from 22 November 2021 to 31 March 2022 (n = 671,763). Previous infection, primary vaccination or both protected well against Delta infection. Protection against Omicron BA.1 infection was much lower compared to Delta. Protection was similar against Omicron BA.1 compared to BA.2 infection after previous infection, primary and booster vaccination. Higher protection was observed against all variants in individuals with both vaccination and previous infection compared with either one. Protection against all variants decreased over time since last vaccination or infection. We found that primary vaccination with current COVID-19 vaccines and previous SARS-CoV-2 infections offered low protection against Omicron BA.1 and BA.2 infection. Booster vaccination considerably increased protection against Omicron infection, but decreased rapidly after vaccination. The protection of COVID-19 vaccines against emerging variants needs to be monitored. Here, the authors use community testing data from the Netherlands and find that protection against infection by Omicron subvariants BA.1 and 2 is low and that booster vaccines considerably but temporarily increase protection.
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Due to the inconsistency of the information regarding the evolution of the last two waves caused by the Delta and Omicron variants, we are attempting to provide a phylodynamic analysis. We used the nucleotide sequences of the Delta (n = 89) and Omicron (n = 74) variants recorded in the GISAID database to perform an analysis based on the contemporary model of the birth-death horizon. An average rate of evolution of 3.677 × 10 − 4 subs /site/year (range,1.311× 10 − 4 −6.144×10 − 4 ) for the DELTA variant, this gives an estimated mean of tMRCA corresponding to the root of the tree dated at 43.5 days. While for OMICRON a mean evolutionary rate of 3.898×10 − 3 subs/site/year (range,2.686×10 − 3 − 5.102×10 − 3 ) gave an estimated mean of tMRCA corresponding to the root of the tree dated at 26.4 days. The median of Re for the Indian DELTA = 1.81 (range, 0.196–3.94), and increased to 3.837 ≈ 4 on March 30, 2021. While for the South African OMICRON Re = 0.97 (range,0.41–1.54), and rose to 9.59 ≈ 9 on December 14, 2021. The average doubling times of the two waves are estimated respectively at 9 days for the DELTA variant and at 3 days for the OMICRON variant. The spread of the OMICRON pandemic is almost three times faster than that of the DELTA. The mean doubling times of the two waves are 9 days for the DELTA variant and 3 days for the OMICRON variant, respectively. As a result, the OMICRON pandemic is spreading nearly three times faster than the DELTA.
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The SARS-CoV-2 pandemic has been characterised by the regular emergence of genomic variants. With natural and vaccine-induced population immunity at high levels, evolutionary pressure favours variants better able to evade SARS-CoV-2 neutralising antibodies. The Omicron variant (first detected in November 2021) exhibited a high degree of immune evasion, leading to increased infection rates worldwide. However, estimates of the magnitude of this Omicron wave have often relied on routine testing data, which are prone to several biases. Using data from the REal-time Assessment of Community Transmission-1 (REACT-1) study, a series of cross-sectional surveys assessing prevalence of SARS-CoV-2 infection in England, we estimated the dynamics of England’s Omicron wave (from 9 September 2021 to 1 March 2022). We estimate an initial peak in national Omicron prevalence of 6.89% (5.34%, 10.61%) during January 2022, followed by a resurgence in SARS-CoV-2 infections as the more transmissible Omicron sub-lineage, BA.2 replaced BA.1 and BA.1.1. Assuming the emergence of further distinct variants, intermittent epidemics of similar magnitudes may become the ‘new normal’. This study presents data from the REACT-1 SARS-CoV-2 community sampling study in England from November 2021 to March 2022. They show that the Omicron variant peaked in January with a prevalence of ~7% and that the BA.2 sublineage had a 1.5x higher reproduction number compared to other Omicron sublineages.
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Background By March 2021, South Africa experienced two waves of SARS-CoV-2 infections; the second associated with emergence of Beta variant. We estimated the burden and transmission of SARS-CoV-2 over the two waves. Methods We conducted a prospective cohort study during July 2020-March 2021 in one rural and one urban community. Mid-turbinate nasal swabs were collected twice-weekly from consenting household members irrespective of symptoms and tested for SARS-CoV-2 using real-time reverse transcription polymerase chain reaction (rRT-PCR). Serum was collected every two months and tested for anti-SARS-CoV-2 antibodies. Household cumulative infection risk (HCIR) was defined as the proportion of household members with infection following SARS-CoV-2 introduction. Findings Among 71,759 nasal specimens from 1,189 members (follow-up rate 93%), 834 (1%) were SARS-CoV-2-positive. By PCR detection and serology combined, 34% (406/1189) of individuals experienced ≥1 SARS-CoV-2 infection episode, and 3% (12/406) experienced reinfection. Infection by PCR and/or serology before the second wave was 84% (95% confidence interval (CI) 65%-93%) protective against re-infection. Of 254 PCR-confirmed episodes with available data, 17% (n=43) were associated with ≥1 symptom, of which 21% (9/43) were medically attended. Among 222 included households, 161 (73%) had ≥1 SARS-CoV-2-positive individual. HCIR was 16% (66/411). On multivariable analysis, index case lower cycle threshold value (OR 5.8, 95% CI 1.8-19.1), urban community (OR 3.1, 95% CI 1.5-6.2) and infection with Beta variant (OR 3.7, 95% CI 1.6-8.4) were associated with increased HCIR. HCIR was similar for symptomatic (8/67,12%) and asymptomatic (61/373, 16%) index cases (p-0.302). Interpretation In this study, 83% of SARS-CoV-2 infections were asymptomatic and index case symptom status did not affect HCIR, suggesting a limited role for control measures targeting symptomatic individuals. Previous infection was protective against SARS-CoV-2 infection in the second wave although household transmission increased following the emergence of Beta variant. Funding US Centers for Disease Control and Prevention Research in context Evidence before this study Previous studies have generated wide-ranging estimates of the proportion of SARS-CoV-2 infections which are asymptomatic. A recent systematic review found that 20% (95% CI 3%-67%) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections remained asymptomatic throughout infection and that transmission was lower from asymptomatic individuals. ¹ A systematic review and meta-analysis of 54 household transmission studies of SARS-CoV-2 found an estimated secondary attack rate of 17% (95% CI 14-19). ² The review also found that household secondary attack rates were increased from symptomatic index cases and that adults were more likely to acquire infection. South Africa experienced two waves of SARS-CoV-2 infections; the second wave was larger and associated with emergence of 501Y.V2 lineage. Modelling studies have suggested that SARS-CoV-2 variant 501Y.V2 may be more transmissible than other variants or that past exposure may provide limited protection, however, they were not able to establish which of these may be more important. A community study from Denmark and two healthcare worker cohorts estimated protection conferred by previous SARS-CoV-2 infection against reinfection to be between 81% and 89%. Data on protection conferred against infection with the 501Y.V2 variant are limited. Studies to quantify the burden of asymptomatic infections, symptomatic fraction, duration of shedding and household transmission of SARS-CoV-2 from asymptomatically infected individuals have mostly been conducted as part of outbreak investigations or in specific settings. Comprehensive systematic community studies and studies of 501Y.V2 are lacking. Added value of this study We found that from the start of the SARS-CoV-2 pandemic in March 2020 through March 2021 in South Africa, 406 (34%) of 1189 individuals from 222 randomly sampled households in a rural and an urban community in South Africa had at least one confirmed SARS-CoV-2 infection, detected on rRT-PCR- and/or serology, and 3% (12/406) experienced reinfection. Individuals with evidence of infection identified by PCR and/or serology before the onset of the second wave (14 December 2020) experienced 84% (95% CI 65%-93%) protection against re-infection compared to individuals without evidence of previous infection. Symptom data were analysed for 254 rRT-PCR-confirmed infection episodes that occurred >14 days after the start of follow-up (of a total of 310 episodes), of these, 17% (n=43) were associated with one or more symptom, of which 14% (n=6) were hospitalised and 2% (n=1) died. Seventy three percent (161/222) of included households, had one or more individual infected with SARS-CoV-2 within the household. SARS-CoV-2 infected index cases transmitted the infection to 16% (66/411) of susceptible household contacts. Index case ribonucleic acid (RNA) viral load estimated through cycle threshold value as proxy was strongly predictive of household transmission. Index case or household contact age group and presence of symptoms in the index case were not associated with household transmission. Household transmission was four times greater from index cases infected with Beta variant compared to non-variant infection. Implications of all the available evidence We found a high rate of SARS-CoV-2 infection in households in a rural community and an urban community in South Africa, with the majority of infections being asymptomatic in individuals of all ages. The household cumulative infection risk was 16% and did not differ by index case or contact age or presence of symptoms but was higher in households where the index case had potentially higher viral RNA and during the second wave of SARS-CoV-2 infection. The majority of infections were asymptomatic. Asymptomatic individuals transmitted SARS-CoV-2 at similar levels to symptomatic individuals suggesting that interventions targeting symptomatic individuals such as contact tracing of individuals tested because they report symptoms may have a limited impact as a control measure. The high level of heterologous protection from natural immunity evidenced in our study also informs our understanding of possible effects of population-level immunity on SARS-CoV-2 burden and transmission, although key questions remain regarding the durability of protection in the face of continued viral evolution.
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The SARS-CoV-2 B.1.617 lineage was identified in October 2020 in India1–5. It has since then become dominant in some indian regions and UK and further spread to many countries6. The lineage includes three main subtypes (B1.617.1, B.1.617.2 and B.1.617.3), harbouring diverse Spike mutations in the N-terminal domain (NTD) and the receptor binding domain (RBD) which may increase their immune evasion potential. B.1.617.2, also termed variant Delta, is believed to spread faster than other variants. Here, we isolated an infectious Delta strain from a traveller returning from India. We examined its sensitivity to monoclonal antibodies (mAbs) and to antibodies present in sera from COVID-19 convalescent individuals or vaccine recipients, in comparison to other viral strains. Variant Delta was resistant to neutralization by some anti-NTD and anti-RBD mAbs including Bamlanivimab, which were impaired in binding to the Spike. Sera from convalescent patients collected up to 12 months post symptoms were 4 fold less potent against variant Delta, relative to variant Alpha (B.1.1.7). Sera from individuals having received one dose of Pfizer or AstraZeneca vaccines barely inhibited variant Delta. Administration of two doses generated a neutralizing response in 95% of individuals, with titers 3 to 5 fold lower against Delta than Alpha. Thus, variant Delta spread is associated with an escape to antibodies targeting non-RBD and RBD Spike epitopes.
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SARS-CoV-2 has undergone progressive change with variants conferring advantage rapidly becoming dominant lineages e.g. B.1.617. With apparent increased transmissibility variant B.1.617.2 has contributed to the current wave of infection ravaging the Indian subcontinent and has been designated a variant of concern in the UK. Here we study the ability of monoclonal antibodies, convalescent and vaccine sera to neutralize B.1.617.1 and B.1.617.2 and complement this with structural analyses of Fab/RBD complexes and map the antigenic space of current variants. Neutralization of both viruses is reduced when compared with ancestral Wuhan related strains but there is no evidence of widespread antibody escape as seen with B.1.351. However, B.1.351 and P.1 sera showed markedly more reduction in neutralization of B.1.617.2 suggesting that individuals previously infected by these variants may be more susceptible to reinfection by B.1.617.2. This observation provides important new insight for immunisation policy with future variant vaccines in non-immune populations.
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SARS-CoV-2 variants of concern (VOC) have arisen independently at multiple locations [1, 2] and may reduce the efficacy of current vaccines targeting the spike glycoprotein [3]. Here, using a live virus neutralization assay (LVNA), we compared neutralization of a non-VOC variant versus the 501Y.V2 variant using plasma collected from adults hospitalized with COVID-19 from two South African infection waves, with the second wave dominated by 501Y.V2 infections. Sequencing demonstrated that infections in first wave plasma donors were with viruses harbouring none of the 501Y.V2-defining mutations, except for one with the E484K mutation in the receptor binding domain. 501Y.V2 virus was effectively neutralized by plasma from second wave infections and first wave virus was effectively neutralized by first wave plasma. In cross-neutralization, 501Y.V2 virus was poorly neutralized by first wave plasma, with a 15.1-fold drop relative to 501Y.V2 neutralization by second wave plasma across participants. In contrast, second wave plasma cross-neutralization of first wave virus was more effective, showing only a 2.3-fold decline relative to first wave plasma neutralization of first wave virus. While we only tested one plasma elicited by E484K alone, this potently neutralized both variants. The observed effective neutralization of first wave virus by 501Y.V2 infection elicited plasma provides preliminary evidence that vaccines based on VOC sequences could retain activity against other circulating SARS-CoV-2 lineages.
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Boosterism could save lives Postinfection immune protection against severe acute respiratory syndrome coronavirus 2 reinfection is not fully understood. It will be devastating if waves of new variants emerge that undermine natural immune protection. Stamatatos et al. investigated immune responsiveness 4 to 8 months after previously infected individuals were given a messenger RNA–based vaccine developed for the original Wuhan variant (see the Perspective by Crotty). Before vaccination, postinfection serum antibody neutralization responses to virus variants were variable and weak. Vaccination elevated postinfection serum-neutralizing capacity approximately 1000-fold against Wuhan-Hu-1 and other strains, and serum neutralization against the variant B.1.351 was enhanced. Although responses were relatively muted against the variant, they still showed characteristic memory responses. Vaccination with the Wuhan-Hu-1 variant may thus offer a valuable boost to protective responses against subsequent infection with variant viruses. Science , abg9175, this issue p. 1413 ; see also abj2258, p. 1392
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Background The degree to which infection with SARS-CoV-2 confers protection towards subsequent reinfection is not well described. In 2020, as part of Denmark's extensive, free-of-charge PCR-testing strategy, approximately 4 million individuals (69% of the population) underwent 10·6 million tests. Using these national PCR-test data from 2020, we estimated protection towards repeat infection with SARS-CoV-2. Methods In this population-level observational study, we collected individual-level data on patients who had been tested in Denmark in 2020 from the Danish Microbiology Database and analysed infection rates during the second surge of the COVID-19 epidemic, from Sept 1 to Dec 31, 2020, by comparison of infection rates between individuals with positive and negative PCR tests during the first surge (March to May, 2020). For the main analysis, we excluded people who tested positive for the first time between the two surges and those who died before the second surge. We did an alternative cohort analysis, in which we compared infection rates throughout the year between those with and without a previous confirmed infection at least 3 months earlier, irrespective of date. We also investigated whether differences were found by age group, sex, and time since infection in the alternative cohort analysis. We calculated rate ratios (RRs) adjusted for potential confounders and estimated protection against repeat infection as 1 – RR. Findings During the first surge (ie, before June, 2020), 533 381 people were tested, of whom 11 727 (2·20%) were PCR positive, and 525 339 were eligible for follow-up in the second surge, of whom 11 068 (2·11%) had tested positive during the first surge. Among eligible PCR-positive individuals from the first surge of the epidemic, 72 (0·65% [95% CI 0·51–0·82]) tested positive again during the second surge compared with 16 819 (3·27% [3·22–3·32]) of 514 271 who tested negative during the first surge (adjusted RR 0·195 [95% CI 0·155–0·246]). Protection against repeat infection was 80·5% (95% CI 75·4–84·5). The alternative cohort analysis gave similar estimates (adjusted RR 0·212 [0·179–0·251], estimated protection 78·8% [74·9–82·1]). In the alternative cohort analysis, among those aged 65 years and older, observed protection against repeat infection was 47·1% (95% CI 24·7–62·8). We found no difference in estimated protection against repeat infection by sex (male 78·4% [72·1–83·2] vs female 79·1% [73·9–83·3]) or evidence of waning protection over time (3–6 months of follow-up 79·3% [74·4–83·3] vs ≥7 months of follow-up 77·7% [70·9–82·9]). Interpretation Our findings could inform decisions on which groups should be vaccinated and advocate for vaccination of previously infected individuals because natural protection, especially among older people, cannot be relied on. Funding None.
Article
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Continued uncontrolled transmission of the severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) in many parts of the world is creating the conditions for significant virus evolution1,2. Here, we describe a new SARS-CoV-2 lineage (501Y.V2) characterised by eight lineage-defining mutations in the spike protein, including three at important residues in the receptor-binding domain (K417N, E484K and N501Y) that may have functional significance3–5. This lineage was identified in South Africa after the first epidemic wave in a severely affected metropolitan area, Nelson Mandela Bay, located on the coast of the Eastern Cape Province. This lineage spread rapidly, becoming dominant in the Eastern Cape, Western Cape and KwaZulu-Natal Provinces within weeks. Whilst the full significance of the mutations is yet to be determined, the genomic data, showing the rapid expansion and displacement of other lineages in multiple regions, suggest that this lineage is associated with a selection advantage, most plausibly as a result of increased transmissibility or immune escape6–8.
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The COVID-19 pandemic has ravaged the globe, and its causative agent, SARS-CoV-2, continues to rage. The prospects of ending this pandemic rest on the development of effective interventions. Single and combination monoclonal antibody (mAb) therapeutics have received emergency use authorization1–3, with more in the pipeline4–7. Furthermore, multiple vaccine constructs have shown promise8, including two with ~95% protective efficacy against COVID-199,10. However, these interventions were directed toward the initial SARS-CoV-2 that emerged in 2019. The recent emergence of new SARS-CoV-2 variants B.1.1.7 in the UK11 and B.1.351 in South Africa12 is of concern because of their purported ease of transmission and extensive mutations in the spike protein. We now report that B.1.1.7 is refractory to neutralization by most mAbs to the N-terminal domain (NTD) of the spike and relatively resistant to a few mAbs to the receptor-binding domain (RBD). It is not more resistant to convalescent plasma or vaccinee sera. Findings on B.1.351 are more worrisome in that this variant is not only refractory to neutralization by most NTD mAbs but also by multiple individual mAbs to the receptor-binding motif on RBD, largely owing to an E484K mutation. Moreover, B.1.351 is markedly more resistant to neutralization by convalescent plasma (9.4 fold) and vaccinee sera (10.3-12.4 fold). B.1.351 and emergent variants13,14 with similar spike mutations present new challenges for mAb therapy and threaten the protective efficacy of current vaccines.
Article
Despite over 140 million SARS‐CoV‐2 infections worldwide since the beginning of the pandemic, relatively few confirmed cases of SARS‐CoV‐2 reinfection have been reported. While immunity from SARS‐CoV‐2 infection is probable, at least in the short term, few studies have quantified the reinfection risk. To our knowledge, this is the first systematic review to synthesise the evidence on the risk of SARS‐CoV‐2 reinfection over time. A standardised protocol was employed, based on Cochrane methodology. Electronic databases and preprint servers were searched from 1 January 2020 to 19 February 2021. Eleven large cohort studies were identified that estimated the risk of SARS‐CoV‐2 reinfection over time, including three that enrolled healthcare workers and two that enrolled residents and staff of elderly care homes. Across studies, the total number of PCR‐positive or antibody‐positive participants at baseline was 615,777, and the maximum duration of follow‐up was more than 10 months in three studies. Reinfection was an uncommon event (absolute rate 0%–1.1%), with no study reporting an increase in the risk of reinfection over time. Only one study estimated the population‐level risk of reinfection based on whole genome sequencing in a subset of patients; the estimated risk was low (0.1% [95% CI: 0.08–0.11%]) with no evidence of waning immunity for up to 7 months following primary infection. These data suggest that naturally acquired SARS‐CoV‐2 immunity does not wane for at least 10 months post‐infection. However, the applicability of these studies to new variants or to vaccine‐induced immunity remains uncertain.
Article
Background Increased understanding of whether individuals who have recovered from COVID-19 are protected from future SARS-CoV-2 infection is an urgent requirement. We aimed to investigate whether antibodies against SARS-CoV-2 were associated with a decreased risk of symptomatic and asymptomatic reinfection. Methods A large, multicentre, prospective cohort study was done, with participants recruited from publicly funded hospitals in all regions of England. All health-care workers, support staff, and administrative staff working at hospitals who could remain engaged in follow-up for 12 months were eligible to join The SARS-CoV-2 Immunity and Reinfection Evaluation study. Participants were excluded if they had no PCR tests after enrolment, enrolled after Dec 31, 2020, or had insufficient PCR and antibody data for cohort assignment. Participants attended regular SARS-CoV-2 PCR and antibody testing (every 2–4 weeks) and completed questionnaires every 2 weeks on symptoms and exposures. At enrolment, participants were assigned to either the positive cohort (antibody positive, or previous positive PCR or antibody test) or negative cohort (antibody negative, no previous positive PCR or antibody test). The primary outcome was a reinfection in the positive cohort or a primary infection in the negative cohort, determined by PCR tests. Potential reinfections were clinically reviewed and classified according to case definitions (confirmed, probable, or possible) and symptom-status, depending on the hierarchy of evidence. Primary infections in the negative cohort were defined as a first positive PCR test and seroconversions were excluded when not associated with a positive PCR test. A proportional hazards frailty model using a Poisson distribution was used to estimate incidence rate ratios (IRR) to compare infection rates in the two cohorts. Findings From June 18, 2020, to Dec 31, 2020, 30 625 participants were enrolled into the study. 51 participants withdrew from the study, 4913 were excluded, and 25 661 participants (with linked data on antibody and PCR testing) were included in the analysis. Data were extracted from all sources on Feb 5, 2021, and include data up to and including Jan 11, 2021. 155 infections were detected in the baseline positive cohort of 8278 participants, collectively contributing 2 047 113 person-days of follow-up. This compares with 1704 new PCR positive infections in the negative cohort of 17 383 participants, contributing 2 971 436 person-days of follow-up. The incidence density was 7·6 reinfections per 100 000 person-days in the positive cohort, compared with 57·3 primary infections per 100 000 person-days in the negative cohort, between June, 2020, and January, 2021. The adjusted IRR was 0·159 for all reinfections (95% CI 0·13–0·19) compared with PCR-confirmed primary infections. The median interval between primary infection and reinfection was more than 200 days. Interpretation A previous history of SARS-CoV-2 infection was associated with an 84% lower risk of infection, with median protective effect observed 7 months following primary infection. This time period is the minimum probable effect because seroconversions were not included. This study shows that previous infection with SARS-CoV-2 induces effective immunity to future infections in most individuals. Funding Department of Health and Social Care of the UK Government, Public Health England, The National Institute for Health Research, with contributions from the Scottish, Welsh and Northern Irish governments.