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Comparison of viral levels in individuals with or without symptoms at time of COVID-19 testing among 32,480 residents and staff of nursing homes and assisted living facilities in Massachusetts

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Background Transmission of COVID-19 from people without symptoms poses considerable challenges to public health containment measures. The distribution of viral loads in individuals with and without symptoms remains uncertain. Comprehensive cross-sectional screening of all individuals in a given setting provides an unbiased way to assess viral loads independent of symptoms, which informs transmission risks. COVID-19 cases initially peaked in Massachusetts in mid-April 2020 before declining through June, and congregate living facilities were particularly affected during this early surge. We performed a retrospective analysis of data from a large public health-directed outbreak response initiative that involved comprehensive screening within nursing homes and assisted living facilities in Massachusetts to compare nasopharyngeal (NP) viral loads (as measured by RT-PCR cycle threshold (Ct) levels) in residents and staff to inform our ability to detect SARS-CoV-2 in individuals with or without symptoms in the population. Methods Between April 9 and June 9, 2020, we tested NP swabs from 32,480 unique individuals comprising staff and residents of the majority of nursing homes and assisted living facilities in Massachusetts. Under the direction of the MA Department of Public Health (MDPH), symptomatology at the time of sampling and demographic information was provided by each facility for each individual to facilitate reporting to health officials. NP swabs were collected, RNA extracted, and SARS-CoV-2 testing performed using quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). Results The nursing home and assisted living facilities resident cohort (N =16,966) was 65% female with a mean age of 82 years (SD 13 yrs). The staff cohort (N = 15,514) was 76% female with a median age of 45 (SD 15 yrs). A total 2654 residents (15.5%) and 624 staff (4.1%) tested positive for SARS-CoV-2. 12.7% of residents and 3.7% of staff without symptoms tested positive for SARS-CoV-2, compared to 53.1% of residents and 18.2% of staff with symptoms. Of the individuals who tested positive, 70.8% of residents and 92.4% of staff lacked symptoms at the time of testing. In aggregate, the distributions of Cts for viral probes used in the qRT-PCR assay were very similar, with a statistically but not meaningfully different mean (∆Ct 0.71 cycles, p = 0.006) and a similar range (12-38 cycles), between populations with and without symptoms over the entire time period, across all sub-categories examined (age, race, ethnicity, sex, resident/staff). Importantly, the Ct mean values and range were indistinguishable between the populations by symptom class during the peak of the outbreak in Massachusetts, with a Ct gap appearing only later in the survey period, reaching >3 cycles (p ≤ 0.001) for facilities sampled during the last two weeks of the study. Conclusions In a large cohort of individuals screened for SARS-CoV-2 by qRT-PCR, we found strikingly similar distributions of viral load in patients with or without symptoms at the time of testing during the local peak of the epidemic; as the epidemic waned, individuals without symptoms at the time of testing had lower viral loads. The size of the study population, including both staff and residents spanning a wide range of ages, provides a comprehensive cross-sectional point prevalence measurement of viral burden in a study spanning 2 months. Because the distributions of viral loads in infected individuals irrespective of symptomatology are very similar, existing testing modalities that have been validated for detection of SARS-CoV-2 RNA in symptomatic patients should perform similarly in individuals without symptoms at the time of testing.
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Comparison of viral levels in individuals with or without symptoms at
time of COVID-19 testing among 32,480 residents and staff of nursing
homes and assisted living facilities in Massachusetts
Niall J. Lennon1*, Roby P. Bhattacharyya1,2*, Michael J. Mina1,3,4, Heidi L. Rehm1,2,3,7, Deborah T.
Hung1,2,3,7, Sandra Smole5, Ann Woolley1,3, Eric S. Lander1,6,7,@ and Stacey B. Gabriel1,@
1Broad Institute of MIT and Harvard, Cambridge, MA
2Massachusetts General Hospital, Boston, MA
3Brigham and Women’s Hospital, Boston, MA
4Harvard T.H. Chan School of Public Health, Boston, MA
5State Public Health Laboratory, Massachusetts Department of Public Health, Boston MA
6Massachusetts Institute of Technology, Cambridge MA
7Harvard Medical School, Boston, MA
*These authors contributed equally
@The authors co-supervised the work
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. .https://doi.org/10.1101/2020.07.20.20157792doi: medRxiv preprint
Abstract
Background
Transmission of COVID-19 from people without symptoms poses considerable challenges to
public health containment measures. The distribution of viral loads in individuals with and
without symptoms remains uncertain. Comprehensive cross-sectional screening of all
individuals in a given setting provides an unbiased way to assess viral loads independent of
symptoms, which informs transmission risks. COVID-19 cases initially peaked in Massachusetts
in mid-April 2020 before declining through June, and congregate living facilities were particularly
affected during this early surge. We performed a retrospective analysis of data from a large
public health-directed outbreak response initiative that involved comprehensive screening within
nursing homes and assisted living facilities in Massachusetts to compare nasopharyngeal (NP)
viral loads (as measured by RT-PCR cycle threshold (Ct) levels) in residents and staff to inform
our ability to detect SARS-CoV-2 in individuals with or without symptoms in the population.
Methods
Between April 9 and June 9, 2020, we tested NP swabs from 32,480 unique individuals
comprising staff and residents of the majority of nursing homes and assisted living facilities in
Massachusetts. Under the direction of the MA Department of Public Health (MDPH),
symptomatology at the time of sampling and demographic information was provided by each
facility for each individual to facilitate reporting to health officials. NP swabs were collected, RNA
extracted, and SARS-CoV-2 testing performed using quantitative reverse-transcriptase
polymerase chain reaction (qRT-PCR).
Results
The nursing home and assisted living facilities resident cohort (N =16,966) was 65% female with
a mean age of 82 years (SD 13 yrs). The staff cohort (N = 15,514) was 76% female with a
median age of 45 (SD 15 yrs). A total 2654 residents (15.5%) and 624 staff (4.1%) tested
positive for SARS-CoV-2. 12.7% of residents and 3.7% of staff without symptoms tested positive
for SARS-CoV-2, compared to 53.1% of residents and 18.2% of staff with symptoms. Of the
individuals who tested positive, 70.8% of residents and 92.4% of staff lacked symptoms at the
time of testing. In aggregate, the distributions of Cts for viral probes used in the qRT-PCR assay
were very similar, with a statistically but not meaningfully different mean (∆Ct 0.71 cycles, p =
0.006) and a similar range (12-38 cycles), between populations with and without symptoms over
the entire time period, across all sub-categories examined (age, race, ethnicity, sex,
resident/staff). Importantly, the Ct mean values and range were indistinguishable between the
populations by symptom class during the peak of the outbreak in Massachusetts, with a Ct gap
appearing only later in the survey period, reaching >3 cycles (p ≤ 0.001) for facilities sampled
during the last two weeks of the study.
Conclusions
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In a large cohort of individuals screened for SARS-CoV-2 by qRT-PCR, we found strikingly
similar distributions of viral load in patients with or without symptoms at the time of testing during
the local peak of the epidemic; as the epidemic waned, individuals without symptoms at the time
of testing had lower viral loads. The size of the study population, including both staff and
residents spanning a wide range of ages, provides a comprehensive cross-sectional point
prevalence measurement of viral burden in a study spanning 2 months. Because the
distributions of viral loads in infected individuals irrespective of symptomatology are very similar,
existing testing modalities that have been validated for detection of SARS-CoV-2 RNA in
symptomatic patients should perform similarly in individuals without symptoms at the time of
testing.
Background
Despite the public health importance of coronavirus infectious disease 2019 (COVID-19), the
relationship between viral load, symptom severity, and transmission risk remain poorly
understood. As the primary focus on controlling community transmission of Severe Acute
Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) expands from existing outbreak response
to new outbreak surveillance, it is increasingly important to be able to perform accurate viral
testing in individuals that do not show COVID-19 symptoms at the time of testing1,2,3,4,5,6,7. While
still evolving, recent reports suggest that a substantial fraction of SARS-CoV-2 spread occurs
from such infected individuals without symptoms at the time of transmission8,9. Detecting such
individuals before they expose others to the virus could therefore play an important role in
limiting spread within a population.
To determine whether current testing methods are appropriate for testing individuals without
symptoms, it is necessary to understand the relative distributions of viral loads in patients with
and without symptoms. (We employ the commonly-used operational definition of viral load
based on the quantity of viral RNA in a patient specimen as measured by qRT-PCR; we
address limitations of this measure in the Discussion.) A major question with testing individuals
without symptoms at the time of sampling is whether their viral loads will be substantially lower
than in symptomatic individuals, as has been demonstrated for influenza10, so as to compromise
the reliability of existing assays for detecting virus in these infected individuals. Assays to detect
the presence of SARS-CoV-2 RNA in samples often have limits of detection between several
hundred to several thousand viral genomes per milliliter (mL)11.
To date, viral loads in individuals without symptoms have not been extensively studied because
testing has been primarily focused on individuals with symptoms12,13. In contrast to influenza,
where asymptomatic or paucisymptomatic individuals have been reported to have 10 to 100-fold
less virus than symptomatic individuals10, several recent small studies have found similar
SARS-CoV-2 RNA levels in infected individuals irrespective of symptoms. In one study of 30
individuals in quarantine, 13 asymptomatic individuals had the same viral loads as 17
symptomatic patients at baseline14. A second study of 37 hospitalized asymptomatic individuals
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also found similar viral loads as their symptomatic counterparts15. Among those who do show
symptoms severe enough to require hospitalization, one recent study showed that initial viral
load on admission was associated with increased risk for death or intubation12. However, given
the wide range of viral load over time within each patient811, higher viral load at the time of
admission could simply be a proxy for those who became sick earlier in the course of their
infection. Given the small number of asymptomatic patients in these studies, it is important to
study viral load data by symptom status across a larger population, including across age, sex,
baseline health status, and other factors such as comorbidities.
To understand the biological relationship between symptomatology and viral loads, certain study
designs are preferable. Systematic screens of all individuals in a given setting, without regard to
the presence of symptoms, are preferable to studies of individuals who present for testing or for
whom testing is ordered: the latter design is likely to involve significant selection bias, because
most people without symptoms are not currently tested, and those who are tested are unlikely to
be representative of the whole. Studies occurring relatively early in an epidemic are also
preferable to studies at later times, because the infection dates are likely to be more closely
synchronized.
As the local epidemic neared its peak in April 2020, in response to several large outbreaks, the
Commonwealth of Massachusetts initiated an aggressive, systematic program to perform
comprehensive viral testing of all staff and residents in all skilled nursing facilities and assisted
living facilities, regardless of whether individuals showed COVID-19 symptoms. For the majority
of these facilities, specimens (collected by nasopharyngeal swabs) were sent to either the
Massachusetts Department of Public Health State Public Health Laboratory (MASPHL) or its
contracted reference laboratory, the Clinical Research Sequencing Platform (CRSP) at the
Broad Institute for viral testing via a real-time qPCR assay. Between April 9 and June 9, 2020,
the Broad Institute laboratory performed 32,480 unique individual diagnostic tests on people
who were identified as residents or staff at 366 skilled nursing facilities and assisted living
facilities in Massachusetts. During the period of this study, overall COVID-19 burden in the state
peaked at >3000 confirmed cases per day on April 17 (week 2 of the study) and declined
thereafter, dropping over 7-fold by the end of the study period (Figure S1)16. For each individual,
the facility reported symptomatology at the time of sampling as ascertained by the onsite
physician or nursing staff, as well as basic demographic information to facilitate reporting to the
Massachusetts Department of Public Health.
These data (symptomatic status reported by the facility, demographic data, and viral load
measured by the RT-PCR assay) provide a large point-prevalence survey. Because the vast
majority of the individuals were sampled only once and longitudinal history was not available,
these data do not distinguish individuals who were durably asymptomatic from those who
subsequently developed symptoms and were thus presymptomatic at the time of testing. We
therefore refer to these people throughout as individuals without symptoms at the time of
testing, to clarify that we do not attempt to distinguish asymptomatic from presymptomatic
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infection. While these two groups have different implications for contact-tracing efforts, either
may transmit disease in the absence of symptoms18 and are thus crucial to study quantitatively.
The primary issue addressed in this paper is the comparison of estimated viral load distributions
(as measured by cycle threshold (Ct) for viral detection in the real-time RT-PCR assay) from
nasopharyngeal (NP) swabs between individuals with and without symptoms at the time of
testing. The Ct value measures the number of amplification cycles required to detect cDNA
produced from viral RNA; a higher Ct value indicates less viral RNA in the sample. While a
small, statistically significant difference between the populations could be detected over the
entire study period, notably, no difference was detected at the peak of the epidemic, with a
small gap between their mean Ct values emerging as the epidemic waned locally. The results
suggest that the distribution of viral load in infected individuals with or without symptoms at the
time of testing is similar, and thus assays that reliably detect virus in symptomatic individuals
should perform equally well for individuals without symptoms.
Methods
Study Population
Between April 9 and June 9, 2020, the Broad Institute’s CLIA-certified clinical laboratory
received NP swab specimens for SARS-CoV-2 testing from 366 skilled nursing facilities,
nursing homes, and assisted living facilities across the Commonwealth of Massachusetts.
Determination of the population selected for testing was based on the CDC 2019-Novel
Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel (CDC-006-00019, Rev 2)
instructions for use in which “2019-nCoV testing may be indicated as part of a public health
investigation”. Swabs were collected by trained staff onsite at the homes (a minority of samples)
or by the Massachusetts National Guard (MANG) (the majority of samples). To facilitate
collection, MANG deployed twelve medical teams each consisting of medics, decontamination
personnel, a non-commissioned officer in charge, and other support members of the MANG.
Eligibility criteria for testing were broad: the intention was to test every resident and every staff
member of every facility. Testing was performed on 32,480 unique individuals, with a small
proportion (6.7%) tested more than once during the period. For individuals tested more than
once, only data from the first test are reported here in order to avoid duplication.
Symptom and Demographic Information
Beginning in the second week of the testing program (labeled as Week 2, April 17-23), the
facility filled out a requisition form for each individual swabbed that asked whether the individual
did or did not show COVID-19 symptoms. While the requisition form did not request specific
details about the types or severity of symptoms and thus assessments may not be completely
uniform across facilities, a binary judgment of symptomatology was made by the facility’s trained
skilled nursing staff or physician on-site. Longitudinal information was not available about
whether individuals without symptoms at the time of collection previously had or later developed
symptoms. However, because most nursing facilities in Massachusetts during the study period
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required negative SARS-CoV-2 RT-PCR before accepting patients with known or suspected
COVID-19 in transfer from acute care hospitals, those testing positive would not include many
post-symptomatic, persistently positive individuals convalescing from known COVID-19.
For each individual swabbed, the requisition form also requested personal and demographic
information consisting of name, date of birth, race, ethnicity, sex, symptom status (symptomatic
or asymptomatic), and whether they were a resident or a staff member. In the minority of
responses where answers on the requisition form were blank or unclear, data were coded as
missing (see Table 1).
Laboratory Testing
NP swabs were placed in barcoded tubes with 3 mL of viral transport medium (VTM),
transported in coolers with ice packs, and delivered to the laboratory on the day of collection.
Symptomatology and demographic information listed on the test requisition form were entered
into a database and associated with the barcode on the specimen tube. RNA was extracted
from 50ul of VTM using the MagMax-96 RNA extraction kit (Thermo Fisher) on a Bravo liquid
handler platform (Agilent). One-step real-time reverse transcriptase–polymerase chain reaction
(RT-PCR) was performed on a QuantStudio 7 (Applied Biosystems), using a laboratory
developed SARS-CoV2 CDC assay protocol run under the FDA’s Emergency Use Authorization
framework; cycle threshold (Ct) values were reported for two viral probes, the N1 and N2 viral
nucleocapsid protein gene regions, and a RNaseP human gene control (RP)17). Ct values lower
than 40 cycles for both N1 and N2 indicate a diagnostic qualitative positive result for
SARS-CoV-2 (a single positive viral probe was reported as Inconclusive). Viral loads
(copies/mL) were estimated by interpolation from a standard curve generated by serial dilutions
of a synthetic RNA construct (Twist Biosciences, CA) containing the viral N2 target sequence;
the Ct values correlated strongly with the logarithm of RNA concentration (R2 > 0.99), with the
observed range from Ct =12 cycles to Ct = 38 cycles corresponding to viral loads ranging from
~1.9 billion copies/mL to 8 copies/mL, respectively.
Analyses
The distribution of Ct values were plotted as a function of various metadata. For simplicity of
analysis and presentation the Ct values for the N1 and N2 probes in positive patients were
averaged. One-way ANOVA and pooled t-tests were performed between subpopulations to
determine the significance of differences in Ct values. All analyses were completed with SAS
JMP software, version 13 (SAS Institute). Internal Ct data were collected as part of the
diagnostic efforts as part of this public health response and were deemed exempt human
subjects research by the Broad Institute Office of Research Subject Protection and approved
with waiver of informed consent by the MA Department of Public Health’s Institutional Review
Board.
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Results
Summary
Across all facilities, 2654 residents (15.5%) and 624 staff (4.1%) tested positive for
SARS-CoV-2. Among the residents, 78.6% were listed as asymptomatic at the time of swabbing
and 7.8% were listed as symptomatic (remaining resident forms did not indicate
symptomatology). Among the staff, 78.1% were listed as asymptomatic at the time of swabbing,
and 1.3% were listed as symptomatic (remaining staff forms did not indicate symptomatology).
Mean age of residents was 82 years (SD 13, range 17 to 114), while mean age of staff was 45
years (SD 15, range 16 to 101). Table 1 shows the demographics and aggregate results for the
resident and staff cohorts.
Positive rates by symptom class
Among 13,341 residents who lacked symptoms at the time of swabbing, 1692 (12.7%) were
positive, compared with 487 (3.7%) of 12,724 staff without symptoms. 699 (53.1%) of 1316
residents with symptoms tested positive, compared with 40 (18.2%) of 220 staff with symptoms
(Table 2a).
Symptomatology by test results
Of the individuals who tested positive, a substantial majority lacked symptoms at the time of
sampling, including 1692 (70.8%) of 2391 residents and 487 (92.4%) of 527 staff (Table 2b).
Comparison of viral load between individuals with and without symptoms at the time of
testing over the entire 6 week study period
Among individuals who tested positive, over the entire time period, the Ct levels for viral load
(as an average of the N1 and N2 probes) covered a broad range, from 11.6 to 37.7 cycles in
individuals without symptoms and 11.9 to 37 cycles in individuals with symptoms (Figure 1a,b),
while the Ct for the human host probe (RP) was more tightly distributed around a mean of 28.9
(SD 2.4) and 28.1 (SD 2.7) cycles for each population (Figure 1c,d).
The distributions for the viral level differed slightly between individuals with and without
symptoms, with a difference in mean Ct of only 0.71 cycles (26.4 vs 25.7, p=0.006) and a
slightly higher proportion of individuals with Ct ≥ 30 cycles (36% for individuals without
symptoms vs. 29.2% for those with symptoms) (Figure 1a,b). Similarly, the mean Ct for the
human host probe differed by 0.74 cycles (p=0.0001) between these two populations (Figure
1c,d).
Despite the statistically detectable differences, both individuals with and without
symptoms show substantially similar distributions down to the limit of detection of the assay,
with only a small difference in mean Ct value. For context, test developers and the FDA typically
use a Ct difference of <3 cycles as an indicator of substantial equivalence between viral testing
methods. Furthermore, the observed differences in Ct are less than the typical variability in
sampling efficiency, as reflected in the RP probe Ct distributions (SD 2.4 and 2.6 cycles).
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Cumulative distributions of virus abundance
The ~250 million-fold range of viral loads observed (from Ct of 12 to 38 for the N2 probe) is
consistent with prior studies18,19,20,21. This wide range implies that the vast majority of total viral
load in the population sampled was carried by a minority of individuals with the lowest Ct values.
Following an analysis in a recent report18, we calculated the proportion of total viral load carried
by those individuals with the highest x% of viral load, for each value of x, by weighting the
number of people in each bin of Ct values by the viral load corresponding to each bin of Ct
values (Figure 2). For individuals without symptoms, 9% of individuals (those with Ct ≤ 17
cycles) harbored 90% of the total virus in the population sampled, and 24% of individuals (those
with Ct ≤ 20 cycles) harbored 99%. Similar values were seen for the set of individuals with
symptoms (Figure 2, inset).
Variation of viral loads over time
When the distribution of viral loads between individuals with and without symptoms was
compared over time, on a weekly basis, no difference was observed between the two
populations, either in mean Ct value or range, during the time period that coincided with the
peak outbreak of COVID-19 in Massachusetts (April 17-23) (Figure 3; Figure S1). However, with
time, a gap emerged, with mean Ct value shifting higher in the population without symptoms
while remaining essentially unchanged for symptomatic patients throughout the testing period.
Specifically, individuals without symptoms tested in the last two weeks of this study during
weeks 5 and 6 (May 7-20) had Ct values >3 cycles greater (less virus) than symptomatic
individuals (p = 0.0013 and 0.0007 for weeks 5 and 6, respectively). These weeks
corresponded to a waning of the epidemic in Massachusetts, as weekly case counts declined by
7-fold from the beginning to the end of the study period.
Effect of age and other demographic variables
Since age dramatically affects COVID-19 severity22, to examine whether age modified the
relationship between viral load and symptom class, we partitioned individuals by decade of life
and assessed mean viral level in the resident and staff cohorts separately (Table 3). We
observed that symptomatology does not significantly alter viral level in any of the age groups
over the entire study period, regardless of whether the person is a staff member or a resident.
We also looked at the percentage of positive cases by symptom class across different age
groups for both residents and staff (Figure 4). Notably, in each age group, the majority of
positive results occurred in persons listed as having no symptoms at the time of swabbing.
Other available demographic variables (sex, race, ethnicity, resident vs staff) were
examined to see if they modified the relationship between viral load between individuals with
and without symptoms at the time of testing (Figure 5), since social determinants of health2324
and baseline health status22 also impact COVID-19 outcomes. Again, statistically significant but
numerically small differences were observed between those with and without symptoms in some
categories (∆Ct = 0.75 cycles overall, range 0.8 - 1.2 cycle difference among demographic
classes with p < 0.05).
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Discussion
COVID-19 has become a devastating pandemic because of its considerable morbidity and
mortality25 coupled to its frequent spread from individuals who do not show symptoms at the
time of transmission 8,25. Control measures that aim to detect a substantial portion of
transmission events thus require diagnosis of infected patients who do not display symptoms at
the time of testing26. Prior reports of large-scale cross-sectional SARS-CoV-2 testing have not
reported quantitative viral load in individuals without symptoms at the time of testing.
By comprehensively screening 16,966 residents and 15,514 staff of residential nursing facilities
in Massachusetts, while capturing simultaneous symptom classification from healthcare
providers, we generated quantitative RT-PCR data from 2179 and 739 subjects without and with
symptoms, respectively, the largest cohort of individuals without symptoms at the time of testing
reported to date. The Ct distributions between the two populations over the entire time period
were remarkably similar. They were essentially identical in the week corresponding to the peak
of the outbreak, with a general shift to slightly higher Ct values (lower viral loads) in individuals
without symptoms as the epidemic waned in Massachusetts. Over the entire time period, no
clinically meaningful differences were observed overall, nor in each subgroup examined by age,
sex, race, and ethnicity, despite some of the comparisons reaching statistical significance. By
testing a large number of both residents and staff of nursing facilities, our study reports on viral
load in a vulnerable subpopulation at risk for severe illness and death, as well as a younger and
generally healthier staff population, with neither group exhibiting a meaningful overall difference
in average Ct between individuals with and without symptoms at the time of testing.
As a group, individuals without symptoms at the time of testing had a distribution of viral loads
similar to those measured in individuals with symptoms. There has been much discussion about
potential heterogeneity in individuals who are labelled as asymptomatic at a single point in time,
as they could be presymptomatic and will go on to develop symptoms in the future,
post-symptomatic and are recovering, or durably asymptomatic and will never develop
symptoms1. While this heterogeneity cannot be resolved without longitudinal follow-up, our
point-prevalence study found that individuals without symptoms at the time of testing had viral
loads that were similar to those from individuals with symptoms, with the viral loads being nearly
identical during the peak of an epidemic when the time since infection acquisition is most similar
between the two classes. These results suggest that the assay should be effective in detecting
new infections in individuals without symptoms at the time of testing.
For individuals both with and without symptoms, viral loads detected on nasopharyngeal swabs
varied by more than 250 million-fold, consistent with prior studies18,19,20,21 (Figure 1). The
variation in viral load is much greater than seen for other factors that may affect infectivity (for
example, the number of droplets expelled can vary by 100-fold across hosts27). Consistent with
results for other respiratory illnesses28 and preliminary data for COVID-19 2930, it is plausible (but
not proven) that infectivity of individuals with SARS-CoV-2 may be roughly proportional to viral
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load11. If so, a minority of individuals might account for the vast majority of transmission (Figure
2). For example, we calculated that 8-9% of individuals (either with or without symptoms)
harbored 90% of the total viral load at the time of testing — which may partially explain the
phenomenon of superspreading. Interestingly, our estimates are similar to recent inferences
based on modeling of overdispersion in transmission, in which 10% of individuals may account
for 80% of infections.31 (By contrast, the half of individuals with the lowest viral loads, those with
Ct >26, carry only ~0.01% of total viral load at the time of testing.)
The mean and distribution of Ct values for individuals with and without symptoms showed
increasing differences over the 2 month duration of this study (Figure 3). The two groups were
nearly identical in the first week in which symptom data were collected (Week 2 of the screening
program, April 17-23), which corresponded to a few weeks after the statewide count of new
cases began rising sharply (Figure S1). The mean viral load observed in individuals without
symptoms then became lower in subsequent weeks, as the local prevalence subsided over the
course of May. In fact, such a shift in the relationship between viral load and symptoms is
expected based on local epidemic dynamics due to two factors. First, viral load changes over
the course of an infection, increasing rapidly and then waning slowly over the course of
weeks8,32,33, with the result that the viral loads observed at a given point in time will depend on
the distribution of time since infection for the population studied. Second, in individuals who
eventually develop them, symptoms are typically displayed within a limited time period early in
the course of viral shedding, close to the peak of viral shedding8. As a consequence, even if the
distribution of viral levels over time is identical between individuals who will and will not
eventually develop symptoms, the set of individuals with symptoms at any given time will tend to
be skewed toward more recent infections and thus higher viral levels compared to individuals
without symptoms.
During the rapid initial growth phase of a local epidemic, and particularly in congregate settings
where onset may be more synchronous, the skew in time-since-infection between those with
and without symptoms might be expected to be modest because the vast majority of infections
are recent. As a local epidemic stabilizes or declines, the skew would be expected to increase.
Our data are consistent with this expectation, as the distribution of viral load in individuals
without symptoms showed an increasing proportion of individuals with low viral loads over time,
who are likely enriched for cases later in the course of infection.
Because the expected skew in time-since-infection is minimized in the rapid initial growth phase
of a local epidemic, this period may provide a better representation of the prospective
distribution of viral loads across individuals infected at roughly the same time. The fact that the
distribution of viral load was initially nearly identical in individuals with and without symptoms
suggests that whether an individual develops symptoms may not be primarily determined by
viral load, but rather by other factors. However, longitudinal studies of both viral burden and
symptoms are needed to clarify the relationship between viral load, symptoms, and clinical
severity.
10
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Notably, the majority of positive tests from both residents (70.8%) and staff (92.4%) came from
individuals without symptoms at the time of testing. While each group of individuals may have
been somewhat depleted for symptomatic COVID-19 (residents with severe symptoms may
have been transferred to hospitals, while most symptomatic staff would likely have been
required to stay home), these large percentages are consistent with smaller-scale
cross-sectional studies in Iceland 7, Italy34, congregate facilities 5,6,35,36,37, labor and delivery
wards in high-incidence cities 2,3,4, and the Diamond Princess cruise ship 38,39,40. However, the
percent of truly asymptomatic infection remains an issue of much debate; smaller-scale
longitudinal monitoring studies in a variety of settings vary considerably in their reported rates of
symptom development among individuals who lacked symptoms at the time of a positive test
36,38,39,40,41,37. Nevertheless, modeling studies suggest that a substantial fraction of transmission
occurs from people who are not symptomatic at the time, whether asymptomatic or
pre-symptomatic8, which is reinforced by contact-tracing studies 14,42,43. Together, these findings
underscore the need to expand beyond symptom-based screening as a sole tactic for detecting
infected individuals and preventing transmission.
This study should be interpreted with certain caveats. First, without longitudinal follow-up, we
cannot distinguish infected individuals who are permanently asymptomatic from those who are
pre-symptomatic. However, both classes likely carry risk for transmitting the virus in the absence
of symptoms 8,14,42 even while differing in their implications for contact tracing and for
understanding the natural history of COVID-191. Quantifying the viral burden in individuals
without symptoms at the time of testing is thus an important step towards better understanding
their transmission risk relative to symptomatic individuals. Second, with only a binary
point-prevalence assessment of symptoms at the time of testing, we cannot draw any
conclusions about the relationship between viral load and concurrent or future symptom severity
in this population; however, the similarity in viral load distributions between individuals with and
without symptoms suggests that viral load may not be the sole determinant of symptoms. These
are important avenues for future study in longitudinal studies. Third, nursing home residents and
staff may differ with respect to stages or disease severity from other populations, such as
severely symptomatic individuals presenting to an acute setting for testing or requiring
hospitalization12, or asymptomatic individuals in different settings. Nonetheless, these data
represent Ct values for non-hospitalized individuals who did not seek acute testing, which
represents the majority of COVID-19 cases and the vast majority of those at risk for ongoing
transmission. Fourth, the widely-used approach of defining viral load based on RNA levels
measured in specimens may not precisely reflect the number of live virions carried by an
individual for several reasons. The assay may not reflect viral loads in other sites in the body
and does not distinguish the genomic RNA of live virus from intact RNA from inactive or killed
virus, which are thought to explain the long tail of low-level positive tests often seen during
recovery30. In principle, the RNA level in a specimen could reflect both levels of full-length
genomic RNA and subgenomic expression of the gene. (Expression has been reported to vary
by ~100-fold across the viral genome, with the N gene, targeted here, having higher levels44;
however, this is much smaller than the >108-fold differences in RNA levels observed across
individuals.)
11
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While our study found similar overall distributions in individuals with and without symptoms, our
observation that the distributions began to diverge from the peak to later stages of the local
epidemic suggests that substantial differences may be observed in other settings. We expect
that the distributions seen in other settings will depend on both the selection of individuals for
testing and the stage of the epidemic. Our study design — a cross-sectional study based on
comprehensively sampling all individuals, independent of symptoms, at an early stage of the
epidemic, when many cases are of similar age — is well-suited for understanding the
prospective distribution of viral loads across infected individuals. At the opposite extreme, a
cross-sectional study that tested inpatients who had been hospitalized for varying lengths of
time due to severe COVID-19 symptoms would be expected to show a large difference in viral
loads between individuals with and without symptoms, because those patients without
symptoms at the time of testing would be entirely composed of later-stage recovering patients,
in whom viral loads would be low. Similarly, if outpatients with a known exposure are tested
either at the onset of symptoms for those who develop symptoms or at the end of a period of
self-quarantine for “clearance” if they do not develop symptoms, the distributions of viral load in
individuals with and without symptoms would be expected to differ substantially due to
differences in average time since infection, not necessarily due to intrinsic differences in biology
between the groups.
In summary, the majority of residents and the vast majority of staff who tested positive reported
no symptoms at the time of sampling, and the viral loads in those with and without symptoms
showed very similar distributions, particularly early in the study during the peak of the local
epidemic. With testing of asymptomatic individuals under consideration in many settings,
including contact tracing by public health departments and screening in workplaces or schools,
a quantitative assessment of viral burden in individuals without symptoms is crucial to inform the
viability of such screening strategies. While optimal implementation strategies and
cost-effectiveness must be carefully considered, the finding of relatively similar viral load
between infected individuals with and without symptoms at the time of testing builds confidence
in the technical feasibility of identifying asymptomatic individuals harboring SARS-CoV-2 by
standard RT-PCR assays.
12
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Table 1. Numbers of individual participants in each of the categories for demographic variables
collected on the diagnostic test requisition form. % of the category within each participant type
(Resident or Staff) is also shown. Individual participant ages were grouped into decade of life to
preserve anonymity. Test results for each participant type category is also shown. Positive
indicates the detection of SARS-CoV-2 in the specimen. Negative indicates no detectable
SARS-CoV-2 in the specimen. Inconclusive indicates a case where one viral probe (N1 or N2) is
positive but the other is negative. The human RP probe must be positive for a specimen with
negative viral probes to be called negative. Otherwise that test would be called invalid.
13
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Table 2. Test results in Residents and Staff are shown as a function of symptomatology (2a).
Resident and staff symptomatology is also shown as a function of the test result (2b).
14
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Figure 1. Cumulative and actual distribution of Cts by Symptom class across all unique
individuals tested. Histograms of Cts for the N and RP probes are shown in (a) and (c)
respectively, with each bar indicating samples with values between the tick marks. No symptom
distribution (blue) is shown above the line with the symptomatic distribution (red) below the line
in both cases. Panels b and d indicates the cumulative distribution of the N probes (averaged
across N1 and N2) (b) and the RP probe (d) colored by symptom class (Red = symptomatic;
Blue = No symptoms).
15
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Figure 2. Cumulative distribution of total viral load across individuals (red, individuals with
symptoms; blue, individuals without symptoms). The fraction of total viral load (y-axis) harbored
by individuals with a given Ct value was calculated by multiplying the number of individuals with
each Ct value by the viral load corresponding to that Ct value, and then normalizing these
numbers to sum to 1. These fractions were then used to create the cumulative distribution plot,
with people ordered from highest to lowest viral load (lowest to highest Ct value) along the
x-axis. Dotted lines correspond to 90%, 99%, 99.9%, and 99.99% of cumulative viral burden,
with the corresponding percentage of individuals tabulated in the inset, along with the
corresponding Ct threshold.
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Figure 3. (a) Distribution of Cts by Symptom class across unique individuals tested by week of
study (Week 2, the first week in which symptom data were collected, spans April 17-23, 2020;
Week 3 spans April 24-30; and so on) by symptom status (Blue = no symptoms at time of
testing, Red = symptoms at time of testing). Weeks with fewer than 20 data points in either
category are not shown. Week 1 data is not shown as symptom class was not captured in the
first week of testing. (b) Cumulative distribution plots of the data from (a) overlaid (Blue = no
symptoms at time of testing, Red = symptoms at time of testing) (c) Box-plots of the average
viral N probe (N1 and N2) Ct by week and symptom class, with vertical line at median, colored
boxes at IQR, and whiskers showing full range. Asterisks indicate statistically significant
differences within a sub-category. Table of sample size and mean Ct with standard deviation
(SD) is shown in (c). Also shown is the sub-category ∆Ct between the symptomatic and no
symptom cases and the associated p-value.
17
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Table 3. Examination of the mean N probe (average of N1 and N2) Ct by age group across
residents and staff and symptom class. SD: standard deviation.
18
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Figure 4. Percentage of the positive cases within the staff and resident groups that had no
symptom at each age group level.
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Figure 5. Ct distributions across demographic and symptom categories. Any sub-category with
<20 data points was removed. Box-plots of the average viral N probe (N1 and N2) Ct by
category and symptom class are shown in (a), with vertical line at median, colored boxes at
IQR, and whiskers showing full range. Asterisks indicate statistically significant differences
within a sub-category. Table of sample size and mean Ct with standard deviation (SD) is shown
in (b). Also shown is the sub-category ∆Ct between the symptomatic and no symptom cases
and the associated p-value.
20
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Figure S1. Time course of outbreak in MA. Daily confirmed cases (by date of sample collection)
over time in Massachusetts, along with a 7-day moving average. Data taken from
Massachusetts Department of Public Health COVID-19 Dashboard as of 7/14/2020 (see
https://www.mass.gov/info-details/archive-of-covid-19-cases-in-massachusetts).
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... Importantly, viral load, which spans 6 orders of magnitude or more [4][5][6], is emerging as an important transmission risk factor [7]. ...
... Given the many variables associated with determining the asymptomatic infection rate, including evolving symptom definition, demographic characteristics, study population size, and testing penetrance, estimates of the asymptomatic fraction vary substantially. However, studies with high testing penetrance or incorporation of large population serology data indicate that 40-90% of cases may be asymptomatic [4,[8][9][10]. Importantly, asymptomatic infected individuals still transmit virus and have viral loads equivalent to symptomatic individuals, yet are expected to be more mobile because they are not experiencing illness [6,8,11,12]. ...
... Therefore, in investigating SARS-CoV-2 transmission, it is critical to University residence hall roommates are an example of dense households, typically with shared bedrooms, bathrooms, and dining facilities. Moreover, they are also a young adult population with a higher likelihood of being asymptomatic and lower rates of co-morbidities linked to severe disease [4,9,14]. Thus, they are ideal for investigating the extent of transmissibility from asymptomatic or mildly symptomatic cases. ...
Article
Background The COVID-19 pandemic spread to over 200 countries in less than six months. To understand COVID spread, determining transmission rate and defining factors that increase transmission risk are essential. Most cases are asymptomatic, but they have viral loads indistinguishable from symptomatic people and do transmit SARS-CoV-2 virus. However, they are often undetected. Methods Given high residence hall student density, the University of Colorado Boulder established a mandatory weekly screening test program. We analyzed longitudinal data of 6408 students and identified 116 likely transmission events in which a second roommate tested positive within 14 days of the index roommate. Results Although the infection rate was lower in single rooms (10%) than in multiple-occupancy rooms (19%), inter-roommate transmission only occurred ~20% of the time. Cases were usually asymptomatic at the time of detection. Notably, individuals who likely transmitted had an average viral load ~6.5-fold higher than individuals who did not (mean Cq 26.2 vs 28.9). Although diagnosed students moved to isolation rooms, there was no difference in time-to-isolation between cases with or without inter-roommate transmission. Conclusions This analysis argues that inter-roommate transmission occurs infrequently in residence halls and provides strong correlative evidence that viral load is proportional to transmission probability.
... The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic remains a major global health challenge which limits universities' academic and research activities, thereby forcing the implementation of new teaching and working paradigms (online distance learning, etc.). Interestingly, SARS-CoV-2 transmission at the university has been associated with the well documented infectivity of asymptomatic individuals, many of them being pre-symptomatic with high viral loads [1][2][3][4][5][6]. Those asymptomatic carriers are likely to be responsible for as many as 44% of new infections [7]. ...
... These individuals constituted the 0.22% (6 out of 2775, 95%CI: 0.10-0.47%), likely considered as asymptomatic cases with high viral loads, and thus potential transmitters, as discussed earlier [1][2][3][4][5][6]. ...
... Asymptomatic and paucymptomatic individuals can unknowingly transmit the virus and fuel covert outbreaks [5,23,24]. Our results show that the prevalence of asymptomatic SARS-CoV-2 infected individuals (1.94%) was low, as expected, but this finding does not guarantee safety. ...
Full-text available
Article
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic started in December 2019 and still is a major global health challenge. Lockdown measures and social distancing sparked a global shift towards online learning, which deeply impacted universities’ daily life, and the University of Barcelona (UB) was not an exception. Accordingly, we aimed to determine the impact of the SARS-CoV-2 pandemic at the UB. To that end, we performed a cross-sectional study on a sample of 2784 UB members (n = 52,529). Participants answered a brief, ad hoc, online epidemiological questionnaire and provided a nasal swab for reverse transcription polymerase chain reaction (RT-PCR) SARS-CoV-2 analysis and a venous blood sample for SARS-CoV-2 IgG antibody assay. Total prevalence of SARS-CoV-2 infection (positive RT-PCR or positive IgG) was 14.9% (95%CI 13.3 to 17.0%). Forty-four participants (1.6%, 95%CI: 1.2–2.1%) were positive for SARS-CoV-2 RT-PCR. IgG against SARS-CoV-2 was observed in 12.8% (95%CI: 11.6–14.1%) of participants. Overall, while waiting for population vaccination and/or increased herd immunity, we should concentrate on identifying and isolating new cases and their contacts.
... The above specified comorbidities, however, are inadequate to explain why age is a risk factor in relenting to COVID-19, in its own right. Besides the elevated risk of older people succumbing to SARS-CoV-2 infection, there are several studies that suggest a great degree of difference in the sickness outcome between younger and older SARS-CoV-2 patients (Yang R et al., 2020;Zhang X et al., 2020;Lennon et al., 2020;Jung et al., 2020;Liu Yet al., 2020). In fact, despite comparable viral loads, younger COVID-19 patients are likely to be asymptomatic than older patients, according to a cross-sectional analysis of residents, and employees in nursing homes and assisted living facilities (Lennon et al., 2020). ...
... Besides the elevated risk of older people succumbing to SARS-CoV-2 infection, there are several studies that suggest a great degree of difference in the sickness outcome between younger and older SARS-CoV-2 patients (Yang R et al., 2020;Zhang X et al., 2020;Lennon et al., 2020;Jung et al., 2020;Liu Yet al., 2020). In fact, despite comparable viral loads, younger COVID-19 patients are likely to be asymptomatic than older patients, according to a cross-sectional analysis of residents, and employees in nursing homes and assisted living facilities (Lennon et al., 2020). In Shanghai, a systematic analysis of clinical, genetic, and immunological data from 326 confirmed COVID-19 cases revealed that, among other factors, age was substantially associated with poor clinical outcomes . ...
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During the last 2 years, the entire world has been severely devastated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) as it resulted in several million deaths across the globe. While the virus infects people indiscriminately, the casualty risk is higher mainly in old, and middle-aged COVID-19 patients. The incidences of COVID-19 associated co-morbidity and mortality have a great deal of correlation with the weakened and malfunctioning immune systems of elderly people. Presumably, due to the physiological changes associated with aging and because of possible comorbidities such as diabetes, hypertension, obesity, cardiovascular, and lung diseases, which are more common in elderly people, may be considered as the reason making the elderly vulnerable to the infection on one hand, and COVID-19 associated complications on the other. The accretion of senescent immune cells not only contributes to the deterioration of host defense, but also results in elevated inflammatory phenotype persuaded immune dysfunction. In the present review, we envisage to correlate functioning of the immune defense of older COVID-19 patients with secondary/super infection, increased susceptibility or aggravation against already existing cancer, infectious, autoimmune, and other chronic inflammatory diseases. Moreover, we have discussed how age-linked modulations in the immune system affect therapeutic response against administered drugs as well as immunological response to various prophylactic measures including vaccination in the elderly host. The present review also provides an insight into the intricate pathophysiology of the aging and the overall immune response of the host to SARS-CoV-2 infection. A better understanding of age-related immune dysfunction is likely to help us in the development of targeted preemptive strategies for deadly COVID-19 in elderly patients.
... Individuals who are SARS-CoV-2 positive but asymptomatic can still be infectious [12][13][14] , and may exhibit a similar viral load to their symptomatic counterparts 12,13,15 . We therefore identified 375 positive cases who were evaluated for COVID-19 symptoms at testing to assess the relationship between symptom presentation and Ct values (Fig. 3A). ...
... Although both groups exhibited Ct peaks around 19-22, there was a noticeable rightward shift in the cumulative Ct frequency in the asymptomatic versus symptomatic population, indicative of reduced viral load in the asymptomatic group (Fig. 3E). In comparison, other studies with cohorts differing in location and in constituents, including a large study involving senior citizens from nursing houses and assisted living facilities in Massachusetts, found that Ct values did not differ significantly between the symptomatic and the asymptomatic individuals; but observed a faster virus clearance, as measured by Ct value, in the asymptomatic cases than in the symptomatic cases 13,15 . These and our studies thus suggest that infections with a higher viral load may more likely lead to symptom development, or that symptomatic persons tend to have higher viral loads or to maintain their viral loads for a longer time. ...
Article
SARS-CoV-2 is highly contagious, and the global spread has caused significant medical/socioeconomic impacts. Other than vaccination, effective public health measures, including contact tracing, isolation and quarantine, is critical for deterring viral transmission, preventing infection progression and resuming normal activities. Viral transmission is affected by many factors but the viral load and vitality could be among the most important ones. Although in vitro studies have indicated that the amount of virus isolated from infected people affects the successful rate of virus isolation, whether the viral load carried at the individual level would determine the transmissibility was unknown. From the diagnostic point of view, we aimed to examine whether the Ct value, a measurement of viral load by RT-PCR assay, could differentiate the spreaders from the non-spreaders in a population of college students. Our results indicate that while at the population level the Ct value is lower, suggesting a higher viral load, in the symptomatic spreaders than that in the asymptomatic non-spreaders, there is significant overlap in the Ct values between the two groups. Thus Ct value, or the viral load, at the individual level could not predict the transmissibility. Our studies also suggest that a sensitive method to detect the presence of virus is needed to identify asymptomatic persons who may carry a low viral load but can still be infectious.
... To demonstrate the potential of this method with a single cross-section from a closed population, we first investigate how the distribution of Ct values and prevalence of PCR positivity changed over time in four well-observed Massachusetts long-term care facilities that underwent SARS-CoV-2 outbreaks in March and April 2020 (29). In each facility, we have the results of near-universal PCR testing of residents and staff from three time points after the outbreak began, including the number of positive samples, the Ct values of positive samples, and the number of negative samples (Materials and Methods: Long-Term Care Facilities Data). ...
... Ct values for N1 and N2 gene targets were provided along with sample collection date, a random tube ID, and a unique anonymized institute ID to reflect that specimens came from distinct institutions. The specimens used here originated in early 2020 when public health efforts in Massachusetts led to comprehensively serial testing senior nursing facilities as described previously (29). Swabs from those public health efforts were processed for clinical diagnostics. ...
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Added value of PCR testing for COVID-19 During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, polymerase chain reaction (PCR) tests were generally reported only as binary positive or negative outcomes. However, these test results contain a great deal more information than that. As viral load declines exponentially, the PCR cycle threshold (Ct) increases linearly. Hay et al. developed an approach for extracting epidemiological information out of the Ct values obtained from PCR tests used in surveillance for a variety of settings (see the Perspective by Lopman and McQuade). Although there are challenges to relying on single Ct values for individual-level decision-making, even a limited aggregation of data from a population can inform on the trajectory of the pandemic. Therefore, across a population, an increase in aggregated Ct values indicates that a decline in cases is occurring. Science , abh0635, this issue p. eabh0635 ; see also abj4185, p. 280
... Taken together, the current study examined how four trait-based risk and resilience factors and six state-based coping strategies influenced the trajectory of OC symptoms during the Spring surge of COVID-19 (which occurred between April 03, 2020 and June 17, 2020) and the Fall/Winter surge (between November 27, 2020 andMarch 16, 2021) in the Northeast region of the US, based on > 30 deaths per day within a 7-day average (Hawkins et al., 2020;Lennon et al., 2020;Reale et al., 2021). Data also indicate that the week of the surge peak period was between April 15, 2020 and April 21, 2020 (Krieger et al., 2020), which coincided with the first week that the study was launched. ...
Article
The COVID-19 pandemic may exacerbate common symptoms of obsessive-compulsive disorder, such as fears of contamination or causing harm to others. To investigate the potential impact of COVID-19 on obsessive-compulsive (OC) symptoms, we utilized a frequent sampling prospective design to assess changes in OC symptoms between April 2020 and January 2021. We examined in a broad clinical and non-clinical sample whether baseline risk (e.g., emotion dysregulation, anxiety sensitivity, intolerance of uncertainty) and protective (e.g., resilience) factors would predict OC symptom changes, and whether coping strategies would mediate week-to-week changes in COVID-19 impact and OC symptoms. Emotion dysregulation was associated with greater likelihood of OC symptom worsening, whereas resilience was associated with lower likelihood. Longitudinal mediation analyses revealed that coping strategies were not significant mediators; however, changes in adaptive coping were associated with subsequent-week OC symptom reductions. Regardless of perceived COVID-19 impact, implementing adaptive coping strategies may prospectively reduce OC symptoms. Supplementary information: The online version contains supplementary material available at 10.1007/s41811-021-00128-4.
... Notably, as in the Boulder cohort, just 2% of individuals harbored 90% of the virus and a single individual harbored more than 5% of the virions (Fig. 1). Lack of correlation between COVID-19 symptomatology and SARS-CoV-2 viral load has been reported before in multiple contexts throughout the pandemic (13,18). In most people, then, the ability to control SARS-CoV-2 replication does not distinguish people with asymptomatic infection from those with severe COVID-19. ...
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It is breathtaking to consider how the response to pandemic viral pathogens has been transformed over the past century by greater knowledge of fundamental biology and technological innovations including PCR and next-generation sequencing. In striking contrast to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the pathogen responsible for the 1918 influenza pandemic was not identified until years after the outbreak. The definitive text in 1927 described influenza as “an epidemiologic conception” likely caused by the bacterium Haemophilus influenzae. Six decades later, HIV-1 was discovered within a few years of the first report of AIDS, although it took another decade before HIV-1 RNA detection methods were sensitive enough to correlate viral load during clinical latency with rate of progression to AIDS. Four decades later, the genomic sequence of SARS-CoV-2 was publicly available on the internet within weeks of the unexplained outbreak of fatal pneumonia that is now known as COVID-19. This critical information enabled academic researchers, vaccine manufacturers, diagnostic laboratories, and some governments to spring into action. In the midst of COVID-19 lockdown, despite collapse of reagent supply chains, independent investigators around the world shared expertise and reagents in order to establish desperately needed local screening programs for SARS-CoV-2. A paper by Yang et al. in PNAS describes the analysis of viral load data from one local screening program, the results of which have important implications for efforts to control the spread of SARS-CoV-2 and for understanding the pathogenesis of SARS-CoV-2 infection.
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Background: Risk of severe COVID-19 increases with age, is greater in males, and is associated with lymphopenia, but not with higher burden of SARS-CoV-2. It is unknown whether effects of age and sex on abundance of specific lymphoid subsets explain these correlations. Methods: Multiple regression was used to determine the relationship between abundance of specific blood lymphoid cell types, age, sex, requirement for hospitalization, duration of hospitalization, and elevation of blood markers of systemic inflammation, in adults hospitalized for severe COVID-19 (n=40), treated for COVID-19 as outpatients (n=51), and in uninfected controls (n=86), as well as in children with COVID-19 (n=19), recovering from COVID-19 (n=14), MIS-C (n=11), recovering from MIS-C (n=7), and pediatric controls (n=17). Results: This observational study found that the abundance of innate lymphoid cells (ILCs) decreases more than 7-fold over the human lifespan - T cell subsets decrease less than 2-fold - and is lower in males than in females. After accounting for effects of age and sex, ILCs, but not T cells, were lower in adults hospitalized with COVID-19, independent of lymphopenia. Among SARS-CoV-2-infected adults, the abundance of ILCs, but not of T cells, correlated inversely with odds and duration of hospitalization, and with severity of inflammation. ILCs were also uniquely decreased in pediatric COVID-19 and the numbers of these cells did not recover during follow-up. In contrast, children with MIS-C had depletion of both ILCs and T cells, and both cell types increased during follow-up. In both pediatric COVID-19 and MIS-C, ILC abundance correlated inversely with inflammation. Blood ILC mRNA and phenotype tracked closely with ILCs from lung. Importantly, blood ILCs produced amphiregulin, a protein implicated in disease tolerance and tissue homeostasis. Among controls, the percentage of ILCs that produced amphiregulin was higher in females than in males, and people hospitalized with COVID-19 had a lower percentage of ILCs that produced amphiregulin than did controls. Conclusions: These results suggest that, by promoting disease tolerance, homeostatic ILCs decrease morbidity and mortality associated with SARS-CoV-2 infection, and that lower ILC abundance contributes to increased COVID-19 severity with age and in males. Funding: This work was supported in part by the Massachusetts Consortium for Pathogen Readiness and NIH grants R37AI147868, R01AI148784, F30HD100110, 5K08HL143183.
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Objectives The global COVID-19 pandemic produced large-scale health and economic complications. Older people and those with comorbidities are particularly vulnerable to this virus, with nursing homes and long term care facilities (LTCF) experiencing significant morbidity and mortality associated with COVID-19 outbreaks. The aim of this rapid systematic review was to investigate measures implemented in LTCF to reduce transmission of COVID-19 and their effect on morbidity and mortality of residents, staff and visitors. Setting Long-term care facilities. Participants Residents, staff and visitors of facilities. Primary and secondary outcome measures Databases (PubMed, EMBASE, CINAHL, Cochrane Databases and repositories and MedRXiv prepublished database) were systematically searched from inception to 27 July 2020 to identify studies reporting assessment of interventions to reduce transmission of COVID-19 in nursing homes among residents, staff or visitors. Outcome measures include facility characteristics, morbidity data, case fatalities and transmission rates. Due to study quality and heterogeneity, no meta-analysis was conducted. Results The search yielded 1414 articles, with 38 studies included. Reported interventions include mass testing, use of personal protective equipment, symptom screening, visitor restrictions, hand hygiene and droplet/contact precautions, and resident cohorting. Prevalence rates ranged from 1.2% to 85.4% in residents and 0.6% to 62.6% in staff. Mortality rates ranged from 5.3% to 55.3% in residents. Conclusions Novel evidence in this review details the impact of facility size, availability of staff and practices of operating between multiple facilities, and for-profit status of facilities as factors contributing to the size and number of COVID-19 outbreaks. No causative relationships can be determined; however, this review provides evidence of interventions that reduce transmission of COVID-19 in LTCF. PROSPERO registration number CRD42020191569.
Article
Background: Starting in late 2019, COVID‐19, caused by the novel coronavirus SARS‐CoV‐2, spread around the world. Long‐term care facilities are at particularly high risk of outbreaks, and the burden of morbidity and mortality is very high among residents living in these facilities. Objectives: To assess the effects of non‐pharmacological measures implemented in long‐term care facilities to prevent or reduce the transmission of SARS‐CoV‐2 infection among residents, staff, and visitors. Search methods: On 22 January 2021, we searched the Cochrane COVID‐19 Study Register, WHO COVID‐19 Global literature on coronavirus disease, Web of Science, and CINAHL. We also conducted backward citation searches of existing reviews. Selection criteria: We considered experimental, quasi‐experimental, observational and modelling studies that assessed the effects of the measures implemented in long‐term care facilities to protect residents and staff against SARS‐CoV‐2 infection. Primary outcomes were infections, hospitalisations and deaths due to COVID‐19, contaminations of and outbreaks in long‐term care facilities, and adverse health effects. Data collection and analysis: Two review authors independently screened titles, abstracts and full texts. One review author performed data extractions, risk of bias assessments and quality appraisals, and at least one other author checked their accuracy. Risk of bias and quality assessments were conducted using the ROBINS‐I tool for cohort and interrupted‐time‐series studies, the Joanna Briggs Institute (JBI) checklist for case‐control studies, and a bespoke tool for modelling studies. We synthesised findings narratively, focusing on the direction of effect. One review author assessed certainty of evidence with GRADE, with the author team critically discussing the ratings. Main results: We included 11 observational studies and 11 modelling studies in the analysis. All studies were conducted in high‐income countries. Most studies compared outcomes in long‐term care facilities that implemented the measures with predicted or observed control scenarios without the measure (but often with baseline infection control measures also in place). Several modelling studies assessed additional comparator scenarios, such as comparing higher with lower rates of testing. There were serious concerns regarding risk of bias in almost all observational studies and major or critical concerns regarding the quality of many modelling studies. Most observational studies did not adequately control for confounding. Many modelling studies used inappropriate assumptions about the structure and input parameters of the models, and failed to adequately assess uncertainty. Overall, we identified five intervention domains, each including a number of specific measures. Entry regulation measures (4 observational studies; 4 modelling studies) Self‐confinement of staff with residents may reduce the number of infections, probability of facility contamination, and number of deaths. Quarantine for new admissions may reduce the number of infections. Testing of new admissions and intensified testing of residents and of staff after holidays may reduce the number of infections, but the evidence is very uncertain. The evidence is very uncertain regarding whether restricting admissions of new residents reduces the number of infections, but the measure may reduce the probability of facility contamination. Visiting restrictions may reduce the number of infections and deaths. Furthermore, it may increase the probability of facility contamination, but the evidence is very uncertain. It is very uncertain how visiting restrictions may adversely affect the mental health of residents. Contact‐regulating and transmission‐reducing measures (6 observational studies; 2 modelling studies) Barrier nursing may increase the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent cleaning and environmental hygiene measures may reduce the number of infections, but the evidence is very uncertain. It is unclear how contact reduction measures affect the probability of outbreaks. These measures may reduce the number of infections, but the evidence is very uncertain. Personal hygiene measures may reduce the probability of outbreaks, but the evidence is very uncertain. Mask and personal protective equipment usage may reduce the number of infections, the probability of outbreaks, and the number of deaths, but the evidence is very uncertain. Cohorting residents and staff may reduce the number of infections, although evidence is very uncertain. Multicomponent contact ‐regulating and transmission ‐reducing measures may reduce the probability of outbreaks, but the evidence is very uncertain. Surveillance measures (2 observational studies; 6 modelling studies) Routine testing of residents and staff independent of symptoms may reduce the number of infections. It may reduce the probability of outbreaks, but the evidence is very uncertain. Evidence from one observational study suggests that the measure may reduce, while the evidence from one modelling study suggests that it probably reduces hospitalisations. The measure may reduce the number of deaths among residents, but the evidence on deaths among staff is unclear. Symptom‐based surveillance testing may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Outbreak control measures (4 observational studies; 3 modelling studies) Separating infected and non‐infected residents or staff caring for them may reduce the number of infections. The measure may reduce the probability of outbreaks and may reduce the number of deaths, but the evidence for the latter is very uncertain. Isolation of cases may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent measures (2 observational studies; 1 modelling study) A combination of multiple infection‐control measures, including various combinations of the above categories, may reduce the number of infections and may reduce the number of deaths, but the evidence for the latter is very uncertain. Authors' conclusions: This review provides a comprehensive framework and synthesis of a range of non‐pharmacological measures implemented in long‐term care facilities. These may prevent SARS‐CoV‐2 infections and their consequences. However, the certainty of evidence is predominantly low to very low, due to the limited availability of evidence and the design and quality of available studies. Therefore, true effects may be substantially different from those reported here. Overall, more studies producing stronger evidence on the effects of non‐pharmacological measures are needed, especially in low‐ and middle‐income countries and on possible unintended consequences of these measures. Future research should explore the reasons behind the paucity of evidence to guide pandemic research priority setting in the future.
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On the 21st of February 2020 a resident of the municipality of Vo’, a small town near Padua, died of pneumonia due to SARS-CoV-2 infection¹. This was the first COVID-19 death detected in Italy since the emergence of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province². In response, the regional authorities imposed the lockdown of the whole municipality for 14 days³. We collected information on the demography, clinical presentation, hospitalization, contact network and presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo’ at two consecutive time points. On the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI) 2.1-3.3%). On the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% Confidence Interval (CI) 0.8-1.8%). Notably, 42.5% (95% CI 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (i.e. did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (p-values 0.62 and 0.74 for E and RdRp genes, respectively, Exact Wilcoxon-Mann-Whitney test). This study sheds new light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides new insights into its transmission dynamics and the efficacy of the implemented control measures.
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Transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), by asymptomatic and presymptomatic persons poses important challenges to controlling spread of the disease, particularly in congregate settings such as correctional and detention facilities (1). On March 29, 2020, a staff member in a correctional and detention facility in Louisiana developed symptoms† and later had a positive test result for SARS-CoV-2. During April 2-May 7, two additional cases were detected among staff members, and 36 cases were detected among incarcerated and detained persons at the facility; these persons were removed from dormitories and isolated, and the five dormitories that they had resided in before diagnosis were quarantined. On May 7, CDC and the Louisiana Department of Health initiated an investigation to assess the prevalence of SARS-CoV-2 infection among incarcerated and detained persons residing in quarantined dormitories. Goals of this investigation included evaluating COVID-19 symptoms in this setting and assessing the effectiveness of serial testing to identify additional persons with SARS-CoV-2 infection as part of efforts to mitigate transmission. During May 7-21, testing of 98 incarcerated and detained persons residing in the five quarantined dormitories (A-E) identified an additional 71 cases of SARS-CoV-2 infection; 32 (45%) were among persons who reported no symptoms at the time of testing, including three who were presymptomatic. Eighteen cases (25%) were identified in persons who had received negative test results during previous testing rounds. Serial testing of contacts from shared living quarters identified persons with SARS-CoV-2 infection who would not have been detected by symptom screening alone or by testing at a single time point. Prompt identification and isolation of infected persons is important to reduce further transmission in congregate settings such as correctional and detention facilities and the communities to which persons return when released.
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Background: Outbreaks of COVID-19 have been reported in nursing homes and assisted living facilities; however, the extent of asymptomatic and pre-symptomatic SARS-CoV-2 infection in this high-risk population remains unclear. Methods: We conducted an investigation of the first known outbreak of SARS-CoV-2 at a skilled nursing facility (SNF) in Illinois on March 15, 2020 and followed residents for 30 days. We tested 126/127 residents for SARS-CoV-2 via RT-PCR and performed symptom assessments. We calculated the point prevalence of SARS-CoV-2 and assessed symptom onset over 30-day follow-up to determine: 1) the proportion of cases who were symptomatic, pre-symptomatic, and asymptomatic and 2) incidence of symptoms among those who tested negative. We used the Kaplan-Meier method to determine the 30-day probability of death for cases. Results: Of 126 residents tested, 33 had confirmed SARS-CoV-2 on March 15. Nineteen (58%) had symptoms at the time of testing, 1 (3%) developed symptoms over follow-up, and 13 (39%) remained asymptomatic. Thirty-five residents who tested negative on March 15 developed symptoms over follow-up; of these, 3 were re-tested and 2 were positive. The 30-day probability of death among cases was 29%. Conclusions: SNFs are particularly vulnerable to SARS-CoV-2, and residents are at risk of severe outcomes. Attention must be paid to preventing outbreaks in these and other congregate care settings. Widespread testing and infection control are key to help prevent COVID-19 morbidity and mortality in these high-risk populations.
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The clinical features and immune responses of asymptomatic individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have not been well described. We studied 37 asymptomatic individuals in the Wanzhou District who were diagnosed with RT–PCR-confirmed SARS-CoV-2 infections but without any relevant clinical symptoms in the preceding 14 d and during hospitalization. Asymptomatic individuals were admitted to the government-designated Wanzhou People’s Hospital for centralized isolation in accordance with policy¹. The median duration of viral shedding in the asymptomatic group was 19 d (interquartile range (IQR), 15–26 d). The asymptomatic group had a significantly longer duration of viral shedding than the symptomatic group (log-rank P = 0.028). The virus-specific IgG levels in the asymptomatic group (median S/CO, 3.4; IQR, 1.6–10.7) were significantly lower (P = 0.005) relative to the symptomatic group (median S/CO, 20.5; IQR, 5.8–38.2) in the acute phase. Of asymptomatic individuals, 93.3% (28/30) and 81.1% (30/37) had reduction in IgG and neutralizing antibody levels, respectively, during the early convalescent phase, as compared to 96.8% (30/31) and 62.2% (23/37) of symptomatic patients. Forty percent of asymptomatic individuals became seronegative and 12.9% of the symptomatic group became negative for IgG in the early convalescent phase. In addition, asymptomatic individuals exhibited lower levels of 18 pro- and anti-inflammatory cytokines. These data suggest that asymptomatic individuals had a weaker immune response to SARS-CoV-2 infection. The reduction in IgG and neutralizing antibody levels in the early convalescent phase might have implications for immunity strategy and serological surveys.
Article
Background: Patients hospitalized with coronavirus disease 2019 (COVID-19) frequently require mechanical ventilation and have high mortality rates, but the impact of viral burden on these outcomes is unknown. Methods: We conducted a retrospective cohort study of patients hospitalized with COVID-19 from March 30 to April 30, 2020 at two hospitals in New York City. SARS-CoV-2 viral load was assessed using cycle threshold (Ct) values from a reverse transcription-polymerase chain reaction assay applied to nasopharyngeal swab samples. We compared patient characteristics and outcomes among patients with high, medium, and low admission viral loads and assessed whether viral load was independently associated with risk of intubation and in-hospital mortality. Results: We evaluated 678 patients with COVID-19. Higher viral load was associated with increased age, comorbidities, smoking status, and recent chemotherapy. In-hospital mortality was 35.0% with a high viral load (Ct<25; n=220), 17.6% with a medium viral load (Ct 25-30; n=216), and 6.2% with a low viral load (Ct>30; n=242; P<0.001). The risk of intubation was also higher in patients with a high viral load (29.1%), compared to those with a medium (20.8%) or low viral load (14.9%; P<0.001). High viral load was independently associated with mortality (adjusted odds ratio [aOR] 6.05; 95% confidence interval [CI]: 2.92-12.52; P<0.001) and intubation (aOR 2.73; 95% CI: 1.68-4.44; P<0.001) in multivariate models. Conclusions: Admission SARS-CoV-2 viral load among hospitalized patients with COVID-19 independently correlates with the risk of intubation and in-hospital mortality. Providing this information to clinicians could potentially be used to guide patient care.
Article
Background The ongoing COVID-19 pandemic is a global threat. Identification of markers for symptom onset and disease progression is a pressing issue. We described the clinical features of people infected on board the Diamond Princess cruise ship who were diagnosed with asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or mild or severe COVID-19, on admission to the Self-Defense Forces Central Hospital (Tokyo, Japan) and at the end of observation. Methods This retrospective, single-centre study included participants with laboratory-detected SARS-CoV-2 infection who were admitted to the Self-Defense Forces Central Hospital from Feb 11 to Feb 25, 2020. Clinical records, laboratory data, and radiological findings were analysed. Clinical outcomes were followed up until discharge or Feb 26, 2020, whichever came first. We defined asymptomatic infection as SARS-CoV-2 infection with no history of clinical signs and symptoms, severe COVID-19 as clinical symptoms of pneumonia (dyspnoea, tachypnoea, peripheral capillary oxygen saturation <93%, and need for oxygen therapy), and mild COVID-19 as all other symptoms. Clinical features on admission were compared among patients with different disease severity, including asymptomatic infection, at the end of observation. We used univariable analysis to identify factors associated with symptomatic illness among asymptomatic people infected with SARS-CoV-2 and disease progression in patients with COVID-19. Findings Among the 104 participants included in the final analysis, the median age was 68 years (IQR 47–75) and 54 (52%) were male. On admission, 43 (41%) participants were classified as asymptomatic, 41 (39%) as having mild COVID-10, and 20 (19%) as having severe COVID-19. At the end of observation, 33 (32%) participants were confirmed as being asymptomatic, 43 (41%) as having mild COVID-19, and 28 (27%) as having severe COVID-19. Serum lactate hydrogenase concentrations were significantly higher in the ten participants who were asymptomatic on admission but developed symptomatic COVID-19 compared with the 33 participants who remained asymptomatic throughout the observation period (five [50%] vs four [12%] participants; odds ratio 7·25, 95% CI 1·43–36·70; p=0·020). Compared with patients with mild disease at the end of observation, patients with severe COVID-19 were older (median age 73 years [IQR 55–77] vs 60 years [40–71]; p=0·028) and had more frequent consolidation on chest CT (13 [46%] of 28 vs nine [21%] of 43; p=0·035) and lymphopenia (16 [57%] vs ten [23%]; p=0·0055) on admission. Interpretation Older age, consolidation on chest CT images, and lymphopenia might be risk factors for disease progression of COVID-19 and contribute to improved clinical management. Funding None.
Article
Background A cruise ship is a closed-off environment that simulates the basic functioning of a city in terms of living conditions and interpersonal interactions. Thus, the Diamond Princess cruise ship, which was quarantined because of an onboard outbreak of COVID-19 in February, 2020, provides an opportunity to define the shedding pattern of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and patient antibody responses before and after the onset of symptoms. Methods We recruited adult (≥18 years) passengers from Hong Kong who had been on board the Diamond Princess cruise ship docked in Yokohama, Japan in February, 2020. All participants had been found to be negative for SARS-CoV-2 by RT-PCR 4 days before disembarking and were transferred to further quarantine in a public estate in Hong Kong, where they were recruited. Participants were prospectively screened by quantitative RT-PCR (RT-qPCR) of nasopharyngeal and throat swabs, and serum IgG and IgM against internal nucleoprotein and the surface spike receptor-binding protein (RBD) of SARS-CoV-2 at baseline (upon entering quarantine) and on days 4, 8, and 12 of quarantine. Findings On Feb 22, 2020, 215 adults were recruited, of whom nine (4%; 95% CI 2–8) were positive for SARS-CoV-2 by RT-qPCR or serology and were hospitalised. Of these nine patients, nasopharyngeal swab RT-qPCR was positive in eight patients (89%; 57–99) at baseline. All nine patients were positive for anti-RBD IgG by day 8. Eight (89%; 57–99) were simultaneously positive for nasopharyngeal swab RT-PCR and anti-RBD IgG. One patient who was positive for anti-RBD IgG and had a negative viral load had multifocal peripheral ground-glass changes on high-resolution CT that were typical of COVID-19. Five patients (56%; 27–81) with ground-glass changes on high-resolution CT were found to have higher anti-nucleoprotein-IgG OD values on day 8 and 12 and anti-RBD IgG OD value on day 12 than patients without ground-glass changes. Six (67%; 35–88) patients remained asymptomatic throughout the 14-day quarantine period. Interpretation Patients with COVID-19 can develop asymptomatic lung infection with viral shedding and those with evidence of pneumonia on imaging tend to have an increased antibody response. Positive IgG or IgM confirmed infection of COVID-19 in both symptomatic and asymptomatic patients. A combination of RT-PCR and serology should be implemented for case finding and contact tracing to facilitate early diagnosis, prompt isolation, and treatment. Funding Shaw Foundation Hong Kong; Sanming-Project of Medicine (Shenzhen); High Level-Hospital Program (Guangdong Health Commission).