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Our take —

Among 224 quarantined inmates at the Cook County Jail in Chicago, Illinois, more SARS-CoV-2 infections were identified among inmates receiving three tests during a 14-day quarantine compared to a single test at the end of the 14-day quarantine period (17 cases vs. 2 cases). Most new cases were identified within 1-3 days after the start of quarantine, and many were asymptomatic. SARS-CoV-2 attack rates were likely underestimated by high testing refusal rates, diagnostic test characteristics, and potential COVID-19 recoveries in the study population. These results highlight the importance of early and frequent testing among close contacts of cases in congregate settings.

Study design

Prospective Cohort

Study population and setting

Between May 1 and 19, 2020, incarcerated individuals in the Cook County Jail (Chicago, Illinois, United States) were placed into a 14-day quarantine after at least 1 laboratory-confirmed SARS-CoV-2 infection was identified in a housing unit. Two testing strategies were used to detect SARS-CoV-2 transmission in quarantined housing units following potential exposure: 1) single-time testing (at day 14 of the quarantine period) or 2) serial testing (3 time points during the quarantine period: day 1, days 3-5, days 13-14). All individuals in a quarantined housing unit received the same type of testing strategy. The investigators compared COVID-19 positivity (confirmed by RT-PCR) between housing units receiving single-time and serial testing, respectively.

Summary of Main Findings

Among 137 individuals in the serial testing cohort, 17 of the 96 individuals who received at least one test (18%) had a positive test result for SARS-CoV-2: 16 on day 1, and 1 on days 3-5. Of the 87 individuals in the single-time period cohort, only 2 out of 76 individuals who were tested on the final day of quarantine (day 14), had a positive test result. Among newly identified COVID-19 cases (N = 19), 63% were pre-symptomatic or asymptomatic at the time of diagnosis.

Study Strengths

Investigators compared COVID-19 detection rates in incarcerated populations receiving two distinct testing strategies using individual-level data, which is infrequently available in epidemiologic studies of COVID-19 transmission.

Limitations

Assignment of the testing protocol to housing units was not random, and there may have been differences in underlying transmission risks between the two groups that were unmeasured or unaccounted for. The estimated attack rate was substantially lower (11%) than what has been observed in other enclosed/incarcerated settings. Possible explanations for this include the high testing refusal rates (13%) in the source population and possible previous SARS-CoV-2 infections that were not ascertained (either by self-report or through antibody testing). Given that one-fourth of quarantined inmates with negative SARS-CoV-2 test results reported illness symptoms consistent with COVID-19 (e.g., loss of taste smell, headache), COVID-19 diagnoses may have been misclassified due to suboptimal RT-PCR test sensitivity.

Value added

This is the first study to compare effectiveness of differenting COVID-19 testing strategies in an incarceration setting, where COVID-19 transmission remains unacceptably high.

Our take —

This study sought to examine the by-county changes in COVID-19 cases associated with the BLM protests that occurred in the US from May 25 to June 16, 2020. They found that the crude measure of whether a protest occurred in the county was associated with modest increases in case-rates (1.6 per 10,000 people by week 3 after the protests), but there was no significant by-protestor increase in case-rate in the 3 week period following the protest. The researchers were unable to control for potential confounders or county-level differences between protest and control counties, however, such as mask mandates. Therefore, it is not clear if this association is due to the protests themselves, or related to by-county differences that are also associated with having a protest (e.g., confounding which could underestimate or overestimate the total effects). These results should be interpreted with caution given these notable limitations.

Study design

Prospective Cohort

Study population and setting

The study set out to examine whether there was an increase in COVID-19 incidence following Black Lives Matter (BLM) protests in the US. The study crosswalked two publicly available data aggregator sites: one for the BLM protests that included the estimated number of protestors and their location, and one for COVID-19 statistics at the county level. BLM protests from May 25 (Memorial Day holiday in the US) through June 16, 2020 were included, and a control county in which no protest occurred within the same state was selected, selected based off of county population size and COVID-19 case numbers on the day of the protest. Multiple protests in a county were merged as a sum of protestors, and the differences in case rates were analyzed in the 3 weeks following the protest date, with separate analysis for week 1, 2, and 3 conducted. Additional cases estimated by each protestor was determined using quantile regression.

Summary of Main Findings

The study found that 326 counties had 868 demonstrations and an estimated 757,077 protestors in attendance over the 22-day study period. The median initial case rate in protest counties was 3.1 per 100 people, as compared to 2.9 per 100 people in control counties. While there was a statistically significant increase in the case rate where protests occurred, this was estimated as a median of 1.4 per 100,000 people in week 1, 5.4 per 100,000 cases in week 2, and 16 per 100,000 people in week 3. Using quantile regression to assess increases by protestors rather than by area with a protest, they found that each protestor added less than 1 case per 100,000,000 people in week 1, 2, or 3, which was not significant.

Study Strengths

The study used publicly available data which aggregated across all counties. They calculated not only the by-protest rate of case increases, but the by-protestor rate as well.

Limitations

By using county-level aggregation and the BLM protest aggregation site, there may be issues with misclassification, and the number of protestors may be misestimated. This is particularly challenging for determining the per-protestor change in case-rate. Additionally, the study could not control for other temporal trends that differed by county, and assumes that the change in case-rate were related to the BLM protests, which may not be the case. The study also summed multiple protests occurring in the same county together, while they may have implemented different social distancing and infection control protocols, or draw different populations. Finally, they attempted to define controls based on population size and baseline case-rate, but were unable to control for confounders that may bias estimates, such as differences in county-level mask mandates or lockdown requirements.

Value added

This study is an updated analysis following the BLM protests over the summer to determine if there were changes in COVID-19 incidence in counties.

Our take —

In this study of 154 patients with asymptomatic or severe COVID-19 in India, individuals with severe COVID-19 (defined as ICU admission) had lower vitamin D levels and were more likely to have vitamin D deficiency. Those with vitamin D deficiency were more likely to die than those without deficiency. However, caution is warranted as these findings do not provide evidence that vitamin D supplementation can prevent severe COVID-19, results were not adjusted for any potential confounders (including age), and vitamin D levels were measured after illness onset. Further research regarding this relationship is warranted.

Study design

Case-Control, Prospective Cohort

Study population and setting

This observational study at the M.L.B. Medical College in Jhansi, India was designed to evaluate the association between vitamin D and COVID-19 disease severity. Individuals aged 30-60 years with PCR-confirmed COVID-19 starting were consecutively enrolled for 6 weeks starting on June 5, 2020. Patients were included if they were asymptomatic at admission and remained so until their discharge (Day 12) or if they required ICU admission due to severe COVID-19 disease, defined as clinical pneumonia with respiratory rate >30 or oxygen levels <90%, signs of multi-organ impairment, or laboratory evidence of coagulation abnormalities. Vitamin D levels were estimated from Serum 25 (OH)D, and levels <20 ng/mL were considered as vitamin D deficiency. Participants were followed until death or discharge alive from the hospital.

Summary of Main Findings

In total, 154 patients were included: 91 were asymptomatic (53% male; mean age: 42 years) and 63 had severe COVID-19 (67% male, mean age: 51 years). The mean concentration of 25 (OH)D was higher among asymptomatic cases (mean: 27.9 ng/mL, 32% with vitamin D deficiency), compared to those with severe disease (mean: 14.4 ng/mL, 97% with vitamin D deficiency). Serum inflammatory markers, such as IL-6, ferritin, and TNF-alpha, were also associated with vitamin D deficiency. Regardless of symptom severity at enrollment, those with vitamin D deficiency were more likely to die (19/90 [21%] vs. 2/64 [3%]).

Study Strengths

This was a prospective cohort study with completed follow-up of included participants.

Limitations

No adjusted analyses were presented, and confounding is likely due to differences in age and comorbidities between asymptomatic and severely ill COVID-19 patients (older age is a known risk factor for vitamin D deficiency). The timing of vitamin D measurement relative to SARS-CoV-2 infection is not reported. Temporality between vitamin D levels and COVID-19 severity cannot be established; it is unknown whether vitamin D deficiency is a cause or consequence of severe disease. The sample size was relatively small and did not include patients with moderate disease (symptoms but not requiring ICU admission), which limits generalizability and inhibits inferences about any potential dose-response relationship between vitamin D levels and COVID-19 severity.

Value added

This is one of the first studies examining the relationship between Vitamin D and COVID-19 disease severity.

Our take —

This study among mildly, pre-symptomatic, or asymptomatically infected individuals provides further evidence that antibody responses to SARS-Cov-2 infection frequently wane or never reach detectable levels within a short period following active SARS-CoV-2 infection within a substantial number of individuals. While purportedly focused on asymptomatic infections, both pre-symptomatic and mildly symptomatic individuals were included within this study. As the host immune response may contribute to differences between asymptomatic, mildly symptomatic, and severe SARS-CoV-2 infection, it remains unclear whether the estimates obtained by this study accurately reflect SARS-CoV-2-specific serology within asymptomatic individuals.

Study design

Prospective Cohort

Study population and setting

This serology-focused study examined a subset of individuals from the University of Milan personnel-based UNICORN study in Lombardy, Italy. This analysis focused on 31 out of 197 asymptomatic individuals initially enrolled in UNICORN with evidence of prior or current SARS-CoV-2 infection on the day of enrollment. These individuals purportedly had no symptoms 14 days prior to and including the day of recruitment, which involved both a nasal swab and serological test for SARS-CoV-2. A second plasma sample and a questionnaire was obtained 8 weeks after the baseline sample to assess the proportion of individuals who developed or retained immunoglobulins (Ig) or antibodies specific to the spike-receptor binding domain (RBD) of SARS-CoV-2.

Summary of Main Findings

Of 197 asymptomatic individuals screened, 31 individuals were initially positive for SARS-CoV-2 infection via nasal swab or serology and of these 29 underwent a secondary serological assessment eight weeks after the initial testing. Among 21 individuals with an active asymptomatic infection at baseline (positive nasal swab), 52.3% (11/21) never had detectable SARS-CoV-2-specific antibodies while 80% of these baseline PCR positive individuals had no evidence of circulating Ig against SARS-Cov-2 eight weeks later. Roughly two-thirds of individuals with a positive baseline serology test (11/17) did not have SARS-CoV-2-specific IgG eight weeks later. Overall, the majority of individuals with evidence of asymptomatic SARS-CoV-2 infection did not have antibodies against the RBD-spike protein when serologically assessed eight weeks later.

Study Strengths

This study utilized data from the previously well described UNICORN cohort study during the initial surge of the SARS-CoV-2 pandemic in Milan, Italy. The serological tests used by the investigators were appropriate, and the study benefits from Ig-specific tests screening for multiple types of antibodies (IgM and IgG).

Limitations

This study purports to explore the serological dynamics of asymptomatic SARS-CoV-2 infected individuals, however it remains unclear whether the individuals included within this study were truly asymptomatic. Many of the included individuals reported experiencing fevers (37.9%), episodes of upper/lower airway infections, and are characterized within the discussion as ‘subjects with a mild SARS-CoV-2 infection’. The distinction between asymptomatic, pre-symptomatic (1 individual reported a fever and upper airway infection days after their initial positive SARS-CoV-2 nasal swab), and mildly symptomatic infection is of critical importance, and coupled with the small sample size, suggests that these findings may not accurately reflect the serological dynamics of asymptomatic SARS-CoV-2 infection.

Value added

This study supports similar findings related to the transient nature of host antibody responses to SARS-CoV-2, suggesting that among asymptomatic or mildly symptomatic individuals, a significant proportion of infections may result in rapidly waning antibody responses or fail to induce detectable antibody responses within eight weeks.

Our take —

This study, which has not been peer reviewed, measures components of immune memory at various time points from COVID-19 symptom onset. Despite a small number of repeated measurements, they provide preliminary data that SARS-CoV-2 infection — assuming that it behaves similarly to other pathogens that induce analogous immune responses — can induce sustained immunity to either prevent or lessen the severity of subsequent infection.

Study design

Cross-sectional; Prospective Cohort

Study population and setting

This preprint used cross-sectional data from 185 individuals and longitudinal data from 38 of those individuals to measure SARS-CoV-2-induced antibodies, memory B cells, memory CD8+ T cells, and memory CD4+ T cells from six to 240 days after COVID-19 symptom onset (median 90.5 days). Participants had a documented history of SARS-CoV-2 infection via PCR, serodiagnostics, or both, and lived mostly in California or New York in the United States. Only 7% of participants were hospitalized with COVID-19, 2% of patients were asymptomatic, their ages ranged from 19-81, and participants were more likely to be female than male (57% vs 43%). The authors measured circulating antibody (IgG and IgA), memory B-cells (including surface immunoglobulin isotype), CD8+ T cells, and CD4+ T cells specific to various components of the SARS-CoV-2 virus via assays that included negative and positive controls for each immune component of interest. They created lines of best fit via linear regression or kinetic models for both cross-sectional and longitudinal data and assessed differential immune memory by gender with ANCOVA to assess trends in immune memory after SARS-CoV-2 infection.

Summary of Main Findings

They found that IgG antibody titers tended to decrease slightly from symptom onset with 90% being seropositive at 6-8 months vs 98% at 1 month, regardless of antigen specificity type, although there was high variability in antibody titers across individuals. Memory B cells increased steadily the further individuals got from COVID-19 symptom onset regardless of the antigen type, plateauing around 150 days, with immunoglobulin isotype switching to predominantly IgG+ memory B cells as time progressed. Memory CD8+ and CD4+ T cells, on the other hand, were detectable in a higher percentage of people at earlier measurements (61% and 94% at one month respectively), after which they decreased marginally to 50% and 89% respectively among the 18 participants with data available after six months from COVID-19 symptom onset. These trends seem to hold in individuals who provided data at more than one time point across all immune components. They further homed in on CD4+ T follicular helper cells — cells essential for activating memory B cells that then produce antibodies in response to an antigen — which were stable among tested individuals for at least six months from symptom onset. Finally, they found no significant differences in immune memory by gender or hospitalization status.

Study Strengths

This study provides an in-depth assessment of both circulating antibodies and memory B- and T-cells following symptom onset of natural COVID-19 infection. A subset of their participants provided data at more than one time point, which allowed for comparisons across time in individuals.

Limitations

It appears that in calculating lines of best fit for various antibodies and immune cells, the authors combined data from participants who only provided data at one time point with those who provided data at more than one time point. This introduces data points that are not independent from one another, which biases the lines of best fit in unforeseen ways. Furthermore, it is difficult to compare antibody or immune cell titers across time in different individuals, since, as the authors note, each person’s immune system reacts to pathogens (such as SARS-CoV-2) differently. The small sample size used also makes it difficult to generalize these data to all individuals infected with SARS-CoV-2. Lastly, it is also unknown how immunological responses to SARS-CoV-2 will differ between those who are naturally infected and individuals who are vaccinated.

Value added

The authors provide evidence, through a rather thorough examination of various immune cell subtypes and responses, for persistent SARS-CoV-2 immunity or reduced disease severity following SARS-CoV-2 infection.

Our take —

This study of 9,157,814 people in England from February 1 to August 3, 2020, available as a preprint and thus not yet peer reviewed, sought to identify the risk of COVID-19 among adults due to living with children of different age groups. They found adults <65 years old had a reduced hazard of death (26% reduction) if they lived with children 0 to 11 years old than if they did not. They also found that living with children 12 to 18 years old increased the risk of infection by 8%. There was no significant effect of living with children for adults >65 years old on any outcomes. This was a national study from England, which has important policy implications for lockdown restrictions, including potential school closures. Caution in the interpretation is warranted as unaccounted for behavioral differences within families with small children could have affected their risk of infection, and authors did not account for timing of school closures.

Study design

Prospective Cohort

Study population and setting

The study objective was to examine the risk of infection among adults associated with living with children of different age groups, both during and following school lockdown orders in England. The study included 9,157,814 adults between >=18 and <=65 years of age, and an additional cohort of adults >65 years old (N=2,567,671), and assessed the impact on SARS-CoV-2 infection from primary care records in The Phoenix Partnership, COVID-19 hospital admission using data from the Secondary Uses Service, COVID-19 ICU admission from the Intensive Care National Audit & Research Center, and death due to COVID-19 noted in the Office for National Statistics mortality records. This was done on the OpenSAFELY data analytics platform created for the National Health Service (NHS) of England. The study population required individuals to have >=3 months of active follow-up via general practices using the Phoenix Partnerhsip software from February 1, 2020 onwards. Hospital admission data were available until May 1, 2020, while infection outcomes were available through August 3. 2020. Children were linked to households via a household identifier and enumerated based on age, and exposure categories for adults in the study were grouped as: (1) no children under 18 in the house; (2) any child 0 to 11 years of age; (3) no children 0 to 11 years of age but one or more children 12 to 18 years old.

Summary of Main Findings

The study found that living with children 0 to 11 years of age was not associated with an increase in SARS-CoV-2 infection, COVID-19 hospital admission, or ICU admission among adults <65 years old. It did significantly reduce the hazard of death from COVID-19 by 26% (95% CI: 0.60 – 0.90). Living with children 12 to 18 years of age increased the risk of infection by 8% (95% CI: 1.03 – 1.13) but was not associated with any other outcomes. For adults <65 years of age, living with children age 0 to 11 years reduced the risk of death from non-COVID-19 causes by 32% (95% CI: 0.62 – 0.74), and by 27% (95% CI: 0.66 – 0.81) for those living with children 12 to 18 years. For adults >65 years of age, there were no associations with any of the outcomes, including infection, hospital admission, ICU admission, COVID-19-specific death, or non-COVID-19-death.

Study Strengths

The study was able to use a large sample size based on attendance of a TPP general practice during the study period, which means they likely had power to identify infections and outcomes when they occurred. By linking to a number of registries, it had follow-up for the majority of this large population. They also controlled for many potential confounders, including age, sex, body mass index, smoking status, deprivation index, ethnicity, geographic area, and the total number of individuals in a household. They also controlled for chronic comorbidities associated with severe COVID-19 outcomes, further reducing the potential for confounding in these estimates.

Limitations

The largest limitation was data availability—for instance, for hospital admission they only had from February 1 to May 1, 2020, while for the other outcomes, they had longer follow-up until August. Given hospital admission data is focused in the beginning of the pandemic during the first wave, it may not be reflective of more recent trends. Occupation was also unmeasured, which could reflect whether individuals stayed in the house or were essential workers that had to continue contact. There also may be differences between risk behaviors among parents of children compared to those without that may impact their risk of exposure, thereby impacting the risk ratios with unmeasured confounding. Similarly, the study could not adjust for temporality differences such as school closures or other lockdown restrictions imposed, likely due to variability across schools. Finally, their measure of children was based off simply the number of children linked in the record, and may not actually reflect contact due to children residing with a different parent or with other family members.

Value added

This is the largest and most comprehensive study from a nation-wide cohort that examines the change in risk of COVID-19 in adults due to contact with children.

Our take —

Researchers enrolled 333 persons in the United States with fitness tracking devices (e.g., FitBits) who had COVID-like symptoms and received a SARS-CoV-2 test. Variations in sleep and activity, alongside self-reported symptoms can suggest a possible positive COVID-19 case and may make individuals more aware of possible infective status prior to confirmed testing. However, it is also possible that participants changed their lifestyles as a result of learning that they had been exposed to a person with COVID-19 or that they were positive. The methods in this paper do not explicitly account for either of these possibilities and therefore, should be interpreted with caution.

Study design

Prospective Cohort, Retrospective Cohort

Study population and setting

This analysis was restricted to 333 people with fitness trackers (Fitbit, Apple HealthKit, Google Fit) in the United States who reported COVID-like symptoms, sought testing for SARS-CoV-2, and were enrolled in the study between March 25 and June 7, 2020. The authors evaluated whether the addition of sensor data (i.e., changes in resting heart rate, sleep, and physical activities) to symptoms could improve detection of people who reported a positive test result for SARS-CoV-2. Authors evaluated the ability of each model to correctly classify people with and without the disease using receiver operating curves (ROC) and the area under the curve (AUC). AUC is a common metric for simultaneously considering the sensitivity and specificity of tests that attempt to diagnosis a disease.

Summary of Main Findings

Fifty-four (16.2%) participants reported being positive for SARS-CoV-2. Symptomatic people who reported receiving a positive test result were significantly more likely to get more sleep and take fewer steps daily than those who reported a negative result. Combining both sensor and symptom data resulted in an AUC of 0.80, which was significantly better than self-reported symptom or sensor data alone. An AUC of 0.8 suggests an 80% chance that the test will correctly distinguish an infected from a non-infected patient

Study Strengths

This study drew participants from across the United States and gathered data from objective, real-time fitness trackers worn by participants.

Limitations

The most pressing limitation of this study is whether participants changed their sleeping and activity as a result of receiving their diagnosis. If participants altered their habits after their diagnosis, then the proposed diagnostic method (i.e., detecting changes in sensor data) would not be useful in predicting infection. The authors acknowledge that participation in this study was limited to persons with fitness trackers, who are likely not representative of the general population.

Value added

People diagnosed with COVID-19 may have COVID-specific symptoms and change their sleeping and physical activity habits, as measured by a fitness tracker. Fitness trackers and self-reporting may offer new ways to potentially identify COVID-19 positive individuals before RT-PCR testing is done.

Our take —

The study examined publicly available data to describe changes in the incidence of SARS-CoV-2 infection in ICE facilities from April to August 2020. It found an increase in the rate of infections over time, increasing to 6,683 people infected per 100,000 detained individuals in August, and 91 out of 135 (67%) facilities reporting an outbreak. The ICE-detained population was reduced by 45% from the pre-pandemic period in February to August. In general, testing rates increased over time and testing positivity decreased, but positivity rates remained high with 18% of individuals receiving tests testing positive in August. This suggests that, despite population reduction and fluctuation in the rate of testing, case numbers increased among people detained by ICE over time and overall were substantially higher than those observed in the US population overall.

Study design

Prospective Cohort, Ecological

Study population and setting

The study objective was to quantify the changes in incidence of SARS-CoV-2 infections among people in Immigration and Customs Enforcement (ICE) custody in the United States from April 1 to August 31, 2020. The study used publicly available data and calculated new COVID-19 cases each month by subtracting cumulative counts at the end of each month from the month prior. Mean daily detained populations were assessed per month from the ICE Statistics Fiscal-Year 2020 dataset, and compared to that of the population in February, before the pandemic. Notably, testing patterns varied between facilities and over time: some facilities piloted universal testing, others only tested symptomatic presumed cases and all of their contacts, while others tested only symptomatic contacts of cases.

Summary of Main Findings

The study found that the mean detained population decreased by 45% from 39,319 in February to 21,591 in August. Cases were reported from 91 of 135 facilities (67.4%), though case distribution was unequal and 20 facilities had 71% of cases reported. The monthly cumulative incidence increased from April (1527 per 100,000 people) to August (6683 per 100,000 people), while the monthly testing rate fluctuated. The test positivity rate was 47% in April, decreased to 11% in July, then increased against to 18% in August. Overall, the number of cases among detained people increased each month from April to August. The monthly case rate was consistently higher among persons in ICE custody than in the US population at large, ranging from 5.7 times higher in April to 21.8 times higher in ICE facilities in June, and falling to 15.1 times higher in August 2020.

Study Strengths

The study utilized publicly available data to examine the changes not only in case counts but in the testing rate and test positivity rates. This helped to explore whether the change in cases could be due to changes in testing rates, rather than the change in true incidence.

Limitations

The study accuracy is subject to reporting, which is often unreliable in detention settings. For instance, reporting delays are common, and presumed positive clinical cases who do not receive testing are missing. Asymptomatic testing is not conducting, and ICE does not have a unified surveillance method in place to ensure standardized reporting across facilities. Over the summer, a handful of ICE facilities piloted universal testing, though this was in the extreme minority. Additionally, given that ICE continued to detain new individuals and transfer detained people between facilities, the case rate that they saw may stem from infections in community being brought to the facility. Additionally, the study did not examine the number of staff members who have been tested, and therefore it does not reflect the number of infections in ICE facilities overall, but only among those detained.

Value added

The study is one of the first to utilize publicly available data to examine the changes in infection rates at ICE facilities.

Our take —

The degree of immunity acquired by individuals after SARS-CoV-2 infection, and how it changes over time, is a matter of considerable public health concern. In contrast to a previous study showing rapidly waning levels of antibodies that target a different SARS-CoV-2 antigen among patients with mild symptoms, this study from New York City shows that neutralizing antibodies to SARS-CoV-2 spike protein diminished over time but remained at high levels up to 3 months after symptom onset in a group of patients with mostly mild illness. The patient population was small and not well described, so it is unclear if these results apply to the broader population of those infected with SARS-CoV-2. Important questions remain about how varying levels of SARS-CoV-2 antibody translate to protection from infection.

Study design

Case series; prospective cohort; other

Study population and setting

This paper reports on three related studies: 1) Enzyme linked immunoassay (ELISA) testing for IgG antibodies to the SARS-CoV-2 spike protein among 72,401 individuals with laboratory-confirmed or suspected infection approximately 30 days after symptom onset from the Mount Sinai Health System in New York City, from March to October 6, 2020; 2) testing of 120 samples that had a known ELISA titer for neutralization of SARS-CoV-2 using a quantitative neutralization assay; and 3) longitudinal screening of 121 patients for IgG antibodies to the SARS-CoV-2 spike protein at two additional time points (approximately 82 and 148 days after symptom onset) after the initial screening (approximately 30 days after symptom onset).

Summary of Main Findings

Less than 5% of all individuals screened required hospitalization or emergency room evaluation. Of those screened, 30,082 (42%) tested positive for detectable antibodies to the SARS-CoV-2 spike protein at a titer of 1:80 or higher. Most of those testing positive had moderate-to-high titers (defined as 1:320 or higher): 2.3% had a titer of 1:80, 4.8% of 1:160, 22.5% of 1:320, 31.8% of 1:960, and 38.6% of 1:2880. Neutralizing titers significantly correlated with ELISA titers (Spearman’s r=0.87). Half of sera with spike-binding titers between 1:80 and 1:160 had neutralizing activity, while 90% of sera with 1:320 titers and all those with 1:960 titers or above had neutralizing activity. Among the 121 individuals sampled over a total of three time points (at an average of 30, 82, and 148 days post-symptom onset), the geometric mean titers (GMT) declined from 764 to 690 to 404. Among those with a 1:320 titer or lower, antibody titers increased on average at the second time point, followed by a decrease at the third time point. Three individuals with initially low titers (1:80) lost reactivity, one at the second time point and two at the third. The correlation between neutralizing and ELISA titers remained high at the third time point (r=0.79).

Study Strengths

This study examined antibodies to the spike protein of SARS-CoV-2, which are likely to be more relevant to immunity than antibodies to nucleoprotein. Antibody titers were measured in a large number of individuals using an assay with high accuracy in a validation panel. Longitudinal analyses of antibody titers considered two time points after the initial assay, allowing for discernment of nonlinearities in trajectory.

Limitations

Patient demographic and clinical characteristics were not reported, which makes it difficult to interpret how these data apply to any particular group of individuals. Of particular concern is the lack of data on COVID-19 clinical severity in either the larger study population or in the two substudies. Thus, while it appears that these studies were conducted on primarily mild cases of COVID-19, this study did not assess any relationship between disease severity and antibody response. Also, the timing of initial antibody screening relative to symptom onset or date of potential exposure was not characterized in the larger study population and there was variation in the timing of antibody assays in the longitudinal study. Not all individuals screened for antibodies were tested for SARS-CoV-2 infection, which means an unknown number of people who had been infected with SARS-CoV-2 may have tested negative for antibodies. The size of the population in the longitudinal study (n=121) and the lack of reported demographic or clinical characteristics makes it difficult to generalize the results.

Value added

This study provides some of the strongest evidence to date regarding the persistence of neutralizing antibodies to SARS-CoV-2 over time, particularly among those without severe disease. This has implications for both pandemic planning and vaccine development.

Our take —

This prospective longitudinal study among National Basketball Association players, coaches, staff and vendors, available as a preprint and thus not yet peer reviewed, provides the first set of data on the RT-qPCR dynamics of early SARS-CoV-2 infection. Peak viral load appears to occur early during the response and symptomatic individuals appear to shed SARS-CoV-2 for a longer period of time. A second test within 2 days of a positive test may predict if an individual’s viral load is increasing or decreasing which can inform clinical care.

Study design

Prospective Cohort

Study population and setting

Study subjects included the National Basketball Association (NBA) players, staff, and vendors tested for SARS-CoV-2 using real-time quantitative PCR (RT-qPCR) from June 23 to July 9, 2020. Data presented in this article was collected in the teams’ local cities prior to relocation of the study cohort to isolation in Orlando, Florida. Multiple samples (n>5) were taken from each individual and data from individuals that tested positive for SARS-CoV-2 one or more times was analyzed.

Summary of Main Findings

In total, 2,411 serial RT-qPCR threshold cycle (Ct) values from 68 individuals with SARS-CoV-2 infections were analyzed, with a median number of 41 test results per person. Ct values were used to estimate viral load, with a lower Ct value corresponding to a higher viral load. Of the 68 individuals with positive results, 46/68 had active infections and only 13/46 of these individuals reported symptoms. Results demonstrated no significant difference in peak Ct values between symptomatic and asymptomatic individuals [mean Ct 22.2 (95% CI: 19.1, 25.1) vs 22.4 (95% CI: 20.2, 24.5), respectively) implying that there was no difference in viral load between those with and without symptoms. Individuals with symptoms took longer to clear the virus [mean duration 10.5 days (95% CI: 6.5, 14.0)] compared to asymptomatic individuals [mean duration 6.7 days (95% CI: 3.2, 9.2)]. Regardless of symptoms, the mean duration of acute shedding for the 46 individuals with active infection was 10.1 days (95% CI: 6.5, 12.6). The remaining 22/68 individuals were presumed to be infected prior to study implementation and were still clearing virus but did not have a new infection. Probability estimation showed that while Ct values alone could predict if a person was in the acute or persistent viral shedding stage, a positive test followed by a second test with a higher viral RNA concentration within a 48-hour window was strongly associated with an active infection in the proliferation phase.

Study Strengths

The longitudinal design of the study and multiple testing outcomes for each individual provided insight into the kinetics of early SARS-CoV-2 infection using Ct values rather than a binary positive or negative result. Additionally, the data presented pre-dated the “bubble” phase of testing and simulated real-world exposure to the virus.

Limitations

Statistical modeling was used to infer a time frame from onset of infection to peak infection and a time frame from peak infection to the conclusion of viral shedding which may or may not correlate with actual clinical time frames. The study findings may have limited applicability to the general population as the study cohort was composed of predominantly male professional athletes with unrestricted access to health care. No association between Ct values and viral copy number was reported, which limits the applicability of these findings for diagnostic assessments.

Value added

This is the first study to use serial PCR to infer if a person is in the proliferation or recovery (clearance) stage of viral infection, and provides strong evidence that a follow-up test within two days of the initial positive test can determine if an individual’s viral load is increasing or decreasing. Repeated testing can identify individuals carrying SARS-CoV-2 if they are infectious or not and in turn, help to reduce the number of overall infections in a community setting.