Laboratory-Based SARS-CoV-2 Receptor Binding Domain Serologic Assays Perform with Equivalent Sensitivity and Specificity to Commercial FDA-EUA Approved Tests.

During early phases of the SARS-CoV-2 epidemic, many research laboratories repurposed their efforts towards developing diagnostic testing that could aid public health surveillance while commercial and public diagnostic laboratories developed capacity and validated large scale testing methods. Simultaneously, the rush to produce point-of-care and diagnostic facility testing resulted in FDA Emergency Use Authorization with scarce and poorly validated clinical samples. Here, we review serologic test results from 186 serum samples collected in early phases of the pandemic (May 2020) from skilled nursing facilities tested with six laboratory-based and two commercially available assays. Serum neutralization titers were used to set cut-off values using positive to negative ratio (P/N) analysis to account for batch effects. We found that laboratory-based receptor binding domain (RBD) binding assays had equivalent or superior sensitivity and specificity compared to commercially available tests. We also determined seroconversion rate and compared with qPCR outcomes. Our work suggests that research laboratory assays can contribute reliable surveillance information and should be considered important adjuncts to commercial laboratory testing facilities during early phases of disease outbreaks.

A multiplexed, paired-pooled droplet digital PCR assay for detection of SARS-CoV-2 in saliva.

In response to the SARS-CoV-2 pandemic, we developed a multiplexed, paired-pool droplet digital PCR (MP4) screening assay. Key features of our assay are the use of minimally processed saliva, 8-sample paired pools, and reverse-transcription droplet digital PCR (RT-ddPCR) targeting the SARS-CoV-2 nucleocapsid gene. The limit of detection was determined to be 2 and 12 copies per µl for individual and pooled samples, respectively. Using the MP4 assay, we routinely processed over 1,000 samples a day with a 24-h turnaround time and over the course of 17 months, screened over 250,000 saliva samples. Modeling studies showed that the efficiency of 8-sample pools was reduced with increased viral prevalence and that this could be mitigated by using 4-sample pools. We also present a strategy for, and modeling data supporting, the creation of a third paired pool as an additional strategy to employ under high viral prevalence.

Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities.

The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide as of March 2021. As COVID-19 case rates declined and state and county restrictions were lifted in spring 2021, facility managers, local and state health agencies were challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern and variable vaccine uptake across facilities highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. This dashboard quantifies the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Key findings were that choice of COVID-19 diagnostic (ex. nasal swab qRT-PCR vs rapid antigen) and testing cadence has less impact on attack rate and staff workdays missed than does vaccination rates among staff and residents. Specifically, low vaccine uptake among staff at long-term care facilities puts staff and residents at risk of ongoing COVID-19 outbreaks. Here we present our model and dashboard as an exemplar of a tool for state public health officials and facility directors to gain insights from an infectious disease model that can directly inform policy decisions in the midst of a pandemic.

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications.

INTRODUCTION: The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. METHODS: We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. RESULTS: In most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups.Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. CONCLUSION: The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.

Ethnoracial Disparities in SARS-CoV-2 Seroprevalence in a Large Cohort of Individuals in Central North Carolina from April to December 2020.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths around the world within the past 2 years. Transmission within the United States has been heterogeneously distributed by geography and social factors with little data from North Carolina. Here, we describe results from a weekly cross-sectional study of 12,471 unique hospital remnant samples from 19 April to 26 December 2020 collected by four clinical sites within the University of North Carolina Health system, with a majority of samples from urban, outpatient populations in central North Carolina. We employed a Bayesian inference model to calculate SARS-CoV-2 spike protein immunoglobulin prevalence estimates and conditional odds ratios for seropositivity. Furthermore, we analyzed a subset of these seropositive samples for neutralizing antibodies. We observed an increase in seroprevalence from 2.9 (95% confidence interval [CI], 1.8 to 4.5) to 12.8 (95% CI, 10.6 to 15.2) over the course of the study. Latinx individuals had the highest odds ratio of SARS-CoV-2 exposure at 6.56 (95% CI, 4.66 to 9.44). Our findings aid in quantifying the degree of asymmetric SARS-CoV-2 exposure by ethnoracial grouping. We also find that 49% of a subset of seropositive individuals had detectable neutralizing antibodies, which was skewed toward those with recent respiratory infection symptoms. IMPORTANCE PCR-confirmed SARS-CoV-2 cases underestimate true prevalence. Few robust community-level SARS-CoV-2 ethnoracial and overall prevalence estimates have been published for North Carolina in 2020. Mortality has been concentrated among ethnoracial minorities and may result from a high likelihood of SARS-CoV-2 exposure, which we observe was particularly high among Latinx individuals in North Carolina. Additionally, neutralizing antibody titers are a known correlate of protection. Our observation that development of SARS-CoV-2 neutralizing antibodies may be inconsistent and dependent on severity of symptoms makes vaccination a high priority despite prior exposure.

Durable Antibody Responses in Staff at Two Long-Term Care Facilities, during and Post SARS-CoV-2 Outbreaks.

SARS-CoV-2 has had a disproportionate impact on nonhospital health care settings, such as long-term-care facilities (LTCFs). The communal nature of these facilities, paired with the high-risk profile of residents, has resulted in thousands of infections and deaths and a high case fatality rate. To detect presymptomatic infections and identify infected workers, we performed weekly surveillance testing of staff at two LTCFs, which revealed a large outbreak at one of the sites. We collected serum from staff members throughout the study and evaluated it for binding and neutralization to measure seroprevalence, seroconversion, and type and functionality of antibodies. At the site with very few incident infections, we detected that over 40% of the staff had preexisting SARS-CoV-2 neutralizing antibodies, suggesting prior exposure. At the outbreak site, we saw rapid seroconversion following infection. Neutralizing antibody levels were stable for many weeks following infection, suggesting a durable, long-lived response. Receptor-binding domain antibodies and neutralizing antibodies were strongly correlated. The site with high seroprevalence among staff had two unique introductions of SARS-CoV-2 into the facility through seronegative infected staff during the period of study, but these did not result in workplace spread or outbreaks. Together, our results suggest that a high seroprevalence rate among staff can contribute to immunity within a workplace and protect against subsequent infection and spread within a facility. IMPORTANCE Long-term care facilities (LTCFs) have been disproportionately impacted by COVID-19 due to their communal nature and high-risk profile of residents. LTCF staff have the ability to introduce SARS-CoV-2 into the facility, where it can spread, causing outbreaks. We tested staff weekly at two LTCFs and collected blood throughout the study to measure SARS-CoV-2 antibodies. One site had a large outbreak and infected individuals rapidly generated antibodies after infection. At the other site, almost half the staff already had antibodies, suggesting prior infection. The majority of these antibodies bind to the receptor-binding domain of the SARS-CoV-2 spike protein and are potently neutralizing and stable for many months. The non-outbreak site had two unique introductions of SARS-CoV-2 into the facility, but these did not result in workplace spread or outbreaks. Our results reveal that high seroprevalence among staff can contribute to immunity and protect against subsequent infection and spread within a facility.

Association Between COVID-19 Exposure and Self-reported Compliance With Public Health Guidelines Among Essential Employees at an Institution of Higher Education in the US.

Importance: Detailed analysis of infection rates paired with behavioral and employee-reported risk factors is vital to understanding how transmission of SARS-CoV-2 infection may be exacerbated or mitigated in the workplace. Institutions of higher education are heterogeneous work units that supported continued in-person employment during the COVID-19 pandemic, providing a test site for occupational health evaluation. Objective: To evaluate the association between self-reported protective behaviors and prevalence of SARS-CoV-2 infection among essential in-person employees during the first 6 months of the COVID-19 pandemic in the US. Design, Setting, and Participants: This cross-sectional study was conducted from July 13 to September 2, 2020, at an institution of higher education in Fort Collins, Colorado. Employees 18 years or older without symptoms of COVID-19 who identified as essential in-person workers during the first 6 months of the pandemic were included. Participants completed a survey, and blood and nasal swab samples were collected to assess active SARS-CoV-2 infection via quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and past infection by serologic testing. Exposure: Self-reported practice of protective behaviors against COVID-19 according to public health guidelines provided to employees. Main Outcomes and Measures: Prevalence of current SARS-CoV-2 infection detected by qRT-PCR or previous SARS-CoV-2 infection detected by an IgG SARS-CoV-2 testing platform. The frequency of protective behavior practices and essential workers' concerns regarding contracting COVID-19 and exposing others were measured based on survey responses. Results: Among 508 participants (305 [60.0%] women, 451 [88.8%] non-Hispanic White individuals; mean [SD] age, 41.1 [12.5] years), there were no qRT-PCR positive test results, and only 2 participants (0.4%) had seroreactive IgG antibodies. Handwashing and mask wearing were reported frequently both at work (480 [94.7%] and 496 [97.8%] participants, respectively) and outside work (465 [91.5%] and 481 [94.7%] participants, respectively). Social distancing was reported less frequently at work (403 [79.5%]) than outside work (465 [91.5%]) (P < .001). Participants were more highly motivated to avoid exposures because of concern about spreading the infection to others (419 [83.0%]) than for personal protection (319 [63.2%]) (P < .001). Conclusions and Relevance: In this cross-sectional study of essential workers at an institution of higher education, when employees reported compliance with public health practices both at and outside work, they were able to operate safely in their work environment during the COVID-19 pandemic.

Serial population-based serosurveys for COVID-19 in two neighbourhoods of Karachi, Pakistan.

OBJECTIVE: To determine population-based estimates of coronavirus disease 2019 (COVID-19) in a densely populated urban community of Karachi, Pakistan. METHODS: Three cross-sectional surveys were conducted in April, June and August 2020 in low- and high-transmission neighbourhoods. Participants were selected at random to provide blood for Elecsys immunoassay for detection of anti-severe acute respiratory syndrome coronavirus-2 antibodies. A Bayesian regression model was used to estimate seroprevalence after adjusting for the demographic characteristics of each district. RESULTS: In total, 3005 participants from 623 households were enrolled in this study. In Phase 2, adjusted seroprevalence was estimated as 8.7% [95% confidence interval (CI) 5.1-13.1] and 15.1% (95% CI 9.4-21.7) in low- and high-transmission areas, respectively, compared with 0.2% (95% CI 0-0.7) and 0.4% (95% CI 0-1.3) in Phase 1. In Phase 3, it was 12.8% (95% CI 8.3-17.7) and 21.5% (95% CI 15.6-28) in low- and high-transmission areas, respectively. The conditional risk of infection was 0.31 (95% CI 0.16-0.47) and 0.41 (95% CI 0.28-0.52) in low- and high-transmission neighbourhoods, respectively, in Phase 2. Similar trends were observed in Phase 3. Only 5.4% of participants who tested positive for COVID-19 were symptomatic. The infection fatality rate was 1.66%, 0.37% and 0.26% in Phases 1, 2 and 3, respectively. CONCLUSION: Continuing rounds of seroprevalence studies will help to improve understanding of secular trends and the extent of infection during the course of the pandemic.

Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys.

Establishing how many people have been infected by SARS-CoV-2 remains an urgent priority for controlling the COVID-19 pandemic. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies have been unclear. We developed a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that seropositivity indicates immune protection, we propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiological parameters needed to evaluate public health interventions and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize serosurvey design given test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.