Disparities in SARS-CoV-2 seroprevalence among individuals presenting for care in central North Carolina over a six-month period

BackgroundRobust community-level SARS-CoV-2 prevalence estimates have been difficult to obtain in the American South and outside of major metropolitan areas. Furthermore, though some previous studies have investigated the association of demographic factors such as race with SARS-CoV-2 exposure risk, fewer have correlated exposure risk to surrogates for socioeconomic status such as health insurance coverage. MethodsWe used a highly specific serological assay utilizing the receptor binding domain of the SARS-CoV-2 spike-protein to identify SARS-CoV-2 antibodies in remnant blood samples collected by the University of North Carolina Health system. We estimated the prevalence of SARS-CoV-2 in this cohort with Bayesian regression, as well as the association of critical demographic factors with higher prevalence odds. FindingsBetween April 21st and October 3rd of 2020, a total of 9,624 unique samples were collected from clinical sites in central NC and we observed a seroprevalence increase from 2{middle dot}9 (1{middle dot}7, 4{middle dot}3) to 9{middle dot}1 (7{middle dot}2, 11{middle dot}1) over the study period. Individuals who identified as Latinx were associated with the highest odds ratio of SARS-CoV-2 exposure at 7{middle dot}77 overall (5{middle dot}20, 12{middle dot}10). Increased odds were also observed among Black individuals and individuals without public or private health insurance. InterpretationOur data suggests that for this care-accessing cohort, SARS-CoV-2 seroprevalence was significantly higher than cumulative total cases reported for the study geographical area six months into the COVID-19 pandemic in North Carolina. The increased odds of seropositivity by ethnoracial grouping as well as health insurance highlights the urgent and ongoing need to address underlying health and social disparities in these populations. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies published through March 21st, 2021. We used search terms that included "COVID-19", "SARS-CoV-2", "prevalence" and "seroprevalence". Our search resulted in 399 papers, from which we identified 58 relevant studies describing SARS-CoV-2 seroprevalence at sites around the United States from March 1 to December 9, 2020, 12 of which utilized remnant clinical samples and three of which overlapped with our study area. Most notably, one study of 4,422 asymptomatic inpatients and outpatients in central NC from April 28-June 19, 2020 found an estimated seroprevalence of 0{middle dot}7 -0{middle dot}8%, and another study of 177,919 inpatients and outpatients (3,817 from NC) from July 27-September 24, 2020 found an estimated seroprevalence of 2{middle dot}5 -6{middle dot}8%. Added value of this studyThis is the largest SARS-CoV-2 seroprevalence cohort published to date in NC. Importantly, we used a Bayesian framework to account for uncertainty in antibody assay sensitivity and specificity and investigated seropositivity by important demographic variables that have not yet been studied in this context in NC. This study corroborates other reports that specific demographic factors including race, ethnicity and the lack of public or private insurance are associated with elevated risk of SARS-CoV-2 infection. Furthermore, in a subset of serum samples, we identify other SARS-CoV-2 antibodies elicited by these individuals, including functionally neutralizing antibodies. Implications of all the available evidenceIt is difficult to say the exact seroprevalence in the central North Carolina area, but a greater proportion of the population accessing healthcare has been infected by SARS-CoV-2 than is reflected by infection cases confirmed by molecular testing. Furthermore, local governments need to prioritize addressing the many forms of systemic racism and socioeconomic disadvantage that drive SARS-CoV-2 exposure risk, such as residential and occupational risk, and an urgent need to provide access to SARS-CoV-2 testing and vaccination to these groups.

Higher viral load drives infrequent SARS-CoV-2 transmission between asymptomatic residence hall roommates

In 2019-2020, the COVID-19 pandemic spread to over 200 countries in less than six months. To understand the basis of this aggressive spread, it is essential to determine the transmission rate and define the factors that increase the risk of transmission. One complication is the large fraction of asymptomatic cases, particularly in young populations: these individuals have viral loads indistinguishable from symptomatic people and do transmit the SARS-CoV-2 virus, but they often go undetected. As university students living in residence halls commonly share a small living space with roommates, some schools established regular, high density testing programs to mitigate on-campus spread. In this study, we analyzed longitudinal testing data of residence hall students at the University of Colorado Boulder. We observed that students in single rooms were infected at a lower rate than students in multiple occupancy rooms. However, this was not due to high rates of transmission between roommates, which only occurred approximately 20% of the time. Since these cases were usually asymptomatic at the time of diagnosis, this provides further evidence for asymptomatic transmission. Notably, individuals who likely transmitted to their roommates had an average viral load [~]6.5 times higher than individuals who did not. Although students were moved to separate isolation rooms after diagnosis, there was no difference in time to isolation between these cases with or without transmission. This analysis argues that inter-roommate transmission occurs in a minority of cases in university residence halls and provides strong correlative evidence that viral load can be proportional to the probability of transmission.

GPS-estimated foot traffic data and venue selection for COVID-19 serosurveillance studies

The initial phase of the COVID-19 pandemic in the US was marked by limited diagnostic testing, resulting in the need for seroprevalence studies to estimate cumulative incidence and define epidemic dynamics. In lieu of systematic representational surveillance, venue-based sampling was often used to rapidly estimate a communitys seroprevalence. However, biases and uncertainty due to site selection and use of convenience samples are poorly understood. Using data from a SARS-CoV-2 serosurveillance study we performed in Somerville, Massachusetts, we found that the uncertainty in seroprevalence estimates depends on how well sampling intensity matches the known or expected geographic distribution of seropositive individuals in the study area. We use GPS-estimated foot traffic to measure and account for these sources of bias. Our results demonstrated that study-site selection informed by mobility patterns can markedly improve seroprevalence estimates. Such data should be used in the design and interpretation of venue-based serosurveillance studies.

Modeling the effectiveness of olfactory testing to limit SARS-2-CoV transmission

A central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to rise to this challenge unless testing capacity increases by at least an order of magnitude while decreasing turnaround times. Here, we evaluate an alternative strategy based on the monitoring of olfactory dysfunction, a symptom identified in 76-83% of SARS-CoV-2 infections--including those with no other symptoms--when a standardized olfaction test is used. We model how screening for olfactory dysfunction, with reflexive molecular tests, could be beneficial in reducing community spread of SARS-CoV-2 by varying testing frequency and the prevalence, duration, and onset time of olfactory dysfunction. We find that monitoring olfactory dysfunction could reduce spread via regular screening, and could reduce risk when used at point-of-entry for single-day events. In light of these estimated impacts, and because olfactory tests can be mass produced at low cost and self-administered, we suggest that screening for olfactory dysfunction could be a high impact and cost-effective method for broad COVID-19 screening and surveillance.

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

When a vaccine for COVID-19 becomes available, limited initial supply will raise the question of how to prioritize the available doses and thus underscores the need for transparent, evidence-based strategies that relate knowledge of, and uncertainty in, disease transmission, risk, vaccine efficacy, and existing population immunity. Here, we employ a model-informed approach to vaccine prioritization that evaluates the impact of prioritization strategies on cumulative incidence and mortality and accounts for population factors such as age, contact structure, and seroprevalence, and vaccine factors including imperfect and age-varying efficacy. This framework can be used to evaluate and compare existing strategies, and it can also be used to derive an optimal prioritization strategy to minimize mortality or incidence. We find that a transmission-blocking vaccine should be prioritized to adults ages 20-49y to minimize cumulative incidence and to adults over 60y to minimize mortality. Direct vaccination of adults over 60y minimizes mortality for vaccines that do not block transmission. We also estimate the potential benefit of using individual-level serological tests to redirect doses to only seronegative individuals, improving the marginal impact of each dose. We argue that this serology-informed vaccination approach may improve the efficiency of vaccination efforts while partially addressing existing inequities in COVID-19 burden and impact.

Serial population based serosurvey of antibodies to SARS-CoV-2 in a low and high transmission area of Karachi, Pakistan

Serial household antibody sero-surveys informs the pandemic where testing is non-uniform. Young populations with intergenerational co-residence may have different transmission dynamics. We conducted two serial cross-sectional surveys in April and June 2020 in low- and high-transmission neighborhoods of Karachi, Pakistan, using random sampling. Symptoms were assessed and blood tested for antibody using chemiluminescence. Seroprevalence was adjusted using Bayesian regression and post stratification. CRI with 95% confidence intervals was obtained. We enrolled 2004 participants from 406 households. In June 8.7% (95% CI 5.1-13.1) and 15.1% (95% CI 9.4-21.7) were infected 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 April. Conditional risk of infection was 0.31 (95% CI 0.16-0.47) and 0.41(95% CI 0.28-0.52) respectively with only 5.4% symptomatic. Rapid increase in seroprevalence from baseline is seen in Karachi, with a high probability of infection within household. Article Summary LineRapid increase in seroprevalence of antibodies against SARS-CoV-2 was seen in Karachi, Pakistan from April to June 2020 with a high conditional risk of infection within the household

Surveillance testing of SARS-CoV-2

The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust surveillance, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential viral growth, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

Case Study: Using Facebook Data to Monitor Adherence to Stay-at-home Orders in Colorado and Utah

In the absence of effective treatments, social distancing has been the only public health measure available to combat the COVID-19 pandemic. In the US, implementing this response has been left to state, county, and city officials, and many localities have issued some form of a stay-at-home order. Without existing tools and with limited resources, localities struggled to understand how their orders changed behavior. In response, several technology companies opened access to their users location data. As part of the COVID-19 Data Mobility Network, we obtained access to Facebook User data and developed four key metrics and visualizations to monitor various aspects of adherence to stay at home orders. These metrics were carefully incorporated into static and interactive visualizations for dissemination to local officials. All code is open source and freely available at https://github.com/ryanlayer/COvid19

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

Establishing how many people have already been infected by SARS-CoV-2 is an urgent priority for controlling the COVID-19 pandemic. Patchy virological testing has hampered interpretation of confirmed case counts, and unknown rates of asymptomatic and mild infections make it challenging to develop evidence-based public health policies. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies has been unclear. Here, we used a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across tested subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that serological positivity indicates immune protection, we propagated these estimates and uncertainty through dynamical models to assess the uncertainty in the epidemiological parameters needed to evaluate public health interventions. We examined the relative accuracy of convenience samples versus structured surveys to estimate population seroprevalence and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize the design of serological surveys given particular test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.