Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.

Estimating the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using mechanistic models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study

BackgroundSARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. MethodsNine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. FindingsAbsent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. ConclusionsResults from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.

Sample Size Calculations for Variant Surveillance in the Presence of Biological and Systematic Biases

As demonstrated during the SARS-CoV-2 pandemic, detecting and tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. Pathogen genome sequencing has emerged as the primary tool for variant characterization, so it is important to consider the number of sequences needed when designing surveillance programs or studies, both to ensure accurate conclusions and to optimize use of limited resources. However, current approaches to calculating sample size for variant monitoring often do not account for the biological and logistical processes that can bias which infections are detected and which samples are ultimately selected for sequencing. In this manuscript, we introduce a framework that models the full process-- including potential sources of bias--from infection detection to variant characterization, and we demonstrate how to use this framework to calculate appropriate sample sizes for sequencing-based surveillance studies. We consider both cross-sectional and continuous sampling, and we have implemented our method in a publicly available tool that allows users to estimate necessary sample sizes given a specific aim (e.g., variant detection or measuring variant prevalence) and sampling method. Our framework is designed to be easy to use, while also flexible enough to be adapted to other pathogens and surveillance scenarios.

In-person schooling and associated COVID-19 risk in the United States over Spring Semester 2021

Because of the importance of schools to childhood development, the relationship between in-person schooling and COVID-19 risk has been one of the most important questions of the COVID-19 pandemic. Previous work using data from the United States in winter 2020-21 showed that in-person schooling carried some risk for household members, and that mitigation measures reduced this risk. However, in-person schooling behavior and the COVID-19 landscape changed radically over the 2021 spring semester. Here we use data from a massive online survey to characterize changes in in-person schooling behavior and associated risks over that period. We find a significant increase in the frequency of in-person schooling and a reduction in mitigation, and that in-person schooling is associated with increased reporting of COVID-19 outcomes, even among vaccinated individuals (though the absolute risk among the vaccinated is greatly reduced). Moreover, vaccinated teachers working outside the home were less likely to report COVID-19-related outcomes than unvaccinated teachers reporting no work outside the home. Adequate mitigation measures appear to eliminate the excess risk associated with in person schooling.

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

What is already known about this topic?The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July--December 2021. What is added by this report?Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July--December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. What are the implications for public health practice?Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.

Progress of the Delta variant and erosion of vaccine effectiveness, a warning from Utah

Since the emergence of SARS-CoV-2, vaccines have been heralded as the best way to curtail the pandemic. Clinical trials have shown SARS-CoV-2 vaccines to be highly efficacious against both disease and infection. However, those currently in use were primarily tested against early lineages. Data on vaccine effectiveness (VE) against variants of concern (VOC), including the Delta variant (B.1.617.2), remain limited. To examine the effectiveness of vaccination in Utah we compared the proportion of cases reporting vaccination to that expected at different VEs, then estimated the combined daily vaccine effectiveness using a field evaluation approach. Delta has rapidly outcompeted all other variants and, as of June 20th, represents 70% of all SARS-CoV-2 viruses sequenced in Utah. If we attribute the entire change in VE to the Delta variant, the estimated vaccine effectiveness against Delta would be 82% (95% CI: 78%, 85%). We show a modest reduction in vaccine effectiveness against COVID-19 in Utah corresponding to the expansion of the Delta lineage in the state. This reduction in the effectiveness of available vaccines correlated with the arrival of novel VOCs, rather than waning immunity, is highly concerning.

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Juba, South Sudan: a population-based study

BackgroundRelatively few COVID-19 cases and deaths have been reported through much of sub-Saharan Africa, including South Sudan, although the extent of SARS-CoV-2 spread remains unclear due to weak surveillance systems and few population-representative serosurveys. MethodsWe conducted a representative household-based cross-sectional serosurvey in Juba, South Sudan. We quantified IgG antibody responses to SARS-CoV-2 spike protein receptor-binding domain and estimated seroprevalence using a Bayesian regression model accounting for test performance. ResultsWe recruited 2,214 participants from August 10 to September 11, 2020 and 22.3% had anti-SARS-CoV-2 IgG titers above levels in pre-pandemic samples. After accounting for waning antibody levels, age, and sex, we estimated that 38.5% (32.1 - 46.8) of the population had been infected with SARS-CoV-2. For each RT-PCR confirmed COVID-19 case, 104 (87-126) infections were unreported. Background antibody reactivity was higher in pre-pandemic samples from Juba compared to Boston, where the serological test was validated. The estimated proportion of the population infected ranged from 30.1% to 60.6% depending on assumptions about test performance and prevalence of clinically severe infections. ConclusionsSARS-CoV-2 has spread extensively within Juba. Validation of serological tests in sub-Saharan African populations is critical to improve our ability to use serosurveillance to understand and mitigate transmission.

Household COVID-19 risk and in-person schooling

In-person schooling has proved contentious and difficult to study throughout the SARS-CoV-2 pandemic. Data from a massive online survey in the United States indicates an increased risk of COVID-19-related outcomes among respondents living with a child attending school in-person. School-based mitigation measures are associated with significant reductions in risk, particularly daily symptoms screens, teacher masking, and closure of extra-curricular activities. With seven or more mitigation measures, the association between in-person schooling and COVID-19-related outcomes all but disappears. Teachers working outside the home were more likely to report COVID-19-related outcomes, but this association is similar to other occupations (e.g., healthcare, office work). In-person schooling is associated with household COVID-19 risk, but this risk can likely be controlled with properly implemented school-based mitigation measures. One sentence summaryLiving with children attending in-person school is linked to a higher risk of COVID-19 outcomes, which school-based interventions can mitigate.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Testing out of quarantine

Since SARS-CoV-2 emerged, a 14-day quarantine has been recommended based on COVID-19"s incubation period. Using an RT-PCR or rapid antigen test to "test out" of quarantine is a frequently proposed strategy to shorten duration without increasing risk. We calculated the probability that infected individuals test negative for SARS-CoV-2 on a particular day post-infection and remain symptom free for some period of time. We estimate that an infected individual has a 20.1% chance (95% CI 9.8-32.6) of testing RT-PCR negative on day five post-infection and remaining asymptomatic until day seven. We also show that the added information a test provides decreases as we move further from the test date, hence a less sensitive test that returns rapid results is often preferable to a more sensitive test with a delay.

Household Transmission of SARS-COV-2: Insights from a Population-based Serological Survey

BackgroundKnowing the transmissibility of asymptomatic infections and risk of infection from household- and community-exposures is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals. ObjectiveEstimate the risk of SARS-CoV-2 infection from household and community exposures, and identify key risk factors for transmission and infection. DesignCross-sectional household serosurvey and transmission model. SettingGeneva, Switzerland Participants4,524 household members [≥]5 years from 2,267 households enrolled April-June 2020. MeasurementsPast SARS-CoV-2 infection confirmed through IgG ELISA. Chain-binomial models based on the number of infections within households used to estimate the cumulative extra-household infection risk and infection risk from exposure to an infected household member by demographics and infectors symptoms. ResultsThe chance of being infected by a SARS-CoV-2 infected household member was 17.3% (95%CrI,13.7-21.7%) compared to a cumulative extra-household infection risk of 5.1% (95%CrI,4.5-5.8%). Infection risk from an infected household member increased with age, with 5-9 year olds having 0.4 times (95%CrI, 0.07-1.4) the odds of infection, and [≥]65 years olds having 2.7 (95%CrI,0.88-7.4) times the odds of infection of 20-49 year olds. Working-age adults had the highest extra-household infection risk. Seropositive asymptomatic household members had 69.6% lower odds (95%CrI,33.7-88.1%) of infecting another household member compared to those reporting symptoms, accounting for 14.7% (95%CrI,6.3-23.2%) of all household infections. LimitationsSelf-reported symptoms, small number of seropositive kids and imperfect serologic tests. ConclusionThe risk of infection from exposure to a single infected household member was more than three-times that of extra-household exposures over the first pandemic wave. Young children had a lower risk of infection from household members. Asymptomatic infections are far less likely to transmit than symptomatic ones but do cause infections. Funding SourceSwiss Federal Office of Public Health, Swiss School of Public Health (Corona Immunitas research program), Fondation de Bienfaisance du Groupe Pictet, Fondation Ancrage, Fondation Privee des Hopitaux Universitaires de Geneve, and Center for Emerging Viral Diseases.

Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States

Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (Reff) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with an Reff below 1 when combined with either stay at home orders (median Reff 0.97, 95% confidence interval (CI) 0.58-1.39)* or face masks (median Reff 0.97, 95% CI 0.58-1.39)*. While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

Maximizing and evaluating the impact of test-trace-isolate programs

September 2, 2020 BackgroundTest-trace-isolate programs are an essential part of COVID-19 control that offer a more targeted approach than many other non-pharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods and FindingsWe present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of case detection, speed of isolation, contact tracing completeness and speed of quarantine using parameters consistent with COVID-19 transmission (R0 = 2.5, generation time 6.5 days). We show that R is most sensitive to changes to the proportion of infections detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (< 30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Formally framing the dynamical process also indicates that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of program performance are sensitive to assumptions about COVID-19 natural history, our qualitative findings are robust across numerous sensitivity analyses. ConclusionsEffective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic, and can alleviate the need for more restrictive social distancing measures.

Insights into the practical effectiveness of RT-PCR testing for SARS-CoV-2 from serologic data, a cohort study

BackgroundVirologic detection of SARS-CoV-2 through Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) has limitations for surveillance. Serologic tests can be an important complementary approach. ObjectiveAssess the practical performance of RT-PCR based surveillance protocols, and the extent of undetected SARS-CoV-2 transmission in Shenzhen, China. DesignCohort study nested in a public health response. SettingShenzhen, China; January-May 2020. Participants880 PCR-negative close-contacts of confirmed COVID-19 cases and 400 residents without known exposure (main analysis). Fifty-seven PCR-positive case contacts (timing analysis). MeasurementsVirological testing by RT-PCR. Measurement of anti-SARS-CoV-2 antibodies in PCR-negative contacts 2-15 weeks after initial testing using total Ab ELISA. Rates of undetected infection, performance of RT-PCR over the course of infection, and characteristics of seropositive but PCR-negative individuals were assessed. ResultsThe adjusted seropositivity rate for total Ab among 880 PCR-negative close-contacts was 4.1% (95%CI, 2.9% to 5.7%), significantly higher than among residents without known exposure to cases (0.0%, 95%CI, 0.0% to 1.0%). PCR-positive cases were 8.0 times (RR; 95% CI, 5.3 to 12.7) more likely to report symptoms than the PCR-negative individuals who were seropositive, but otherwise similar. RT-PCR missed 36% (95%CI, 28% to 44%) of infected close-contacts, and false negative rates appear to be highly dependent on stage of infection. LimitationsNo serological data were available on PCR-positive cases. Sample size was limited, and only 20% of PCR-negative contacts met inclusion criteria. ConclusionEven rigorous RT-PCR testing protocols may miss a significant proportion of infections, perhaps in part due to difficulties timing testing of asymptomatics for optimal sensitivity. Surveillance and control protocols relying on RT-PCR were, nevertheless, able to contain community spread in Shenzhen. Funding sourceBill & Melinda Gates Foundation, Special Foundation of Science and Technology Innovation Strategy of Guangdong Province of China, and Key Project of Shenzhen Science and Technology Innovation Commission, Shenzhen, China

Genomic Diversity of SARS-CoV-2 During Early Introduction into the United States National Capital Region

BackgroundThe early COVID-19 pandemic has been characterized by rapid global spread. In the United States National Capital Region, over 2,000 cases were reported within three weeks of its first detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2, the virus that causes COVID-19, in the region. By correlating genetic information to disease phenotype, we also aimed to gain insight into any correlation between viral genotype and case severity or transmissibility. MethodsWe performed whole genome sequencing of clinical SARS-CoV-2 samples collected in March 2020 by the Johns Hopkins Health System. We analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and the global phylogeny to understand early establishment of the virus within the region. ResultsWe analyzed 620 samples from the Johns Hopkins Health System collected between March 11-31, 2020, comprising 37.3% of the total cases in Maryland during this period. We selected 143 of these samples for sequencing, generating 114 complete viral genomes. These genomes belong to all five major Nextstrain-defined clades, suggesting multiple introductions into the region and underscoring the diversity of the regional epidemic. We also found that clinically severe cases had genomes belonging to all of these clades. ConclusionsWe established a pipeline for SARS-CoV-2 sequencing within the Johns Hopkins Health system, which enabled us to capture the significant viral diversity present in the region as early as March 2020. Efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and interconnectedness of the region as a whole.

A scenario modeling pipeline for COVID-19 emergency planning

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

Characterization of clinical progression of COVID-19 patients in Shenzhen, China

BackgroundUnderstanding clinical progression of COVID-19 is a key public health priority that informs resource allocation during an emergency. We characterized clinical progression of COVID-19 and determined important predictors for faster clinical progression to key clinical events and longer use of medical resources. Methods and FindingsThe study is a single-center, observational study with prospectively collected data from all 420 patients diagnosed with COVID-19 and hospitalized in Shenzhen between January 11th and March 10th, 2020 regardless of clinical severity. Using competing risk regressions according to the methods of Fine and Gray, we found that males had faster clinical progression than females in the older age group and the difference could not be explained by difference in baseline conditions or smoking history. We estimated the proportion of cases in each severity stage over 80 days following symptom onset using a nonparametric method built upon estimated cumulative incidence of key clinical events. Based on random survival forest models, we stratified cases into risk sets with very different clinical trajectories. Those who progressed to the severe stage (22%,93/420), developed acute respiratory distress syndrome (9%,39/420), and were admitted to the intensive care unit (5%,19/420) progressed on average 9.5 days (95%CI 8.7,10.3), 11.0 days (95%CI 9.7,12.3), and 10.5 days (95%CI 8.2,13.3), respectively, after symptom onset. We estimated that patients who were admitted to ICUs remained there for an average of 34.4 days (95%CI 24.1,43.2). The median length of hospital stay was 21.3 days (95%CI, 20.5,22.2) for cases who did not progress to the severe stage, but increased to 52.1 days (95%CI, 43.3,59.5) for those who required critical care. ConclusionsOur analyses provide insights into clinical progression of cases starting early in the course of infection. Patient characteristics near symptom onset both with and without lab parameters have tremendous potential for predicting clinical progression and informing strategic response.

Estimating the burden of SARS-CoV-2 in France

France has been heavily affected by the SARS-CoV-2 epidemic and went into lockdown on the 17th March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find 2.6% of infected individuals are hospitalized and 0.53% die, ranging from 0.001% in those <20y to 8.3% in those >80y. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 3.3 to 0.5 (84% reduction). By 11 May, when interventions are scheduled to be eased, we project 3.7 million (range: 2.3-6.7) people, 5.7% of the population, will have been infected. Population immunity appears insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

A systematic review of antibody mediated immunity to coronaviruses: antibody kinetics, correlates of protection, and association of antibody responses with severity of disease

The duration and nature of immunity generated in response to SARS-CoV-2 infection is unknown. Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARS-CoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The timescale of protection is a critical determinant of the future impact of the pathogen. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. The dynamics of immunity and nature of protection are relevant to discussions surrounding therapeutic use of convalescent sera as well as efforts to identify individuals with protective immunity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV-1, MERS-CoV and human endemic coronaviruses (HCoVs). We reviewed 1281 abstracts and identified 322 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While studies of SARS-CoV-2 are necessary to determine immune responses to it, evidence from other coronaviruses can provide clues and guide future research. Key QuestionsO_TEXTBOXKey Questions for SARS-CoV-2 O_LIWhat are the kinetics of immune responses to infection? C_LIO_LIDo people who have more severe disease mount stronger antibody responses after infection? C_LIO_LIHow do antibody responses vary between different types of antibodies or as measured by different assays? C_LIO_LIHow does the presence of antibodies impact the clinical course and severity of the disease? C_LIO_LIIs there cross-reactivity with different coronaviruses? C_LIO_LIDoes cross-reactivity lead to cross-protection? C_LIO_LIWill infection protect you from future infection? C_LIO_LIHow long will immunity last? C_LIO_LIWhat are correlates of protection? C_LI C_TEXTBOX

Variation in False Negative Rate of RT-PCR Based SARS-CoV-2 Tests by Time Since Exposure

SARS-CoV-2 RT-PCR based tests are being used to "rule out" infection among high-risk individuals such as exposed inpatients and healthcare workers. It is critical to understand how the predictive value of the test varies with time from exposure and symptom onset in order to avoid being falsely reassured by negative tests. As such, the goal of our study was to estimate the false negative rate by day since infection. We used previously published data on RT-PCR sensitivity on samples derived from nasal swabs by day since symptom onset (n=633) and fit a cubic polynomial spline to calculate the false negative rate by day since exposure and symptom onset. Over the four days of infection prior to the typical time of symptom onset (day 5) the probability of a false negative test in an infected individual falls from 100% on day one (95% CI 69-100%) to 61% on day four (95% CI 18-98%), though there is considerable uncertainty in these numbers. On the day of symptom onset, the median false negative rate was 39% (95% CI 16-77%). This decreased to 26% (95% CI 18-34%) on day 8 (3 days after symptom onset), then began to rise again, from 27% (95% CI 20-34%) on day 9 to 61% (95% CI 54-67%) on day 21. Care must be taken when interpreting RT-PCR tests for SARS-CoV-2 infection, particularly if performed early in the course of infection, when using these results as a basis for removing precautions intended to prevent onward transmission. If there is high clinical suspicion, patients should not be ruled out on the basis of RT-PCR alone, and the clinical and epidemiologic situation should be carefully considered.