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BackgroundPerformance of Rapid Antigen Tests for SARS-CoV-2 (Ag-RDT) varies over the course of an infection, and their performance is not well established among asymptomatic individuals. ObjectiveEvaluate performance of Ag-RDT for detection of SARS-CoV-2 in relation to onset of infection for symptomatic and asymptomatic participants. Design, Setting, and ParticipantsProspective cohort study conducted from October 2021 to February 2022 among participants > 2 years-old from across the US who enrolled using a smartphone app. During each testing encounter, participants self-collected one nasal swab and performed Ag-RDT at home; at-least fifteen minutes later, a second nasal swab was self-collected and shipped for SARS-CoV-2 RT-PCR at a central lab. Both nasal swabs were collected 7 times at 48-hour intervals (over approximately 14 days) followed by an extra nasal swab collection with home Ag-RDT test 48-hours after their last PCR sample. Each participant was assigned to one of the three emergency use authorized (EUA) Ag-RDT tests used in this study. This analysis was limited to participants who were asymptomatic and tested negative by antigen and molecular test on their first day of study participation. ExposureSARS-CoV-2 positivity was determined by testing a single home-collected anterior nasal sample with three FDA EUA molecular tests, where 2 out 3 positive test results were needed to determine a SARS-CoV-2 positive result. Onset of infection was defined as day on which the molecular PCR comparator result was positive for the first time. Main Outcomes and MeasuresSensitivity of Ag-RDT was measured based on testing once (same-day), twice (at 48-hours) and thrice (at 96 hours). Analysis was repeated for different Days Post Index PCR Positivity (DPIPP) and stratified based on symptom-status on a given DPIPP. ResultsA total of 7,361 participants enrolled in the study and 5,609 were eligible for this analysis. Among 154 eligible participants who tested positive for SARS-CoV-2 infection based on RT-PCR, 97 were asymptomatic and 57 had symptoms at onset of infection (DPIPP 0). Serial testing with Ag-RDT twice over 48-hours resulted in an aggregated sensitivity of 93.4% (95% CI: 89.1-96.1%) among symptomatic participants on DPIPP 0-6. Among the 97 people who were asymptomatic at the onset of infection, 19 were singleton RT-PCR positive, i.e., their positive test was preceded and followed by a negative RT-PCR test within 48-hours. Excluding these singleton positives, aggregated sensitivity on DPIPP 0-6 for two-time serial-testing among asymptomatic participants was lower 62.7% (54.7-70.0%) but improved to 79.0% (71.0-85.3%) with serial testing three times at 48-hour interval. DiscussionPerformance of Ag-RDT within first week of infection was optimized when asymptomatic participants tested three-times at 48-hour intervals and when symptomatic participants tested two-times separated by 48-hours. Key pointsO_ST_ABSQuestionC_ST_ABSWhat is the performance of serial rapid antigen testing (Ag-RDT) in the first week of infection among symptomatic and asymptomatic SARS-CoV-2 infections? FindingsSerial testing with Ag-RDT two-times separated by 48-hours resulted in detection of more than 90% of SARS-CoV-2 infections when symptomatic participants began testing within first week from onset of molecular positivity; participants who were asymptomatic when they began testing within the first-week of molecular positivity observed a sensitivity of 79.0% when they performed three rapid antigen-tests, 48 hours apart. MeaningTo optimize detection of SARS-CoV-2 infection with home antigen tests, people suspected to be infected with SARS-CoV-2 virus should test twice at least 48-hours apart if they are symptomatic and three times at 48-hour intervals if they do not have symptoms (asymptomatic). Key definitionsO_ST_ABSComparator positiveC_ST_ABScomposite definition of molecular positivity if majority of molecular assays were positive Days Past Index Comparator Positive (DPIPP)Number of calendar-days past the day when first Comparator positive was observed Onset of InfectionDPIPP 0, when first Comparator positive was observed Symptomatic and Asymptomatic CasesBased on presence or absence of self-reported symptoms on the day of testing. Sensitivity was measured for Symptomatic and Asymptomatic cases on DPIPP 0-10 First week of InfectionDPIPP 0 - 6
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Universities are particularly vulnerable to infectious disease outbreaks and are also ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures when outbreaks occur. Here, we introduce a SARS-CoV-2 surveillance and response framework based on high-resolution, multimodal data collected during the 2020-2021 academic year at Colorado Mesa University. We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and wifi-based co-location data) alongside pathogen surveillance data (wastewater, random, and reflexive diagnostic testing; and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy decisions. We quantified group attributes that increased disease risk, and highlighted parallels between traditional and wifi-based contact tracing. We additionally used clinical and environmental viral sequencing to identify cryptic transmission, cluster overdispersion, and novel lineages or mutations. Ultimately, we used distinct data types to identify information that may help shape institutional policy and to develop a model of pathogen surveillance suitable for the future of outbreak preparedness.
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BackgroundThere is a need to understand the performance of rapid antigen tests (Ag-RDT) for detection of the Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) SARS-CoV-2 variants. MethodsParticipants without any symptoms were enrolled from October 18, 2021 to January 24, 2022 and performed Ag-RDT and RT-PCR tests every 48 hours for 15 days. This study represents a non-pre-specified analysis in which we sought to determine if sensitivity of Ag-RDT differed in participants with Delta compared to Omicron variant. Participants who were positive on RT-PCR on the first day of the testing period were excluded. Delta and Omicron variants were defined based on sequencing and date of first RT-PCR positive result (RT-PCR+). Comparison of Ag-RDT performance between the variants was based on sensitivity, defined as proportion of participants with Ag-RDT+ results in relation to their first RT-PCR+ result, for different duration of testing with rapid Ag-RDT. Subsample analysis was performed based on the result of participants second RT-PCR test within 48 hours of the first RT-PCR+ test. ResultsFrom the 7,349 participants enrolled in the parent study, 5,506 met the eligibility criteria for this analysis. A total of 153 participants were RT-PCR+ (61 Delta, 92 Omicron); among this group, 36 (23.5%) tested Ag-RDT+ on the same day, and 84 (54.9%) tested Ag-RDT+ within 48 hours as first RT-PCR+. The differences in sensitivity between variants were not statistically significant (same-day: Delta 16.4% [95% CI: 8.2-28.1] vs Omicron 28.2% [95% CI: 19.4-38.6]; and 48-hours: Delta 45.9% [33.1-59.2] vs. Omicron 60.9% [50.1-70.9]). This trend continued among the 86 participants who had consecutive RT-PCR+ result (48-hour sensitivity: Delta 79.3% [60.3-92.1] vs. Omicron: 89.5% [78.5-96.0]). Conversely, the 38 participants who had an isolated RT-PCR+ remained consistently negative on Ag-RDT, regardless of the variant. ConclusionsThe performance of Ag-RDT is not inferior among individuals infected with the SARS-CoV-2 Omicron variant as compared to the Delta variant. The improvement in sensitivity of Ag-RDT noted with serial testing is consistent between Delta and Omicron variant. Performance of Ag-RDT varies based on duration of RT-PCR+ results and more studies are needed to understand the clinical and public health significance of individuals who are RT-PCR+ for less than 48 hours.
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Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18% to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network.
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College campuses are highly vulnerable to infectious disease outbreaks, and there is a pressing need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world reopen to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to past mumps outbreaks in college campuses and used it to determine which control interventions are most effective. Mumps is a very relevant disease in such settings, given its airborne mode of transmission, high infectivity, and recurrence of outbreaks despite availability of a vaccine. Our model allows for stochastic variation in small populations, missing or unobserved case data, and changes in disease transmission rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that in order to decrease infectious disease incidence on their campuses, universities should apply diagnostic protocols that address false negatives from molecular tests, stricter quarantine policies, and effective awareness campaigns among their students and staff. However, one needs to be careful about the assumptions implicit in the model to ensure that the estimated parameters have a reasonable interpretation. This modeling approach could be applied to data from other outbreaks in college campuses and similar small-population settings.
