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1.
Ann Intern Med ; 2022 Oct 11.
Article in English | MEDLINE | ID: covidwho-2067150

ABSTRACT

BACKGROUND: It is important to document the performance of rapid antigen tests (Ag-RDTs) in detecting SARS-CoV-2 variants. OBJECTIVE: To compare the performance of Ag-RDTs in detecting the Delta (B.1.617.2) and Omicron (B.1.1.529) variants of SARS-CoV-2. DESIGN: Secondary analysis of a prospective cohort study that enrolled participants between 18 October 2021 and 24 January 2022. Participants did Ag-RDTs and collected samples for reverse transcriptase polymerase chain reaction (RT-PCR) testing every 48 hours for 15 days. SETTING: The parent study enrolled participants throughout the mainland United States through a digital platform. All participants self-collected anterior nasal swabs for rapid antigen testing and RT-PCR testing. All Ag-RDTs were completed at home, whereas nasal swabs for RT-PCR were shipped to a central laboratory. PARTICIPANTS: Of 7349 participants enrolled in the parent study, 5779 asymptomatic persons who tested negative for SARS-CoV-2 on day 1 of the study were eligible for this substudy. MEASUREMENTS: Sensitivity of Ag-RDTs on the same day as the first positive (index) RT-PCR result and 48 hours after the first positive RT-PCR result. RESULTS: A total of 207 participants were positive on RT-PCR (58 Delta, 149 Omicron). Differences in sensitivity between variants were not statistically significant (same day: Delta, 15.5% [95% CI, 6.2% to 24.8%] vs. Omicron, 22.1% [CI, 15.5% to 28.8%]; at 48 hours: Delta, 44.8% [CI, 32.0% to 57.6%] vs. Omicron, 49.7% [CI, 41.6% to 57.6%]). Among 109 participants who had RT-PCR-positive results for 48 hours, rapid antigen sensitivity did not differ significantly between Delta- and Omicron-infected participants (48-hour sensitivity: Delta, 81.5% [CI, 66.8% to 96.1%] vs. Omicron, 78.0% [CI, 69.1% to 87.0%]). Only 7.2% of the 69 participants with RT-PCR-positive results for shorter than 48 hours tested positive by Ag-RDT within 1 week; those with Delta infections remained consistently negative on Ag-RDTs. LIMITATION: A testing frequency of 48 hours does not allow a finer temporal resolution of the analysis of test performance, and the results of Ag-RDTs are based on self-report. CONCLUSION: The performance of Ag-RDTs in persons infected with the SARS-CoV-2 Omicron variant is not inferior to that in persons with Delta infections. Serial testing improved the sensitivity of Ag-RDTs for both variants. The performance of rapid antigen testing varies on the basis of duration of RT-PCR positivity. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute of the National Institutes of Health.

2.
Patterns (N Y) ; 3(8): 100572, 2022 Aug 12.
Article in English | MEDLINE | ID: covidwho-2015904

ABSTRACT

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.

3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329104

ABSTRACT

Background: There 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. Methods: Participants 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 proportion of participants with Ag-RDT+ results in relation to their first RT-PCR+ result. Subsample analysis was performed based on the result of participants′ second RT-PCR test within 48 hours of the first RT-PCR+ test. Results: From the 7,349 participants enrolled in the parent study, 5,506 met the eligibility criteria for this study. 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 36 (23.5%) tested Ag-RDT+ within 48 hours as first RT-PCR+. The differences 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;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 (Delta: 79.3% [60.3-92.1] vs. Omicron: 89.5% [78.5-96.0]). Conversely, the 38 participants who had an isolated positive RT-PCR remained consistently negative on Ag-RDT, regardless of the variant. Conclusions: The performance of Ag-RDT is not inferior among Omicron variant in comparison to the Delta variant. The improvement in sensitivity of Ag-RDT 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.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308710

ABSTRACT

The ongoing COVID-19 pandemic calls for a multi-faceted public health response comprising complementary interventions to control the spread of the disease while vaccines and therapies are developed. Many of these interventions need to be informed by epidemic risk predictions given available data, including symptoms, contact patterns, and environmental factors. Here we propose a novel probabilistic formalism based on Individual-Level Models (ILMs) that offers rigorous formulas for the probability of infection of individuals, which can be parameterised via Maximum Likelihood Estimation (MLE) applied on compartmental models defined at the population level. We describe an approach where individual data collected in real-time is integrated with overall case counts to update the a predictor of the susceptibility of infection as a function of individual risk factors.

5.
Sci Rep ; 12(1): 1857, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671608

ABSTRACT

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.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/prevention & control , COVID-19/epidemiology , COVID-19/transmission , Contact Tracing/methods , Female , Humans , Male , Prevalence , Public Health
6.
R Soc Open Sci ; 9(1): 210948, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1666238

ABSTRACT

College campuses are vulnerable to infectious disease outbreaks, and there is an urgent need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world adapt 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 contain a mumps outbreak at Harvard University in 2016. We used our model to determine which containment interventions were most effective and study alternative scenarios without and with earlier interventions. This model allows for stochastic variation in small populations, missing or unobserved case data and changes in disease transmission rates post-intervention. The results suggest that control measures implemented by the University's Health Services, including rapid isolation of suspected cases, were very effective at containing the outbreak. Without those measures, the outbreak could have been four times larger. More generally, we conclude that universities should apply (i) diagnostic protocols that address false negatives from molecular tests and (ii) strict quarantine policies to contain the spread of easily transmissible infectious diseases such as mumps among their students. This modelling approach could be applied to data from other outbreaks in college campuses and similar small population settings.

7.
Viruses ; 13(8)2021 08 13.
Article in English | MEDLINE | ID: covidwho-1376992

ABSTRACT

While investigating a signal of adaptive evolution in humans at the gene LARGE, we encountered an intriguing finding by Dr. Stefan Kunz that the gene plays a critical role in Lassa virus binding and entry. This led us to pursue field work to test our hypothesis that natural selection acting on LARGE-detected in the Yoruba population of Nigeria-conferred resistance to Lassa Fever in some West African populations. As we delved further, we conjectured that the "emerging" nature of recently discovered diseases like Lassa fever is related to a newfound capacity for detection, rather than a novel viral presence, and that humans have in fact been exposed to the viruses that cause such diseases for much longer than previously suspected. Dr. Stefan Kunz's critical efforts not only laid the groundwork for this discovery, but also inspired and catalyzed a series of events that birthed Sentinel, an ambitious and large-scale pandemic prevention effort in West Africa. Sentinel aims to detect and characterize deadly pathogens before they spread across the globe, through implementation of its three fundamental pillars: Detect, Connect, and Empower. More specifically, Sentinel is designed to detect known and novel infections rapidly, connect and share information in real time to identify emerging threats, and empower the public health community to improve pandemic preparedness and response anywhere in the world. We are proud to dedicate this work to Stefan Kunz, and eagerly invite new collaborators, experts, and others to join us in our efforts.


Subject(s)
Disaster Planning , Lassa Fever/epidemiology , Lassa virus/physiology , Africa, Western/epidemiology , Disaster Planning/methods , Humans , Lassa Fever/genetics , Lassa Fever/prevention & control , Lassa Fever/virology , Lassa virus/genetics , N-Acetylglucosaminyltransferases/genetics , N-Acetylglucosaminyltransferases/immunology , Nigeria/epidemiology , Pandemics , Polymorphism, Genetic , Receptors, Virus/genetics , Receptors, Virus/immunology
8.
Cell ; 182(6): 1366-1371, 2020 09 17.
Article in English | MEDLINE | ID: covidwho-739789

ABSTRACT

Operation Outbreak (OO) is a Bluetooth-based simulation platform that teaches students how pathogens spread and the impact of interventions, thereby facilitating the safe reopening of schools. OO also generates data to inform epidemiological models and prevent future outbreaks. Before SARS-CoV-2 was reported, we repeatedly simulated a virus with similar features, correctly predicting many human behaviors later observed during the pandemic.


Subject(s)
Computer Simulation , Computer-Assisted Instruction/methods , Contact Tracing/methods , Coronavirus Infections/epidemiology , Epidemiology/education , Pneumonia, Viral/epidemiology , Basic Reproduction Number , COVID-19 , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Humans , Mobile Applications , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Smartphone
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