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1.
NPJ Digital Medicine ; 5(1), 2022.
Article in English | ProQuest Central | ID: covidwho-1795674

ABSTRACT

The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual’s initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals’ normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual’s normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response.

3.
NPJ Digit Med ; 4(1): 166, 2021 Dec 08.
Article in English | MEDLINE | ID: covidwho-1561517

ABSTRACT

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

4.
Ann Intern Med ; 174(9): 1344-1345, 2021 09.
Article in English | MEDLINE | ID: covidwho-1560142

Subject(s)
COVID-19 , SARS-CoV-2 , Humans
5.
Ann Intern Med ; 174(2): 286-287, 2021 02.
Article in English | MEDLINE | ID: covidwho-1534506
10.
Ann Intern Med ; 174(5): 655-662, 2021 05.
Article in English | MEDLINE | ID: covidwho-1067973

ABSTRACT

BACKGROUND: Asymptomatic infection seems to be a notable feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), but the prevalence is uncertain. PURPOSE: To estimate the proportion of persons infected with SARS-CoV-2 who never develop symptoms. DATA SOURCES: Searches of Google News, Google Scholar, medRxiv, and PubMed using the keywords antibodies, asymptomatic, coronavirus, COVID-19, PCR, seroprevalence, and SARS-CoV-2. STUDY SELECTION: Observational, descriptive studies and reports of mass screening for SARS-CoV-2 that were either cross-sectional or longitudinal in design; were published through 17 November 2020; and involved SARS-CoV-2 nucleic acid or antibody testing of a target population, regardless of current symptomatic status, over a defined period. DATA EXTRACTION: The authors collaboratively extracted data on the study design, type of testing performed, number of participants, criteria for determining symptomatic status, testing results, and setting. DATA SYNTHESIS: Sixty-one eligible studies and reports were identified, of which 43 used polymerase chain reaction (PCR) testing of nasopharyngeal swabs to detect current SARS-CoV-2 infection and 18 used antibody testing to detect current or prior infection. In the 14 studies with longitudinal data that reported information on the evolution of symptomatic status, nearly three quarters of persons who tested positive but had no symptoms at the time of testing remained asymptomatic. The highest-quality evidence comes from nationwide, representative serosurveys of England (n = 365 104) and Spain (n = 61 075), which suggest that at least one third of SARS-CoV-2 infections are asymptomatic. LIMITATION: For PCR-based studies, data are limited to distinguish presymptomatic from asymptomatic infection. Heterogeneity precluded formal quantitative syntheses. CONCLUSION: Available data suggest that at least one third of SARS-CoV-2 infections are asymptomatic. Longitudinal studies suggest that nearly three quarters of persons who receive a positive PCR test result but have no symptoms at the time of testing will remain asymptomatic. Control strategies for COVID-19 should be altered, taking into account the prevalence and transmission risk of asymptomatic SARS-CoV-2 infection. PRIMARY FUNDING SOURCE: National Institutes of Health.


Subject(s)
Asymptomatic Infections/epidemiology , COVID-19/epidemiology , Humans , Pandemics , SARS-CoV-2
12.
Nat Med ; 27(1): 73-77, 2021 01.
Article in English | MEDLINE | ID: covidwho-1065908

ABSTRACT

Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.


Subject(s)
COVID-19/diagnosis , Monitoring, Physiologic/methods , Wearable Electronic Devices , Adult , Aged , COVID-19/pathology , Carrier State , Female , Heart Rate , Humans , Male , Mass Screening , Middle Aged , Self Report , Sleep , United States
13.
Cell ; 184(6): 1401, 2021 03 18.
Article in English | MEDLINE | ID: covidwho-1030852

ABSTRACT

The first two vaccines proven to be effective for inhibiting COVID-19 illness were both mRNA, achieving 95% efficacy (and safety) among 74,000 participants (half receiving placebo) after intramuscular delivery of two shots, 3-4 weeks apart. To view this Bench to Bedside, open or download the PDF.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19 Vaccines/therapeutic use , COVID-19/immunology , COVID-19/prevention & control , SARS-CoV-2/immunology , Vaccines, Synthetic/immunology , Vaccines, Synthetic/therapeutic use , Antigen-Presenting Cells/immunology , B-Lymphocytes/immunology , COVID-19/virology , Drug Delivery Systems/methods , Humans , Liposomes , Nanoparticles , Spike Glycoprotein, Coronavirus/immunology , T-Lymphocytes/immunology , Treatment Outcome
14.
Lancet ; 396(10266): 1874, 2020 12 12.
Article in English | MEDLINE | ID: covidwho-1023776
17.
Ann Intern Med ; 173(5): 362-367, 2020 09 01.
Article in English | MEDLINE | ID: covidwho-505964

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world since the first cases of coronavirus disease 2019 (COVID-19) were observed in December 2019 in Wuhan, China. It has been suspected that infected persons who remain asymptomatic play a significant role in the ongoing pandemic, but their relative number and effect have been uncertain. The authors sought to review and synthesize the available evidence on asymptomatic SARS-CoV-2 infection. Asymptomatic persons seem to account for approximately 40% to 45% of SARS-CoV-2 infections, and they can transmit the virus to others for an extended period, perhaps longer than 14 days. Asymptomatic infection may be associated with subclinical lung abnormalities, as detected by computed tomography. Because of the high risk for silent spread by asymptomatic persons, it is imperative that testing programs include those without symptoms. To supplement conventional diagnostic testing, which is constrained by capacity, cost, and its one-off nature, innovative tactics for public health surveillance, such as crowdsourcing digital wearable data and monitoring sewage sludge, might be helpful.


Subject(s)
Asymptomatic Diseases/epidemiology , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Betacoronavirus , COVID-19 , Humans , Pandemics , Prevalence , SARS-CoV-2
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