ImportanceAsymptomatic and presymptomatic carriers of SARS-CoV-2 are an ongoing and significant risk for community spread of the virus, especially with the majority of the world still unvaccinated and new variants emerging.ObjectiveTo quantify the presence and effects of symptom presentation (or lack thereof) on the community transmission of SARS-CoV-2.DesignA cohort of 12,960 young adults participated in health reporting, contact tracing, and COVID-19 testing for 103 days between August 10 and November 20, 2020.SettingA mid-sized university campus in Indiana, United States.ParticipantsUniversity students, most of whom are 18-23 years old (67%) and living in congregate on-campus housing (60%). Of the 12,960 students, 1,556 (12.0%) tested positive for COVID-19 during the 103 day period. Of the positive cases, 1,198 reported sufficient health check data (7 days prior and 7 days post diagnosis) to be classified as asymptomatic or symptomatic.Main OutcomeSecondary attack rate, based on presentation or absence of symptoms and type of symptoms calculated with respect to confirmed close contacts and a 14-day incubation period, varies on the type of symptom, timing of symptoms, and absence of symptoms. A quantifiable understanding of SAR on the longitudinal data of more than one thousand subjects in a university environment provides keen insights about developing strategies to respond to the continued prevalence of COVID-19 in the unvaccinated world and growth of variants.Results32.5% of all cases reported no symptoms within a 15-day window centered on their positive test (7 days prior, the day of the positive test, and 7 days after). The secondary attack rate (SAR) of asymptomatic COVID-19 index cases was 19.1%. The SAR of symptomatic index cases was 25.4%, and while the onset timing of symptoms did not affect transmission, the presence of certain symptoms like fever, shortness of breath, and dry cough increased the SAR as high as 30.0%.Conclusions and RelevanceAsymptomatic rates of transmission of SARS-CoV-2 are much higher than has been estimated in prior studies and continue to pose a significant and ongoing risk in the pandemic, especially with the prevalence of variants like the Delta variant. In addition, different symptoms are associated with varying rates of transmission, posing a significant challenge in how to diagnose or assess risk through mechanisms such as daily health checks for symptom reporting, a practice commonly in place for entry into schools, offices, restaurants, etc. Given the uncertain nature of symptoms and varied transmission rates, this study suggests a broader embrace of masking, social distancing and testing might be needed to counter the variants until higher global vaccination rates can be achieved.
Objectives: The impact and risk of SARS-CoV-2 transmission from asymptomatic and presymptomatic hosts remains an open question. This study measured the secondary attack rates (SARs) and relative risk (RR) of SARS-CoV-2 transmission from asymptomatic and presymptomatic index cases as compared with symptomatic index cases. Methods: We used COVID-19 test results, daily health check reports, and contact tracing data to measure SARs and corresponding RRs among close contacts of index cases in a cohort of 12 960 young adults at the University of Notre Dame in Indiana for 103 days, from August 10 to November 20, 2020. Further analysis included Fisher exact tests to determine the association between symptoms and COVID-19 infection and z tests to determine statistical differences between SARs. Results: Asymptomatic rates of transmission of SARS-CoV-2 were higher (SAR = 0.19; 95% CI, 0.14-0.24) than was estimated in prior studies, producing an RR of 0.75 (95% CI, 0.54-1.07) when compared with symptomatic transmission. In addition, the transmission rate associated with presymptomatic cases (SAR = 0.25; 95% CI, 0.21-0.30) was approximately the same as that for symptomatic cases (SAR = 0.25; 95% CI, 0.19-0.31). Furthermore, different symptoms were associated with different transmission rates. Conclusions: Asymptomatic and presymptomatic hosts of SARS-CoV-2 are a risk for community spread of COVID-19, especially with new variants emerging. Moreover, typical symptom checks may easily miss people who are asymptomatic or presymptomatic but still infectious. Our study results may be used as a guide to analyze the spread of SARS-CoV-2 variants and help inform appropriate public health measures as they relate to asymptomatic and presymptomatic cases.
COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability, and organizations must be prepared to detect and mitigate its risk to their people and activities. In this report we share key lessons learned from an adaptive COVID-19 testing program implemented at a mid-sized university in the Midwest. The program utilized two simple, diverse, and easily interpretable machine learning models to quickly and accurately predict which students were at elevated risk for contracting COVID-19 and should be called proactively for testing. Our adaptive testing cohorts produced positivity rates that were 26% higher than the random cohort: 0.58% positivity (95% CI 0.47% to 0.68%) from 19,171 tests, and 0.46% positivity (95% CI 0.41% to 0.51%) from 64,003 tests, respectively. Within 14 days of their selection, 2.94% of the adaptive cohort tested positive, compared to 1.27% of the random cohort. Close contacts who were predicted by the adaptive testing models received a COVID-19 test within an average of 0.94 days (95% CI 0.78 to 1.11) of the source testing positive, while those who were manually contact traced were tested in an average of 1.92 days (95% CI 1.81 to 2.02). These results suggest that machine learning strategies can improve surveillance testing effectiveness, especially in a university setting, by effectively distributing testing resources and potentially reducing community transmission.
Background Febrile neutropenia (FN) is an early indicator of infection in oncology patients post-chemotherapy. We aimed to determine clinical predictors of septic shock and/or bacteremia in pediatric cancer patients experiencing FN and to create a model that classifies patients as low-risk for these outcomes. Methods This is a retrospective analysis with clinical data of a cohort of pediatric oncology patients admitted during July 2015 to September 2017 with FN. One FN episode per patient was randomly selected. Statistical analyses include distribution analysis, hypothesis testing, and multivariate logistic regression to determine clinical feature association with outcomes. Results A total of 865 episodes of FN occurred in 429 subjects. In the 404 sampled episodes that were analyzed, 20.8% experienced outcomes of septic shock and/or bacteremia. Gram-negative bacteria count for 70% of bacteremias. Features with statistically significant influence in predicting these outcomes were hematological malignancy (P < .001), cancer relapse (P = .011), platelet count (P = .004), and age (P = .023). The multivariate logistic regression model achieves AUROC = 0.66 (95% CI 0.56–0.76). The optimal classification threshold achieves sensitivity = 0.96, specificity = 0.33, PPV = 0.40, and NPV = 0.95. Conclusions This model, based on simple clinical variables, can be used to identify patients at low-risk of septic shock and/or bacteremia. The model’s NPV of 95% satisfies the priority to avoid discharging patients at high-risk for adverse infection outcomes. The model will require further validation on a prospective population.
COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34–0.77%) from 20,862 tests, with 1.49% (95% CI 1.15–1.89%) of students testing positive within five days of the initial test—a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28–0.47%) with 0.67% (95% CI 0.55–0.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78–1.11) than those who were manually contact traced (1.92 days; 95% CI 1.81–2.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission.
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