Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.
OBJECTIVES Throughout the coronavirus disease 2019 (COVID-19) pandemic, masking has been a widely used mitigation practice in kindergarten through 12th grade (K–12) school districts to limit within-school transmission. Prior studies attempting to quantify the impact of masking have assessed total cases within schools; however, the metric that more optimally defines effectiveness of mitigation practices is within-school transmission, or secondary cases. We aimed to estimate the impact of various masking practices on secondary transmission in a cohort of K–12 schools. METHODS We performed a multi-state, prospective, observational, open cohort study from 7/26/2021 to 12/13/2021. Districts reported mitigation practices and weekly infection data. Districts that were able to perform contact tracing and adjudicate primary and secondary infections were eligible for inclusion. To estimate the impact of masking on secondary transmission, we used a quasi-Poisson regression model. RESULTS 1,112,899 students and 157,069 staff attended 61 K–12 districts across 9 states that met inclusion criteria. The districts reported 40,601 primary and 3,085 secondary infections. Six districts had optional masking policies, 9 had partial masking policies, and 46 had universal masking. Districts that optionally masked throughout the study period had 3.6 times the rate of secondary transmission as universally masked districts. For every 100 community-acquired cases, universally masked districts had 7.3 predicted secondary infections, while optionally masked districts had 26.4. CONCLUSIONS Secondary transmission across the cohort was modest (<10% of total infections) and universal masking was associated with reduced secondary transmission compared to optional masking.
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