Completeness of Race and Ethnicity Reporting in Person-Level COVID-19 Surveillance Data, 50 States, April 2020-December 2021.

Objectives: Black, Indigenous, and People of Color have borne a disproportionate incidence of COVID-19 cases in the United States. However, few studies have documented the completeness of race and ethnicity reporting in national COVID-19 surveillance data. The objective of this study was to describe the completeness of race and ethnicity ascertainment in person-level data received by the Centers for Disease Control and Prevention (CDC) through national COVID-19 case surveillance.

Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics.

Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision.