No abstract

Introduction: More than 93,000 cases of coronavirus disease have been reported worldwide. We describe the epidemiology, clinical course, and virologic characteristics of the first 12 U.S. patients with COVID-19. Methods:We collected demographic, exposure, and clinical information from 12 patients confirmed by CDC during January 20-February 5, 2020 to have COVID-19. Respiratory, stool, serum, and urine specimens were submitted for SARS-CoV-2 rRT-PCR testing, virus culture, and whole genome sequencing. Results:Among the 12 patients, median age was 53 years (range: 21-68); 8 were male, 10 had traveled to China, and two were contacts of patients in this series. Commonly reported signs and symptoms at illness onset were fever (n=7) and cough (n=8). Seven patients were hospitalized with radiographic evidence of pneumonia and demonstrated clinical or laboratory signs of worsening during the second week of illness. Three were treated with the investigational antiviral remdesivir. All patients had SARS-CoV-2 RNA detected in respiratory specimens, typically for 2-3 weeks after illness onset, with lowest rRT-PCR Ct values often detected in the first week. SARS-CoV-2 RNA was detected after reported symptom resolution in seven patients. SARS-CoV-2 was cultured from respiratory specimens, and SARS-CoV-2 RNA was detected in stool from 7/10 patients. Conclusions:In 12 patients with mild to moderately severe illness, SARS-CoV-2 RNA and viable virus were detected early, and prolonged RNA detection suggests the window for diagnosis is long. Hospitalized patients showed signs of worsening in the second week after illness onset.for use under a CC0 license.

, et al. (2020) Enhanced contact investigations for nine early travel-related cases of SARS-CoV-2 in the United States. PLoS ONE 15(9): e0238342.

Significance Language discordance has been shown to contribute to social disparities in healthcare. Contact tracing is essential to combating COVID-19, but language differences between contact tracers and patients have hindered its efficacy. We demonstrate a general method for leveraging machine learning and administrative data to maximize the impact of bilingual contact tracers and level language differences. We evaluate in a randomized controlled trial the impact of language matching on high-volume contact tracing in Santa Clara County, CA, and show that it reduces time spent and improves engagement with contact tracers. These results illustrate the advantages of utilizing bilingual personnel over third-party interpreters in improving social services.