ABSTRACT
OBJECTIVES: Experience of Nextstrain [1,2] and its approach adapted to the local context encouraged us to carry out real-time monitoring of COVID-19 nosocomial clusters in our establishment, the Grenoble Alpes University Hospital. PATIENTS AND METHODS, RESULTS: Through identification from electronic health records of nosocomial pathways and clusters and calculation of genetic distances from sequenced samples of COVID-19 patients, we were able to identify potential nosocomial clusters in very close to real time with a significant time saving compared to classical epidemiological surveillance, and to better understand and characterize nosocomial clusters. CONCLUSION: Through early detection and characterization of clusters, we may prevent infection of our patients by further implementing the appropriate measures.
Subject(s)
COVID-19 , Cross Infection , Humans , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2/genetics , Cross Infection/epidemiology , Hospitals, UniversityABSTRACT
PREDIMED, Clinical Data Warehouse of Grenoble Alps University Hospital, is currently participating in daily COVID-19 epidemic follow-up via spatial and chronological analysis of geographical maps. This monitoring is aimed for cluster detection and vulnerable population discovery. Our real-time geographical representations allow us to track the epidemic both inside and outside the hospital.
Subject(s)
COVID-19 , COVID-19/epidemiology , Data Warehousing , Geography , Hospitals, University , HumansABSTRACT
A 72-year-old immunocompromised man infected with severe acute respiratory syndrome coronavirus 2 received bamlanivimab monotherapy. Viral evolution was monitored in nasopharyngeal and blood samples by melting curve analysis of single-nucleotide polymorphisms and whole-genome sequencing. Rapid emergence of spike receptor binding domain mutations was found, associated with a compartmentalization of viral populations.