Investigating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Surface and Air Contamination in an Acute Healthcare Setting During the Peak of the Coronavirus Disease 2019 (COVID-19) Pandemic in London.

BACKGROUND: We evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface and air contamination during the coronavirus disease 2019 (COVID-19) pandemic in London. METHODS: Prospective, cross-sectional, observational study in a multisite London hospital. Air and surface samples were collected from 7 clinical areas occupied by patients with COVID-19 and a public area of the hospital. Three or four 1.0-m3 air samples were collected in each area using an active air sampler. Surface samples were collected by swabbing items in the immediate vicinity of each air sample. SARS-CoV-2 was detected using reverse-transcription quantitative polymerase chain reaction (PCR) and viral culture; the limit of detection for culturing SARS-CoV-2 from surfaces was determined. RESULTS: Viral RNA was detected on 114 of 218 (52.3%) surfaces and in 14 of 31 (38.7%) air samples, but no virus was cultured. Viral RNA was more likely to be found in areas immediately occupied by COVID-19 patients than in other areas (67 of 105 [63.8%] vs 29 of 64 [45.3%]; odds ratio, 0.5; 95% confidence interval, 0.2-0.9; P = .025, χ2 test). The high PCR cycle threshold value for all samples (>30) indicated that the virus would not be culturable. CONCLUSIONS: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from environmental contamination in managing COVID-19 and the need for effective use of personal protective equipment, physical distancing, and hand/surface hygiene.

Handheld Point-of-Care System for Rapid Detection of SARS-CoV-2 Extracted RNA in under 20 min.

The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis.

BACKGROUND: Understanding nosocomial acquisition, outbreaks, and transmission chains in real time will be fundamental to ensuring infection-prevention measures are effective in controlling coronavirus disease 2019 (COVID-19) in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions. METHODS: The study took place in a large teaching hospital group in London, United Kingdom. All patients tested for SARS-CoV-2 between 4 March and 14 April 2020 were included. Utilizing data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation and generated geotemporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users. RESULTS: Real-time surveillance reports revealed changing rates of HOCI throughout the course of the COVID-19 epidemic, key wards fueling probable transmission events, HOCIs overrepresented in particular specialties managing high-risk patients, the importance of integrating analysis of individual prior pathways, and the value of co-design in producing data visualization. Our surveillance system can effectively support national surveillance. CONCLUSIONS: Through early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterizing risk of transmission and targeting infection-control interventions.