Detection of hospital environmental contamination during SARS-CoV-2 Omicron predominance using a highly sensitive air sampling device

Abstract

Background and objectives: The high transmissibility of SARS-CoV-2 has exposed weaknesses in our infection control and detection measures, particularly in healthcare settings. Aerial sampling has evolved from passive impact filters to active sampling using negative pressure to expose culture substrate for virus detection. We evaluated the effectiveness of an active air sampling device as a potential surveillance system in detecting hospital pathogens, for augmenting containment measures to prevent nosocomial transmission, using SARS-CoV-2 as a surrogate.

Methods: We conducted air sampling in a hospital environment using the AerosolSense^TM air sampling device and compared it with surface swabs for their capacity to detect SARS-CoV-2.

Results: When combined with RT-qPCR detection, we found the device provided consistent SARS-CoV-2 detection, compared to surface sampling, in as little as 2 h of sampling time. The device also showed that it can identify minute quantities of SARS-CoV-2 in designated "clean areas" and through a N95 mask, indicating good surveillance capacity and sensitivity of the device in hospital settings.

Conclusion: Active air sampling was shown to be a sensitive surveillance system in healthcare settings. Findings from this study can also be applied in an organism agnostic manner for surveillance in the hospital, improving our ability to contain and prevent nosocomial outbreaks.

Keywords: Omicron; SARS-CoV-2; air-sampling; mass screening; surveillance.

Figure 1

Sampling plans (air and surface) and location in Hot, Warm, and Cold areas of the emergency department annex, negative pressure isolation ward, and open-cohort wards (C+ and C-).

Figure 2

Comparison of SARS-CoV-2 detection by sampling duration. (A) Comparison of RNA copy number in air samples collected from the same location following 2 or 14 h of sampling. Statistical significance was calculated using a two-sample t-test, n = 6. (B) Paired samples from individual locations with different sampling time. n = 1 for each paired location.

Figure 3

Positive detection of SARS-CoV-2 RNA copies from air and surface swab samples in C+ facilities. (A) Detection of SARS-CoV-2 RNA copies were found in 27 out of 28 (96.4%) air samples compared to 18 out of 32 (56.3%) surface swab samples. Positive detection was defined as being above the detection limit of the RT-qPCR performed on the collected samples, factoring in the dilution factor of the sample collection fluid. Detection limits: 40 RNA copies (air); 90 RNA copies (surface swab). (B) SARS-CoV-2 RNA copies in positive samples from C+ facilities (Hot and Warm areas) were comparable between air and surface swab samples. Statistical significance was calculated using a two-sample t-test.

Figure 4

Comparison of SARS-CoV-2 detection by sampling location and distance. (A) SARS-CoV-2 copy number grouped by location of exposure to SARS-CoV-2. Hot: areas with C+ patient traffic; Warm: areas in COVID-19 wards with no C+ patient traffic; Cold: areas not expected to have C+ patients. n = 32 (excludes masked samples). Groups were compared using non-parametric Kruskal-Wallis test. (B) Comparison of RNA copy number by distance from closest C+ patient for air samples. n = 30 (excludes masked samples and negative controls). Correlation analysis was conducted using Pearson's correlation.

Figure 5

Comparison of SARS-CoV-2 detection by the AerosolSense^TM sampler with or without mask. SARS-CoV-2 RNA was detected in both Hot and Warm locations with or without mask. n = 2 per group.