key: cord-0788620-wj7mgadt
authors: Cherrie, J.; Cherrie, M.; Davis, A.; Holmes, D.; Semple, S.; Steinle, S.; MacDonald, E.; Moore, G.; Loh, M.
title: Contamination of air and surfaces in workplaces with SARS-CoV-2 virus: a systematic review
date: 2021-01-26
journal: nan
DOI: 10.1101/2021.01.25.21250233
sha: a9117472fd0dfff9a33963783f5cb21df0b05922
doc_id: 788620
cord_uid: wj7mgadt

Objectives This systematic review aimed to evaluate the evidence for air and surface contamination of workplace environments with SARS-CoV-2 RNA and the quality of the methods used to identify actions necessary to improve the quality of the data. Methods We searched Web of Science and Google Scholar until 24th December 2020 for relevant articles and extracted data on methodology and results. Results The vast majority of data come from healthcare settings, with typically around 6 % of samples having detectable concentrations of SARS-CoV-2 RNA and almost none of the samples collected had viable virus. There were a wide variety of methods used to measure airborne virus, although surface sampling was generally undertaken using nylon flocked swabs. Overall, the quality of the measurements was poor. Only a small number of studies reported the airborne concentration of SARS-CoV-2 virus RNA, mostly just reporting the detectable concentration values without reference to the detection limit. Imputing the geometric mean air concentration assuming the limit of detection was the lowest reported value, suggests typical concentrations in health care settings may be around 0.01 SARS-CoV-2 virus RNA copies/m3. Data on surface virus loading per unit area were mostly unavailable. Conclusion The reliability of the reported data is uncertain. The methods used for measuring SARS-CoV-2 and other respiratory viruses in work environments should be standardised to facilitate more consistent interpretation of contamination and to help reliably estimate worker exposure.

A large-scale, global research effort has been directed at understanding the risks from infections and seeking successful clinical interventions to help patients. There have been almost 70,000 scientific papers published on the topic during the first 10 months of 2020, around 2.3% of all scientific publications during this period a . Despite all this new knowledge there has been little quantitative data on the extent of exposure to SARS-CoV-2 of workers in the healthcare sector, and much debate about the best strategies to protect them from infection 1 2 . The relative contribution of different transmission routes in terms of the risk of workplace infection continues to be poorly

Viruses may be transmitted from an infected patient to healthcare workers through a number of routes: by large droplets emitted from coughs or sneezes that may splatter directly on the worker's face; from fomite transmission where the worker contacts a surface contaminated by droplet emission and then transfers virus from the surface to their nose, mouth or eyes; and finally, from aerosol transmission where fine particles containing the virus are emitted from the respiratory system of the patient, become airborne for a period and may then be inhaled by the worker. The relative importance of these three routes in determining the risk of infection is poorly understood for SARS-CoV-2 3 .

In the early stages of the pandemic, the World Health Organisation (WHO) was clear that "SARS-CoV-2 transmission appears to mainly be spread via droplets and close contact with infected symptomatic cases" and in most circumstances aerosol transmission was considered unlikely 4 . However, as knowledge of the virus has increased it has become apparent that aerosol transmission may be more important than was previously thought and some have argued that it is a major source of infection 5 .

a Based on 2,769,367 papers listed in WoS for 2020, and 62,478 of these with Covid or SARS-CoV-2 in any data field.

. CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01. 25.21250233 doi: medRxiv preprint The situation is further complicated because our understanding of the extent of SARS-CoV-2 air and surface contamination in hospitals and other workplaces is limited. There are only around 0.06% of all the Covid-19 related research papers that describe measurements of environmental contamination b , and these data tend not to have been appropriately summarised. Without an evidence base to understand how exposure or transmission takes place it is difficult to set out rational plans to control SARS-CoV-2 in the workplace. For example, this has resulted in heated policy debates about whether it is necessary to wear effective respiratory protection when there are no deliberate aerosol generating procedures on Covid-19 patients. It is also likely that the relative importance of different transmission routes will vary depending on the workplace, the tasks being performed and the interaction with an infective source.

The aim of this review is to summarise the reported SARS-CoV-2 RNA air and surface contamination concentrations in workplace settings where the virus is present, particularly considering the quality of the methods used, to draw lessons for future methodological developments.

We searched Web of Science (WoS) using the terms in the title ((SARS-CoV-2 or "severe acute respiratory syndrome") and air), and ((SARS-CoV-2 or "severe acute respiratory syndrome") and surface), for all languages and all document types. In addition, we searched the Google Scholar database for the above search terms, excluding the phrase "severe acute respiratory syndrome" to restrict the hits to a manageable number. The references were combined into a single database and duplicate entries were removed. The entries were then screened by a single researcher (Cherrie, JW) on the basis of title and abstract to identify informative papers containing data on either air or surface concentrations of SARS-CoV-2 RNA in workplaces, including papers that reported their results as either b Based on the papers reviewed here related to the 62,478 papers in WoS with Covid or SARS-CoV-2 in any data field. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint positive or negative contamination without quantifying the extent of the contamination; papers not in the English language were excluded. Data on other measures, for example virus RNA in exhaled breath condensate, were excluded. Following the initial literature search we set up a Google Scholar alert using the same search terms as used initially. These produced periodic updates that were screened in the same way as the original citations and relevant publications were added to the final list. These periodic updates were included up to the 24 th December 2020. Copies of all papers were obtained, and data extracted into tables for summarisation. Numeric data extraction was checked by a second researcher (Steinle).

Data were summarised graphically using the DataGraph software. For datasets with more than one detectable result in a dataset of 10 or more measurements we used the elnormCensored function in in the R-package EnvStats v2.3.1 to estimate the geometric mean and associated 95% confidence intervals using the maximum likelihood method.

The initial WoS searches identified 44 papers relating to airborne contamination and 42 on surface contamination, some of which were included in both lists. Google Scholar produced a greater number of references: 137 on air contamination and 80 relating to surface contamination ( Figure 1 ).

After the removal of duplicates there were 182, which resulted in 26 informative papers for inclusion in the review. A further thirteen papers were added from the ongoing literature searches or other sources and on further reading four papers were excluded: one duplicated data in another identified paper, one related to non-occupational exposure, one was not related to Covid-19 infection risks and the last was written in Persian. In the end, 35 papers were reviewed: three were available as preprints and the remainder as peer-reviewed publications (Table 1) . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint Fifteen of the papers were from studies undertaken in China (14 from the mainland and one from Hong Kong), nine from Europe (two from UK, four from Italy, two from Spain and one from Greece), six from North America (five from USA and one from Canada), and five from Asia (two from Singapore, two from Iran and one from Korea). All but three of the studies were carried out in hospitals, mostly in intensive care settings or isolation wards with Covid-19 patients (75% of the healthcare studies).

The three non-healthcare papers describe measurements made on public transportation (buses in Northern Italy 6 and buses and subway trains in Spain 7 ) and various workplaces in Greece (a ferryboat and a nursing home -this paper also included data for three COVID-19 isolation hospital wards and a long-term care facility where 30 asymptomatic COVID-19 cases were located 8 ). Most of the studies (77%) aimed to describe the contamination present in the setting investigated and the remainder aimed to investigate the extent of contamination in relation to patient viral load or some other patient-related factors.

There are no standardised methods used for quantification of concentrations of SARS-CoV-2 RNA in the air, and as a consequence there were many different methods used. Twenty-five of the studies involved collection of air samples: nine used gelatin filters to collect the sample, eight used wet cyclone samplers, five used impingers, six used dry filters such as PTFE, and two used a water-based condensational growth sampler; some studies used a combination of the techniques (Table 1) . Only one study used a personal sampling methodology 9 . The remainder mostly used various combinations of area sampling close to patients (13 studies), in the background near patients (11 studies) or sampling in other areas (12 studies). The volume of air sampled using these methods varied considerably, from 0.09 m 3 for a midget impinger operated for one hour, to 16 m 3 for a wet cyclone operating at 400 l/min for 40 minutes. Most samples were collected over a relatively short time, typically less than one hour, and flowrates varied from 1.5 to 400 l/min. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01. 25.21250233 doi: medRxiv preprint In contrast with the air sampling, there was greater consistency in the surface sampling methodologies used across the studies. There is a method published by the WHO 10 that recommends samples be collected using a swab with a synthetic tip and a plastic shaft pre-moistened with viral transport medium (VTM). It is recommended that an area of 25 cm 2 is swabbed, but no recommendations were made concerning the reporting of results as SARS-CoV-2 RNA per cm 2 . Twenty-nine papers contained data from surface sampling: 12 followed the general approach set out by the WHO and 13 used an alternative pre-moistened swab but with, for example, water, saline or phosphate buffer solution in place of VTM. The remaining studies either used dry swabs that were then transported in VTM 11 12,7 or did not clearly specify the sampling approach used 13 . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint mention of sampling quality assurance procedures and 15 (44%) mentioned some details of analytical quality control.

Twenty-eight studies had contamination data from surfaces (between 5 and 1,252 swab samples) with between zero and 74% positive (median 6%) for SARS-CoV-2 RNA. Twenty-five studies reported air sampling data for between two and 135 samples; the proportion of samples that were positive ranged from zero to 100% with the median across all studies of 6.6% positive samples. These data are summarised in Figure 2 . There were six studies that did not detect SARS-CoV-2 RNA on any air samples and five that did not detect SARS-CoV-2 on surfaces; there are no obvious differentiation between these studies and the others reported here. Four studies where less than ten air samples were collected tended to show a high proportion of positive samples (40% -100%) and it may be that these data are not representative of general conditions in the sampled environments.

Twenty of the studies had data for both surface and air contamination and these data are summarised is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint samples obtained between 19 February and 2 March 2020, although the data were only reported as average concentrations for 16 specific locations). They used a wetted wall cyclone that collected air samples at 300 l/min over 30-minute periods. The reported concentrations from three locations in the ICU were between 520 and 3,800 copies/m 3 . However, only 4 of the 26 samples had detectable concentrations and the researchers do not describe how they treated the non-detects when taking the average and is possible that they inappropriately assumed they were zero. At the remaining sampling locations, the results were all below the detection limit, which was unspecified. Only two studies reported viral loading on surfaces in terms of RNA copies per unit area swabbed 20,7 ; where copies were reported otherwise they were expressed per sample collected. Ma and is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. 

The studies reviewed here were mostly descriptive and lacked a clear aim other than documenting air or surface contamination. This is perhaps unsurprising given the context of the pandemic and the need to better understand the likely routes of transmission. However, the air sampling methods employed differed greatly between studies, perhaps reflecting local availability of equipment and skills in environmental sampling and previous experience in detecting other airborne viruses, e.g. influenza.

Some used methods developed for first SARS outbreak while others used methods adapted for sampling of microbiological exposures, although most air samples were obtained using high volume flowrates over relatively short durations. Almost all of the air samplers had poorly characterised aerosol aspiration efficiencies, i.e. the aerosol size range effectively collected, and cyclone devices likely only effectively sample aerosols with aerodynamic diameter more than around 1µm, e.g. for the WA-400 air sampler Hu et al 13 quotes a 50% aerodynamic equivalent cut-off diameter of 0.8 μm.

However, Liu et al 21 , who collected three samples using a miniature cascade impactor, were able to show the potential for up to half of the SARS-CoV-2 RNA being mainly associated with aerosol with aerodynamic diameter between 0.25 and 1 μm and larger than 2.5 μm. It is therefore possible that many of the studies underestimate the airborne virus RNA concentrations. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. Despite all these limitations, the available data suggests that higher levels of detectable air contamination is associated with higher surface contamination. The most likely explanation for this is that the main source of surface contamination is fine aerosol rather than droplet spray or transfer from the hands of workers or patients. In most healthcare settings the measured airborne concentrations of SARS-CoV-2 virus RNA were low, with likely geometric mean levels around 0.01 RNA copies/m 3 , and the same is undoubtedly the case for surface contamination. The highest concentrations measured in healthcare settings were in excess of 10,000 RNA copies/m 3 air and around 170,000 RNA copies/cm 2 surface. Data from public transport settings are limited and there are no data on environmental contamination from other higher risk workplaces such as personal service occupations, factory workers and other non-medical essential workers 24 . Of course, detection of RNA does not mean that there was viable virus present, and in almost all cases the concentration in samples was too low to successfully culture virus. In the one study that successfully cultured virus from four is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint

We are grateful to a number of our colleagues for comments on early drafts of this paper.

We have no competing interests to declare. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint Note, the black squares represent the imputed geometric mean and the horizontal lines the upper and lower 95% confidence intervals on the geometric mean. The small vertical lines are the lowest reported measurement, which was assumed to be the LoD. T = study in a transportation setting.

. CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted January 26, 2021. ; https://doi.org/10.1101/2021.01.25.21250233 doi: medRxiv preprint

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Aim Codes: D = descriptive

A = analytic Air sampling method: CG = water-based condensational growth

WC = wetted cyclone Air sampling strategy: AP = area sample near patient

AB = area sample in the background

U = uncertain Surface sampling codes: WHO = WHO method with wetted swab

OW = other wetted swab; Dry = dry swab; Not Spec -not specified