Detection of SARS-CoV-2 RNA in commercial passenger aircraft and cruise ship wastewater: a surveillance tool for assessing the presence of COVID-19 infected travellers

Warish Ahmed; Paul M Bertsch; Nicola Angel; Kyle Bibby; Aaron Bivins; Leanne Dierens; Janette Edson; John Ehret; Pradip Gyawali; Kerry A Hamilton; Ian Hosegood; Philip Hugenholtz; Guangming Jiang; Masaaki Kitajima; Homa T Sichani; Jiahua Shi; Katja M Shimko; Stuart L Simpson; Wendy J M Smith; Erin M Symonds; Kevin V Thomas; Rory Verhagen; Julian Zaugg; Jochen F Mueller

doi:10.1093/jtm/taaa116

. 2020 Jul 14;27(5):taaa116. doi: 10.1093/jtm/taaa116

Detection of SARS-CoV-2 RNA in commercial passenger aircraft and cruise ship wastewater: a surveillance tool for assessing the presence of COVID-19 infected travellers

Warish Ahmed ^1,^✉, Paul M Bertsch ¹, Nicola Angel ², Kyle Bibby ³, Aaron Bivins ³, Leanne Dierens ², Janette Edson ², John Ehret ⁴, Pradip Gyawali ⁵, Kerry A Hamilton ⁶, Ian Hosegood ⁴, Philip Hugenholtz ², Guangming Jiang ⁷, Masaaki Kitajima ⁸, Homa T Sichani ⁹, Jiahua Shi ⁷, Katja M Shimko ⁹, Stuart L Simpson ¹⁰, Wendy J M Smith ¹¹, Erin M Symonds ¹², Kevin V Thomas ⁹, Rory Verhagen ⁹, Julian Zaugg ², Jochen F Mueller ⁹

PMCID: PMC7454825 PMID: 32662867

Abstract

Background

Wastewater-based epidemiology (WBE) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be an important source of information for coronavirus disease 2019 (COVID-19) management during and after the pandemic. Currently, governments and transportation industries around the world are developing strategies to minimize SARS-CoV-2 transmission associated with resuming activity. This study investigated the possible use of SARS-CoV-2 RNA wastewater surveillance from airline and cruise ship sanitation systems and its potential use as a COVID-19 public health management tool.

Methods

Aircraft and cruise ship wastewater samples (n = 21) were tested for SARS-CoV-2 using two virus concentration methods, adsorption–extraction by electronegative membrane (n = 13) and ultrafiltration by Amicon (n = 8), and five assays using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and RT-droplet digital PCR (RT-ddPCR). Representative qPCR amplicons from positive samples were sequenced to confirm assay specificity.

Results

SARS-CoV-2 RNA was detected in samples from both aircraft and cruise ship wastewater; however concentrations were near the assay limit of detection. The analysis of multiple replicate samples and use of multiple RT-qPCR and/or RT-ddPCR assays increased detection sensitivity and minimized false-negative results. Representative qPCR amplicons were confirmed for the correct PCR product by sequencing. However, differences in sensitivity were observed among molecular assays and concentration methods.

Conclusions

The study indicates that surveillance of wastewater from large transport vessels with their own sanitation systems has potential as a complementary data source to prioritize clinical testing and contact tracing among disembarking passengers. Importantly, sampling methods and molecular assays must be further optimized to maximize detection sensitivity. The potential for false negatives by both wastewater testing and clinical swab testing suggests that the two strategies could be employed together to maximize the probability of detecting SARS-CoV-2 infections amongst passengers.

Keywords: SARS-CoV-2, COVID-19, WBE, wastewater, enveloped viruses, cruise ship, aircraft

Introduction

The ongoing pandemic of severe pneumonia and other associated health effects known as coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in >10 million diagnosed cases of COVID-19 and >500 000 deaths globally to date.¹^,² In response, governments throughout the world have implemented stringent measures, including complete lockdowns, border closures and social distancing to suppress transmission of the virus.³ However, these measures are having tremendous negative impacts on local and global economies.⁴^,⁵ Particularly impacted industries include commercial air travel and the cruise liner industry, which have been forced to reduce or cease operation when virus transmission restrictions have been put in place. Cruise ships and aircraft present a confined environment for transmission of infections from human-to-human and numerous outbreaks including SARS-CoV-2 have been reported.^35–37

The International Air Transport Association estimates that international air travel curtailment and restrictions will result in a USD $113 billion loss across the industry (https://www.iata.org/en/iata-repository/publications/economic-reports/third-impact-assessment/). As social distancing measures are being implemented throughout the world to reduce COVID-19 outbreaks, governments and industries are now developing plans for a COVID-19-safe society.⁶ However, as normal operations resume, passengers of air travel and cruise line travel could play a significant role in importing new COVID-19 cases, with several epidemics of COVID-19 observed on cruise ships during the first wave of the pandemic.⁷ The transport industry would benefit from objective matrices for monitoring the potential risk of SARS-CoV-2 transmission associated with their operations.⁸

One potential approach is afforded by the observation that SARS-CoV-2 infection is frequently accompanied by prolonged shedding of viral RNA in the stool and naso-oral fluid of both symptomatic and asymptomatic individuals.⁹^,¹⁰ SARS-CoV-2 RNA has been successfully detected in municipal wastewaters during clinically documented outbreaks of COVID-19 throughout the world.^11–14 Wastewater-based epidemiology (WBE) could provide useful information on COVID-19 infection status and trends in the community that informs risk management decisions.¹⁵^,¹⁶ For example, WBE could be used as an early warning tool to monitor the appearance and resurgence of COVID-19, because it allows for the detection of viral RNA derived from mild, subclinical or even asymptomatic infections. In countries monitoring wastewater for SARS-CoV-2 RNA, the viral signal was detected in wastewater days to weeks before the first clinically confirmed case.¹³^,¹⁷^,¹⁸

During both air travel and cruises, passengers are provided with onboard sanitation facilities. Monitoring the wastewater from these facilities for SARS-CoV-2 RNA could provide public health officials with an additional means of assessing the presence or absence of SARS-CoV-2 infections among the passengers since at least one COVID-19 patient has been observed to be positive by faecal specimen despite being negative by pharyngeal and sputum samples.³⁸ Wastewater-based COVID-19 surveillance could be a cost-effective method for screening of a large proportion of the passenger population to inform and prioritize clinical testing of nasopharyngeal samples. Furthermore, the duration of faecal shedding is longer, and therefore the probability of detecting SARS-CoV-2 RNA in wastewater containing faeces may be greater than clinical screening. However, little has been reported on the presence of SARS-CoV-2 RNA in wastewater from precisely bounded environments, such as aircraft, cruise ships, prisons, aged care facilities and remote vulnerable communities. Establishing the feasibility, performance specifications and limitations of testing wastewater originating from aircraft and cruise ships is critical to rationally leverage WBE within the existing public health framework.

The aim of this study was to investigate whether SARS-CoV-2 RNA can be detected and quantified in wastewater collected from inbound commercial passenger aircraft and a cruise ship docked in Australia, thereby enhancing our understanding of how SARS-CoV-2 monitoring in wastewater can be included in COVID-19 safe society plans, particularly those related to the transportation industry. Implementation of WBE on aircraft and cruise ships could facilitate the resumption of travel via these modes of transport with appropriate precautions for the ongoing COVID-19 pandemic.

Materials and Methods

Wastewater sampling

Two wastewater grab samples (1 L) were collected from the influent and effluent of the membrane bioreactor of a cruise ship on 23 April 2020. Sample collection occurred over a month after passenger disembarcation with only crew on board on the ship on its last day berthed in Australia. Unconfirmed reports suggested as many as 24 infected persons may have been on board in the days prior to sample collection. A total of three aircraft wastewater samples (1 L each) were collected. These were collected from a valve at the bottom of the vacuum-truck that collects the wastewater tanks of the aircraft immediately after landing. The tanks of the aircraft and the vacuum trucks were emptied but not cleaned between flights. Wastewater grab sample (1 L each) were collected from passenger aircraft flight (i) Los Angeles–Brisbane (arr. 26/04/20; 117 passengers plus crew duration 13 h and 52 min), (ii) Hong Kong–Brisbane (arr. 07/05/20; 19 passengers plus crew duration 8 h and 10 min) and (3) New Delhi–Sydney (arr. 10/05/20; 185 passengers plus crew duration 11 h and 23 min). Standard personal protective equipment was used during sample collection. Samples were transported on ice to the laboratory and stored at 4°C and processed within 6–24 h after collection.

Sample concentration and RNA extraction

A total of 21 replicate samples (volume ranging from 50 to 200 ml) were aliquoted from the five wastewater samples (Table 2). Viruses were concentrated from these wastewater samples (50–200 ml) using two previously published virus concentration methods¹⁹^,²⁰: (i) adsorption–extraction with electronegative membrane and (ii) ultrafiltration with Amicon® Ultra-15 centrifugal filter unit (30 kDa). In total, 13 samples (seven from aircraft and six from cruise ship) were concentrated using the adsorption–extraction method and the remaining eight samples (three from aircraft and five from cruise ship) were concentrated using Amicon® Ultra-15 centrifugal filter (Merck Millipore Ltd).

Table 2.

Detection of SARS-CoV-2 RNA in wastewater samples collected from three aircraft and a cruise ship

Sources (sampling dates)	Virus concentration methods used	Sample ID (volume concentrated)	RT-qPCR positive results (Cq)					RT-ddPCR results
Sources (sampling dates)	Virus concentration methods used	Sample ID (volume concentrated)	CDC N1	CDC N2	N_Sarbeco	E_Sarbeco	NIID_2019-nCoV N	CDC N1
Aircraft 1 Los Angeles–Brisbane (26 April 2020)	Adsorption–extraction	A1a-1 (100 ml)	–	–	–	+(36.3)	–	–
	Adsorption–extraction	A1a-2 (100 ml)	–	–	+(38.7)	––	–	–
	Amicon® Ultra-15	A1b-1 (50 ml)	–	–	–	+(37.7)	–	–
		A1b-2 (50 ml)	–	–	–	–	–	–
		A1b-3 (50 ml)	–	–	–	+(39.0)	–	–
Aircraft 2 Hong Kong–Brisbane (07 May 2020)	Adsorption–extraction	A2a-1 (100 ml)	–	–	–	–	–	–
		A2a-2 (100 ml)	–	–	–	–	–	–
		A2a-3 (100 ml)	–	–	–	–	–	–
Aircraft 3 New Delhi–Sydney (10 May 2020)	Adsorption–extraction	A3a-1 (100 ml)	–	–	–	–	–	NT
Aircraft 3 New Delhi–Sydney (10 May 2020)	Adsorption–extraction	A3a-2 (100 ml)	–	–	–	–	–	NT
Cruise ship-influent (23 April 2020)	Adsorption–extraction	CSI1a-1 (100 ml)	+(33.6) +(33.2) +(33.4)	+(35.8) +(37.6)	–	–	–	+
		CSI1a-2 (100 ml)	+(35.0) +(34.5) +(34.4)	+(38.6) +(39.0) +(41.3)	–	–	+(42.1)	+
		CSI1a-3 (100 ml)	+ (32.5) +(33.1) +(32.1)	+(38.2) +(37.9) +(37.9)	–	+(37.3) +(37.3)	+(36.9) +(37.0) +(35.1)	+
	Amicon® Ultra-15	CSI1b-1 (50 ml)	+(36.1)	–	–	–	+(35.7)	+
		CSI1b-2 (50 ml)	+(36.6)	–	–	–	+(36.3)	–
		CSI1b-3 (50 ml)	+(36.8)	+(38.2) +(38.4)	–	–	–	–
Cruise ship-effluent (23 April 2020)	Adsorption–extraction	CSE1a-1 (200 ml)	–	–	–	+(38.7)	–	–
		CSE1a-2 (200 ml)	–	–	–	–	–	+
		CSE1a-3 (200 ml)	–	–	–	+(37.8)	+(36.0)	–
	Amicon® Ultra-15	CSE1b-1 (100 ml)	–	–	–	–	–	+
	Amicon® Ultra-15	CSE1b-2 (100 ml)	–	–	–	–	–	–

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–, not detected; +, positive; NT, not tested due to unavailability of reagents.

RNA was directly extracted from the electronegative membrane using a combination of two kits (RNeasy PowerWater Kit and RNeasy PowerMicrobiome Kit; Qiagen, Hilden, Germany). Briefly, a 5-ml bead tube from the RNeasy PowerWater Kit was used to accommodate the electronegative membrane followed by adding 990 μl of buffer PM1 and 10 μl of β-mercaptoethanol (Sigma-Aldrich, Australia). A tissue homogenizer (Precellys Evolution 24, Bertin Technologies, France) was used to homogenize the samples, in which homogenization occurred for 3 × 20 s cycles at 10 000 rpm with a 10 s pause between cycles. After homegenization, tubes were further centrifuged at 10 000 g for 5 min to pellet the filter debris and beads. RNA was extracted from 450 μl of lysate using the RNeasy PowerMicrobiome and the QIAcube Connect platform (Qiagen) to obtain a final RNA extract volume of 100 μl.

The ultrafiltration method began with the centrifugation of the sample at 4500 g for 10 min at 4°C to remove debris and larger particles from the sample. The resulting supernatant was concentrated using an Amicon® Ultra-15 centrifugal filter, which was centrifuged at 4750 g for 10 min at 4°C. This centrifugal concentration step was repeated multiple times to pass the entire supernatant volume through the filter.¹⁹^,²¹^,²² The concentrated sample (200–300 μl) was collected from the sample reservoir with a pipette and transferred to a 2 ml-bead beating tube followed by adding 650 μl of PM1 and 6.5 μl of β-mercaptoethanol. The tissue homogenizer was used to homogenize the samples (5 ml and 2 ml bead beating tubes) and RNA was extracted as described above. All RNA samples were stored at −80°C and subjected to reverse-transcription quantitative polymerase chain reaction (RT-qPCR) analysis within the 1–3 days of RNA extraction to avoid losses associated with storing, as well as freezing and thawing RNA preparations.

RT-qPCR analysis

Recently published RT-qPCR assays that target different regions of the SARS-CoV-2 genome, specifically N and E genes (CDC N1, CDC N2, N_Sarbeco, NIID_2019-nCOV_N and E_Sarbeco), were used for SARS-CoV-2 RNA detection in wastewater samples.^23–25

The primers and probes sequences, along with qPCR cycling parameters, are shown in Supplementary Table S1. For RT-qPCR assays, double-stranded DNA gene fragment containing the assay target (gBlocks gene fragments) and 2019-nCoV_N plasmid control (Catalogue No. 10006625) were purchased from the Integrated DNA Technologies (Coralville, IA, USA) and used to generate the standard curves (copy/μl). CDC N1 and N2 standard dilutions ranged from 1 × 10⁵ to 1 copy/μl. N_Sarbeco, NIID_2019-nCOV_N, and E_Sarbeco standard dilutions, also ranging from 1 × 10⁵ to 1 copy/μl, were prepared from the gBlocks gene fragments as per the manufacturer’s instructions. All RT-qPCR amplifications were performed in 20 μl reaction mixtures using iTaq™ Universal Probes One-Step Reaction Mix (Bio-Rad Laboratories, Richmond, CA, USA).

Each CDC N1 and N2 RT-qPCR mixture contained 10 μl of Supermix, 2019-nCoV Kit (500 nM of forward primer, 500 nM of reverse primer and 125 nM of probe) (Catalogue No. 10006606), 50 ng/μl of bovine serum albumin (BSA), 0.4 μl of iScript reverse transcriptase and 3 μl of template RNA. N_Sarbeco RT-qPCR mixtures contained 10 μl of Supermix, 600 nM of forward primer, 800 nM of reverse primer, 200 nM of probe, 1 μg of BSA, 0.4 μl of iScript reverse transcriptase and 3 μl of template RNA. NIID_2019-nCOV_N RT-qPCR mixtures contained 10 μl of Supermix, 500 nM of forward primer, 700 nM of reverse primer R2, 700 nM of reverse primer R2-Ver3, 200 nM of probe, 50 ng/μl of BSA, 0.4 μl of iScript reverse transcriptase and 3 μl of template RNA. E_Sarbeco RT-qPCR mixtures contained 10 μl of Supermix, 400 nM of forward primer, 400 nM of reverse primer, 200 nM of probe, 50 ng/μl of BSA, 0.4 μl of iScript reverse transcriptase and 3 μl of template RNA. The RT-qPCR assays were performed using a Bio-Rad CFX96 thermal cycler (Bio-Rad Laboratories). All RT-qPCR reactions were performed in triplicate. For each RT-qPCR run, a series of three positive and no template controls were included.

All RT-qPCR data were generated using default settings for baseline and threshold. Data were only collected from instrument runs in which the positive control was positive and the no template control was negative. All instrument runs passed these criteria. A master standard curve with 95% upper and lower confidence intervals was generated for each assay. The log₁₀-linear regression of copy number and corresponding quantification cycle (Cq) values (derived from the 6-point, assay gBlock 1:10 serial dilution series) measured in triplicate from three qPCR instrument runs were used to generate the master standard curve and 95% confidence intervals.

For each sample replicate, the SARS-CoV-2 RNA concentration (copies/reaction), with the 95% confidence intervals, was calculated from the master standard curve and accounts for the difference in nucleic acid type between the double-stranded oligonucleotide used to generate the standard curve and the single-stranded genome of SARS-CoV-2 (i.e. divide by two).²⁶ For each assay in this study, the assay limit of detection (ALOD) defined as the minimum copy number/μL of template with a 95% probability of detection, was determined as previously described²⁷ and also takes into account the difference in nucleic acid type between the standard curve material and the SARS-CoV-2 genome. The sample limit of detection (SLOD) was calculated by multiplying the ALOD by the total volume of RNA extracted from each sample to yield the total RNA gene copies that could be detected with 95% probability. This number was then normalized to total sample volume processed to yield the SLOD of SARS-CoV RNA/100 ml.

RT-ddPCR analysis

CDC N1 RT-droplet digital PCR (RT-ddPCR) mixture contained 5 μl of One-Step RT-Supermix 900 nM of forward primer, 900 nM of reverse primer and 250 nM of probe, 2 μl of reverse transcriptase, 1 μl of 300 mM DTT and 2 μl of template RNA in a final volume of 22 μl. The reaction mixture and 70 μl droplet generation oil were used to form droplets using an automated droplet generator. About 40 μl of droplet-partitioned samples were then transferred to a 96-well plate, sealed and placed on a Bio-Rad C1000 Touch Thermal Cycler (ramping speed 2.5°C/s) using the following conditions: 45°C for 60 min, followed by 40 cycles of 95°C for 10 s, 95°C for 30 s and 55°C for 1 min and 98°C for 10 min. The plate was then transferred to a QX 200 droplet reader (Bio-Rad) for automatic measurement of fluorescence in each droplet. For each RT-ddPCR run, a series of three positive and no template controls were included. All samples were run in triplicate.

qPCR inhibition and quality control

An experiment was conducted to determine the presence of qPCR inhibition in RNA extracted from wastewater samples using a Sketa22 real-time PCR assay.²⁸ A known copy (10⁴/reaction) of Oncorhynchus keta (O. keta) was added in the DNase- and RNase-free water and the Cq value obtained acted as a reference point. If the Cq value of a wastewater sample increases compared with the reference Cq value, the sample is considered to have PCR inhibitors. Wastewater samples with a 2-Cq delay was considered to have qPCR inhibition.^11,29

With respect to quality control, a reagent blank and extraction blank were included for each batch of RNA extraction to ensure no carryover contamination occurred during RNA extraction. No carryover contamination was observed in reagent blank samples. To minimize potential contamination, RNA extraction and RT-qPCR setup were performed in separate laboratories.

Sequencing and bioinformatics

For NextSeq Illumina sequencing, representative RT-qPCR products were cleaned with 1× ratio of AmpureXP (BeckmanCoulter, USA) and eluted in 15 μl of DNase- and RNase-free water. Amplicons were prepared for sequencing using the NEB UltraII Total RNA kit (New England Biolabs, USA) according to the manufacturer’s protocol but modified to begin at the end repair step. PCR indexing of libraries PCR was undertaken using the NEBNExt Multiplex Oligos Unique Dual indices for Illumina using 10 cycles of PCR. Samples were pooled in equimolar amounts for sequencing and sequenced as a 150-bp paired end run using a 300 cycle v2 NextSeq kit (Illumina, USA).

Primer sequences were removed from de-multiplexed reads using cutadapt (ver. 2.9), with reads not containing primers discarded (−discard-untrimmed). Poor quality reads were identified and removed with trimmomatic (ver. 0.39) using a sliding window of four bases with an average quality of 15 (SLIDINGWINDOW:4:15). Reads were cropped to 140 bp (CROP:140), with any <100 bp in length discarded (MINLEN:100). Overlapping forward and reverse reads were merged using bbmerge from the BBMap suite (ver. 38.71, https://sourceforge.net/projects/bbmap/;mininsert = 100). Quality-controlled, merged reads were then mapped to the reference genome (GenBank accession number MT276598.1) using CoverM ‘make’ (ver 0.4.0, B. Woodcroft, unpublished, https://github.com/wwood/CoverM). Low quality read mappings were removed with CoverM ‘filter’ (minimum identity 95% and minimum aligned length of 90%). Read depth profiles for each sample were calculated using samtools (ver. 1.9).

Effects of aircraft wastewater tank disinfectant

The effect of aircraft toilet deodorant and viricidal/bactericidal (Novirusac Gel Bulk, Aero Defence Pty. Ltd, Southport, Queensland, Australia), which is typically dosed into the tank of an aircraft before departure, on coronavirus (i.e. murine hepatitis virus) stability was assessed. Novirusac Gel Bulk comprised of hexylene glycol, benzalkonium chloride, didecylmethylammonium propionate ethhoxylated, N-(3-aminopropyl)-N-dodecyl-1,3-propanediamine, ethanolamine and water. Briefly, 100 μl of Novirusac Gel Bulk was mixed with 100 μl of untreated wastewater. Murine hepatitis virus (10 μl) was seeded into the mixture in triplicates. Before, seeding the Cq value of the MHV RNA was determined using RT-qPCR. Two sets of samples were incubated at 15°C (typical temperature of wastewater in an aircraft) for 48 h. RNA was extracted from the incubated samples after 24 (set 1) and 48 h (set 2). The Cq values obtained for 24-hour and 48-hour incubated samples were compared with the Cq value obtained for the seeded murine hepatitis virus stock to determine the shift in Cq values over the incubation period. RNA extraction and RT-qPCR of MHV was performed according to a recent study.²⁰

Ethics approval

Low-risk approval as defined by the National Statement on Ethical Conduct in Human Research was obtained from CSIRO Ethics Committee (reference number 2020_031_LR).

Results

PCR inhibition, performance characteristics of RT-qPCR assays and ALOD

All RNA samples were free from PCR inhibition as determined by Sketa22 qPCR, and therefore used for downstream SARS-CoV-2 RT-qPCR analysis. The amplification efficiencies of CDC N1, CDC N2 and NIID_2019-nCOV_N assays were within the prescribed range (90–110%) of MIQE guidelines.²⁶ However, the amplification efficiencies of N_Sarbeco (116%) and E_Sarbeco (89.6%) were slightly outside the recommended range. The correlation coefficient (R²) values for all assays were between 0.996 and 0.998. The slope of the standard curves, Y-intercepts, ALOD and SLOD values are shown in Table 1.

Table 1.

RT-qPCR performance characteristics and assay limit of detection (ALOD) and sample limit of detection (SLOD)

Assay	Performance characteristic (range)
	Efficiency (E) (%)	Linearity (R²)	Slope	Y-intercept	ALOD for SARS-CoV-2 RNA (copies/μl RNA Template)	SLOD for SARS-CoV-2 RNA (copies/100 ml)
CDC N1	98.6–106	0.98–0.99	−3.197 to −3.357	36.60–37.63	1	100
CDC N2	94.6–103	0.99–0.99	−3.247 to −3.458	36.69–38.32	2	200
N_Sarbeco	96.9–116	0.97–0.99	−3.129 to −3.399	39.80–40.25	3	300
NIID_2019-nCOV_N	90.9–104	0.99–0.99	−3.226 to −3.562	37.34–38.71	4	400
E_Sarbeco	89.6–96.5	0.97–0.98	−3.412 to −3.417	39.99–40.44	2	200

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SARS-CoV-2 RNA in wastewater samples

Of the five replicate wastewater samples collected from Aircraft 1 that were processed using both virus concentration methods, four samples yielded a positive signal for SARS-CoV-2 RNA using two different assays (N_Sarbeco and E_Sarbeco) (Table 2). The positive ratio (i.e. 3 of 5) of E_Sarbeco in Aircraft 1 replicate wastewater samples was greater than N_Sarbeco (i.e. 1 of 5). The RT-qPCR amplifications were not consistent for all RT-qPCR replicates; Cq values of the positive samples ranged from 36.3 to 39.0 (E_Sarbeco assay, Table 2). Samples from both adsorption–extraction with an electronegative membrane and ultrafiltration with an Amicon® Ultra-15 centrifugal filter unit recovered SARS-CoV-2 RNA from aircraft wastewater. CDC N1, CDC N2 and NIID_2019-nCoV N assays did not produce any amplification for these samples in two consecutive RT-qPCR runs. All three replicate wastewater samples from Aircraft 2 and two replicate wastewater samples from Aircraft 3 using adsorption–extraction method were negative for all five RT-qPCR assays (i.e. consistently no detection of SARS-CoV-2). All eight replicate wastewater samples from Aircrafts 1 and 2 were negative for CDC N1 RT-ddPCR assay. Samples from Aircraft 3 were not tested with RT-ddPCR due to shortage of supplies.

For the untreated wastewater collected from the cruise ship, all six replicate samples prepared using both virus concentration methods yielded a positive signal for SARS-CoV-2 RNA using the CDC N1 assay (Table 2). The CDC N2 and NIID_2019-nCoV N assays detected SARS-CoV-2 RNA in four replicate samples. The E_Sarbeco assay appeared to be less analytically-sensitive (i.e. greater ALOD); only one of six replicates were RT-qPCR positive. The N_Sarbeco assay did not produce any amplification for these samples in two consecutive RT-qPCR runs. The CDC N1 and CDC N2 assays were consistently positive in replicate RT-qPCR reactions.

When results from all five assays were combined for each virus concentration method, the adsorption–extraction method yielded a greater number (n = 9) of positive samples compared with ultrafiltration with the Amicon® Ultra-15 centrifugal filter device (n = 6) (Table 2). For the adsorption–extraction method, the mean Cq value (Cq = 33.5) of the CDC N1 assay was much lower than the mean Cq value (Cq = 38) of CDC N2, E_Sarbeco, and NIID_2019-nCoV N. For ultrafiltration with the Amicon® Ultra-15, the mean Cq value (Cq = 36.5) of the CDC N1 assay was slightly lower than the mean Cq value (Cq = 37.15) of CDC N2, E_Sarbeco and NIID_2019-nCoV N assays. Among the replicate cruise ship untreated wastewater samples, four of six replicate samples were positive by the CDC N1 RT-ddPCR assay.

Of the five replicate cruise ship effluent wastewater (after treatment) samples prepared using both virus concentration methods, two replicate samples (adsorption–extraction method) yielded a positive signal for SARS-CoV-2 RNA using E_Sarbeco and NIID_2019-nCoV N assays (Table 2). The E_Sarbeco assay detected SARS-CoV-2 RNA in two of three replicate samples, and the NIID_2019-nCoV N assay detected in one of three replicate samples. The RT-qPCR amplifications were not consistent for all RT-qPCR replicates; Cq values ranged from 36.0 to 38.7. Samples processed with the adsorption–extraction method were positive, whereas samples processed through the Amicon® Ultra-15 centrifugal filter unit were negative. Among the five replicate cruise ship treated wastewater samples, two of five replicate samples were positive by the CDC N1 RT-ddPCR assay. Among the 21 replicates tested, seven samples were negative for all assays, and the remaining 14 samples were positive for at least one assay (Table 2).

The concentrations of SARS-CoV-2 RNA (copies/100 ml) in wastewater samples are shown in Table 3. Of the replicate RT-qPCR measurements for positively identified SARS-CoV-2 samples, 15 of 37 (41%) had concentrations above the ALOD for SARS-CoV-2. Greater concentrations were observed in the influent from the cruise ship in comparison to the single positive sample from an aircraft (Aircraft 1) and also the effluent of the cruise ship. Concentrations ranged from approximately 596 copies/100 ml (recovery uncorrected) wastewater to concentrations less than the SLOD for SARS-CoV-2 RNA (Table 1). Replicate measurements for a given sample were typically within one order of magnitude for a given assay. Differing assays estimated variable concentrations for a given sample. For example, the concentrations estimated for cruise ship influent concentrated using adsorption–extraction method differed by as much as an order of magnitude depending upon the RT-qPCR assay used (Table 3). Minimal differences were observed between the concentrations estimated by RT-qPCR and RT-ddPCR using the CDC N1 assay; nevertheless, the frequency of SARS-CoV-2 RNA detection was slightly greater using the CDC N1 RT-ddPCR than RT-qPCR. Representative amplicons were confirmed through sequencing and mapping to their corresponding positions in the SARS-CoV-2 genome (Supplementary Figure F1).

Table 3.

SARS-CoV-2 RNA concentrations (copies/100 mL wastewater), with 95% confidence interval (CI), in wastewater samples collected from aircrafts and a cruise ship

Sources (sampling dates)	Virus concentration methods used	Sample ID (volume concentrated)	Copies/100 ml of wastewater (95% CI)					RT-ddPCR Copies/100 ml of wastewater
Sources (sampling dates)	Virus concentration methods used	Sample ID (volume concentrated)	CDC N1	CDC N2	N_Sarbeco	E_Sarbeco	NIID_2019-nCoV N	CDC N1
Aircraft 1 (26 April 2020) Los Angeles–Brisbane	Adsorption–extraction	A1a-1 (100 ml)	–	–	–	272 (492–158)	–	–
	Adsorption–extraction	A1a-2 (100 ml)	–	–	36.3 (72.9–18.6)^a	–	–	–
	Amicon® Ultra-15	A1b-1 (50 ml)	–	–	–	211 (366–127)	–	–
		A1b-2 (50 ml)	–	–	–	–	–	–
		A1b-3 (50 ml)	–	–	–	87.2 (146–54.6)^a	–	–
Aircraft 2 (07 May 2020) Hong Kong–Brisbane	Adsorption–extraction	A2a-1 (100 ml)	–	–	–	–	–	–
		A2a-2 (100 ml)	–	–	–	–	–	–
		A2-a-3 (100 ml)	–	–	–	–	–	–
Aircraft 3 (10 May 2020) New Delhi–Sydney	Adsorption–extraction	A3a-1 (100 ml)	–	–	–	–	–	NT
Aircraft 3 (10 May 2020) New Delhi–Sydney	Adsorption–extraction	A3a-2 (100 ml)	–	–	–	–	–	NT
Cruise ship-influent (23 April 2020)	Adsorption–extraction	CSI1a-1 (100 ml)	208 (663–76.9) 275 (896–98.9) 239 (769–86.9)	60.9 (353–14.0)^a 7.6 (91.6–4.32)^a	–	–	–	387 103
		CSI1a-2 (100 ml)	77.6 (230–30.3)^a 110 (333–42.3) 118 (363–42.3)	8.99 (43.3–2.33)^a 6.66 (31.9–1.66)^a 1.33 (5.66–0.33)^a	–	–	0.66 (2.33–0.33)^a	175 201 398
		CSI1a-3 (100 ml)	450 (1518–157) 295 (966–106) 596 (2054–203)	11.7 (58.3–2.99)^a 14.3 (73.3–3.66)^a 14.3 (73.3–3.66)^a	–	138 (243–82.6) 138 (243–82.6)	34.3 (145–11.0)^a 32.0 (118–10.3)^a 117 (596–33.3)^a	880 445 258
	Amicon® Ultra-15	CSI1b-1 (50 ml)	71.9 (199–29.3)^a	–	–	–	156 (740–46.6)	–
		CSI1b-2 (50 ml)	50.6 (136–21.3)^a	–	–	–	103 (465–33.6)^a	–
		CSI1b-3 (50 ml)	43.9 (118–18.6)^a	23.3 (117–6.00)^a 20.0 (100–5.32)^a	–	–	–	–
Cruise ship-effluent (23 April 2020)	Adsorption–extraction	CSE1a-1 (200 ml)	–	–	–	53.6 (90.2–32.9)^a	–	–
		CSE1a-2 (200 ml)	–	–	–	–	–	94.5
		CSE1a-3 (200 ml)	–	–	–	98.6 (171–59.6)^a	63.6 (279–19.3)^a	–
	Amicon® Ultra-15	CSE1b-1 (100 ml)	–	–	–	–	–	83.5
	Amicon® Ultra-15	CSE1b-2 (100 ml)	–	–	–	–	–	–

Open in a new tab

^aIndicates that SARS-CoV-2 was positively identified and the calculated copy numbers is below the sample limit of detection (SLOD): 100 copy for CDC N1, 200 copies for CDC N2 and E_Sarbeco, 300 copies for N_Sarbeco and 400 copies for NIID_2019-nCoV N; NT, not tested due to unavailability of reagents.

The mean Cq value (29.2) of obtained for MHV RNA in untreated wastewater in the presence of high concentration of Novirusac Gel Bulk after 24 h was similar to the mean Cq (28.9) value of seeded MHV RNA. However, a 2 Cq value increase was observed after 48 h.

Discussion

Until an effective global SARS-CoV-2 vaccination becomes available, restrictions on domestic and international travel may continue for an extended period of time. Such restrictions have had and will continue to have a significant impact on the commercial airline and cruise line industries, and consequently on tourism and many other industries that depend heavily on people moving across national and international borders. Travel is an important COVID-19 control point. For example, among the COVID-19 infections in Australia, the majority of cases (62.8%) were acquired overseas (https://www.health.gov.au/resources/australian-covid-19-cases-by-source-of-infection). Therefore, it is of utmost importance to identify potential carriers of COVID-19 at points of entry. Screening wastewater samples from incoming aircraft or cruise ships could support clinical testing by providing site-specific, population-level information that can be used to guide passenger screening and contact tracing in a resource efficient and prioritized manner. Given that false negatives are possible through both clinical surveillance and wastewater surveillance, using the two in parallel could maximize the sensitivity of identification SARS-CoV-2 infections upon entry.

In our previous study, we demonstrated that SARS-CoV-2 RNA could be detected in municipal wastewater and has the potential to provide information on the prevalence of COVID-19 in Australian communities.¹¹ In this study, SARS-CoV-2 RNA was analysed and detected in wastewater samples collected from passenger aircraft and a cruise ship that was docked in Australia. To screen wastewater samples for SARS-CoV-2 RNA, we used two virus concentration methods (adsorption–extraction and Amicon® Ultra-15 (30 kDa) Centrifugal Filter Device), five RT-qPCR assays (four targeting N gene and one targeting E gene) and one RT- ddPCR assay (targeting N gene).

Although we were able to detect SARS-CoV-2 RNA in the multiple replicate wastewater sample collected from the vacuum truck that emptied wastewater from Aircraft 1, the RNA fragments were not consistently detected in all replicate wastewater samples and/or corresponding replicate RNA samples. This may be attributed to several factors, including low SARS-CoV-2 RNA concentrations and varying analytical sensitivity among the assays.¹¹^,¹³ It was also postulated that the disinfectants used in the aircraft may accelerate the decay of SARS-CoV-2 RNA. However, only 1.6–2 Cq increase was observed after 48 h for murine hepatitis virus, suggesting that Novirusac Gel Bulk has little impacts on the decay of SARS-CoV-2 for the flight duration 8–13 h. It has to be also noted that for the MHV RNA decay experiment, we used a high concentration of Novirusac Gel (i.e. 1:1 ratio Novirusac Gel:wastewater), however, the ratio of Novirusac Gel to wastewater is typically 100–1000 times lower in the aircraft and will have little impact on the decay of SARS-CoV-2 RNA.

In addition, the wastewater collected from the airplane contains large particulate matter (i.e. toilet paper) when compared with regular wastewater. This heterogeneity can contribute to difficulties in obtaining representative wastewater samples. Additionally, it is possible that the virus concentration, RNA extraction, and RT efficiencies varied among sample replicates given the inherent stochastic variability of these methods.¹¹^,¹³^,³⁰ As viral RNA concentrations become lower, it is expected that sub-sampling errors will increase because only a small portion of the sample (i.e. 3 μl from a total of 100 μl) is used in the RT-qPCR reaction. Although we detected RNA in the wastewater from the vacuum truck, quarantine isolation for 14 days and nasopharyngeal swab testing did not identify infected passengers. Thus, it is possible that the SARS-CoV-2 RNA detected could be carry over from other flights or residuals left in the vacuum truck. Alternatively, the positive detection in the sample from Aircraft 1 could be from a person on board that shed virus particles and did not develop symptoms. At least one study has identified a COVID-19 patient that was positive for SARS-CoV-2 RNA in stool despite repeated negatives in pharyngeal and sputum samples.³⁸ Wastewater samples from Aircrafts 2 and 3 were negative, which corroborated clinical testing and quarantine isolation of passengers from those aircraft. To avoid possible carry over from other flights due to mixing in the vacuum truck Qantas is designing a new sample extraction system allowing direct sampling from the plane before it enters the vacuum truck.

In contrast, SARS-CoV-2 RNA was detected more frequently in untreated cruise ship wastewater samples. It is possible that a greater proportion of passengers had COVID-19; thus, this contributed to the higher concentrations in wastewater. When the cruise ship wastewater samples were collected, there were 24 cases on board immediately prior to wastewater sampling. Additionally, the cruise ship passenger capacity is an order of magnitude greater than that of a commercial aircraft, which could increase the probability of passengers actively shedding SARS-CoV-2 RNA in their faeces. Finally, cruise ship passengers remain on board for several days to months and all passengers will defecate onboard during their trip; whereas aircraft passengers may not defecate in flight, particularly on short flights. Follow-up testing of the cruise ship wastewater was not possible as the ship departed following sample collection.

The frequency of SARS-CoV-2 RNA detection in treated cruise ship effluent wastewater was low in replicate RT-qPCR reactions compared with the cruise ship influent sample; this indicates that SARS-CoV-2 removal occurred in the wastewater treatment process. A recent study in Paris, France reported 2-log removal of SARS-CoV-2 RNA through the WWTP processes.¹⁴ The SARS-CoV-2 RNA copies were low, except for cruise ship influent wastewater samples as determined by the CDC N1 assay. The combined results indicate that when SARS-CoV-2 RNA concentrations are high, they are readily detectable by RT-qPCR. However, when the concentration is low or at concentrations near the SLOD, SARS-CoV-2 RNA will be difficult to detect consistently with one RT-qPCR assay. This present study indicated that the use of only a single assay (rather than a suite of 3–5), and/or not testing a sufficient number of replicate subsamples, may yield false-negative results that can negatively impact SARS-CoV-2 RNA risk mitigation and management decisions.

To maximize the probability of detection of SARS-CoV-2 RNA in wastewater samples, we have used five different RT-qPCR assays (targeting different regions of RNA from SARS-CoV-2 genome). Among the five assays tested, overall, the CDC N1 assay was the most sensitive and the N_Sarbeco assay was the least sensitive. Medema et al.¹³ also noted discrepancies between CDC N1, CDC N2, CDC N3 and E_Sarbeco assays for several wastewater samples.¹³ Assay sensitivity issues have also been documented for nasopharyngeal or oropharyngeal samples³¹^,³² and appear to occur when SARS-CoV-2 RNA concentrations are at or below 10 copies/μl of RNA extract.³²

In this study, the Cq values of SARS-CoV-2 in RT-qPCR positive samples were near the ALOD (i.e. amplified between 37–40 cycles). This may have partially contributed to the inconsistent results among the assays tested. CDC N1 and N2 assays consistently produced amplifications for cruise ship influent samples. Meanwhile, the N_Sarbeco assay yielded negative results for two consecutive runs for the cruise ship influent samples; thus this assay may be less sensitive. Alternatively, it is possible that nucleotide mutations are occurring in the genomic region targeted by the N_Sarbeco assay, which may have affected the detection.³³

Nonetheless, the analytical sensitivity of the five assays warrants further cross-validation using untreated wastewater samples seeded with low levels of SARS-CoV-2. The analytical sensitivity of the SARS-CoV-2 assays in wastewater may also be improved significantly by employing a concentration method which is able to recover >50% SARS-CoV-2 RNA from wastewater.²⁰ Furthermore, detection sensitivity can be improved by increasing the number of RT-qPCR replicates or sample volume from 50–100 ml to 300–500 ml wastewater, or by using digital PCR which has been reported to be one- to two-logs more sensitive than conventional qPCR platforms.¹

Wastewater surveillance has a potential role to play in the management of COVID-19. The monitoring of SARS-CoV-2 RNA in aircraft and cruise ship wastewater could help these industries return to full operation sooner. Many countries, including Australia, have adopted a ‘Controlled Adaptation Strategy’; a reality of accepting that there are ongoing international infections, potential asymptomatic transmission, and a limit to the duration of social distancing measures.³⁴ This strategy requires extensive testing and contact tracing to actively manage public health responses. Although not every passenger will use the toilet on a long-haul flight, the duration of a cruise means that they will on board of a cruise ship. SARS-CoV-2 RNA surveillance in aircraft and cruise ship wastewater has the potential to detect an onboard infection and prioritize clinical testing of all passengers to maximize the efficient use of resources. New approaches, such as wastewater surveillance applied to transportation-based sanitation systems, provide an additional layer of data that can be integrated with clinical testing, travel and border restrictions, as well as quarantine, to robustly manage SARS-CoV-2 transmission during the COVID-19 pandemic.

In conclusion, we show that monitoring vessel wastewater may potentially be a convenient and cheap means of monitoring for viruses during pandemics. This is the first study that reports SARS-CoV-2 RNA detection and concentrations in wastewater from aircraft and a cruise ship in Australia using multiple RT-qPCR assays and an RT-ddPCR assay. All positive RT-qPCR products were confirmed by sequencing. When the concentration of SARS-CoV-2 is low in wastewater samples, application of multiple RT-qPCR or RT-ddPCR assays increase detection sensitivity and minimize false-negative results. The detection sensitivity can be further enhanced by selecting an appropriate virus concentration and RNA extraction method. The analytical sensitivity of detecting low infection prevalences could be greatly increased by reductions in the SLOD through improvements in SARS-CoV-2 concentration and detection methods. It may also be possible to make small modifications of wastewater collection systems within aircraft and ships to improve sample capture. We also acknowledge that the presence of SARS-CoV-2 RNA in wastewater samples does not provide information regarding the infectivity or viability of SARS-CoV-2. Further studies are required regarding the public health implications of both positive and negative RT-qPCR results in the context of surveillance. However, the approach presented in this study is valuable along with the clinical testing to provide multiple lines of evidence of the COVID-19 infection status of passengers during travel.

Finally, although not assessed in this study, there is potential for alternative samples to be collected from passengers to assist with monitoring for viruses during pandemics. For example, pooled saliva samples or faecal samples might be collected from passengers before boarding, and test results from these samples could be available by the time passengers reach their destination. In this way, it will be possible to provide information on potential infections on-board. Thus, we recommend that such alternatives to wastewater testing also be explored.

Conflict of Interest

None declared.

Authors’ contribution

Warish Ahmed performed the designing and analysis of the study. Paul M. Bertsch did the designing of the study. Nicola Angel and Julian Zaugg performed the data analysis. Kyle Bibby, Aaron Bivins, Pradip Gyawali, Kerry Hamilton and Masaaki Kitajima wrote the manuscript. Leanne Dierens, Janette Edson, Philip Hugenholtz did the analysis of data. John Ehret, Ian Hosegood, Guangming Jiang, Jiahua Shi and Rory Verhagen did the sampling. Homa T. Sichani, Katja M. Shimko did the sample analysis. Stuart L. Simpson performed writing and designing. Wendy J.M. Smith did the analysis. Erin M. Symonds and Kevin V. Thomas performed the writing and analysis. Jochen F. Mueller did the designing of study and performed sampling.

Supplementary Material

Supplementary_Materials_taaa116

Click here for additional data file.^{(621.7KB, docx)}

Acknowledgements

We thank Amber Migus and Shane Neilson for assisting with sample collection. We thank Prof. Flavia Huygens from Queensland University of Technology (QUT) for providing access to a ddPCR platform. Erin M. Symonds was supported by the US National Science Foundation grant OCE-1566562. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US National Science Foundation. Kyle Bibby and Aaron Bivins supported by NSF 2027752. Dr Guangming Jiang is the recipient of an Australian Research Council DECRA Fellowship (DE170100694). Dr Jiahua Shi is supported by an ARC Discovery Project (DP190100385). The authors did not receive any funding for this project.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_Materials_taaa116

Click here for additional data file.^{(621.7KB, docx)}

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[ref7] 7. Mizumoto K, Kagaya K, Zerebski A et al. . Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the diamond princess cruise ship, Yokohama, Japan, 2020. Euro Surveill 2020; 25:2000180. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref8] 8. Boldog P, Tekeli T, Vizi Z et al. . Risk assessment of novel coronavirus COVID-19 outbreaks outside China. J Clin Med 2020 2020; 9:571. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[ref10] 10. Zheng S, Fan J, Yu F et al. . Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang Province, China, January-march 2020: retrospective cohort study. BMJ 2020 2020; m1443:369. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[ref12] 12. La Rosa G, Laconelli M, Mancini P et al. . First detection of SARS-CoV-2 in untreated wastewaters in Italy. Sci Total Environ 2020; 736:139652. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref13] 13. Medema G, Heijnen L, Elsinga G et al. . Presence of SARS-Coronavirus-2 in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in the Netherlands. Environ Sci Technol Lett 2020. doi: 10.1021/acs.estlett.0c00357. [DOI] [PubMed] [Google Scholar]

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[ref15] 15. Kitajima M, Ahmed W, Bibby K et al. . SARS-CoV-2 in wastewater: state of the knowledge and research needs. Sci Total Environ 2020; 139076. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref16] 16. Lodder W, Roda Husman AM. SARS-CoV-2 in wastewater: potential health risk, but also data source. The Lancet Gastro Hepatol 2020; 5:533–4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref17] 17. Peccia J, Zulli A, Brackney DE et al. . SARS-CoV-2 RNA concentrations in primary municipal sewage sludge as a leading indicator of COVID-19 outbreak dynamics. medRxiv 2020. doi: 10.1101/2020.05.19.20105999. [DOI] [Google Scholar]

[ref18] 18. Randazzo W, Truchado P, Ferrando CE et al. . SARS-CoV-2 RNA titers in wastewater anticipated COVID-19 occurrence in a low prevalence area. Water Res 2020; 181:115942. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref19] 19. Ahmed W, Harwood VJ, Gyawali P et al. . Comparison of concentration methods for quantitative detection of sewage-associated viral markers in environmental waters. Appl Environ Microbiol 2015; 81:2042–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref20] 20. Ahmed W, Bertsch P, Bivins A et al. . Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater. Sci Total Environ 2020b; 739:139960. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref21] 21. Symonds EM, Griffin DW, Breitbart M. Eukaryotic viruses in wastewater samples from the United States. Appl Environ Microbiol 2009; 75:402–1409. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref22] 22. Ikner LA, Soto-Beltran M, Bright KR. New method using a positively charged microporous filter and ultrafiltration for concentration of viruses from tap water. Appl Environ Microbiol 2011; 77:3500–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref23] 23. US Centers for Disease Control and Prevention 2019-Novel coronavirus (2019-nCoV) real-time rRT-PCR panel primers and probes. 2019. https://www.cdc.gov/coronavirus/2019-ncov/downloads/rt-pcr-panel-primer-probes.pdf.

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PERMALINK

Detection of SARS-CoV-2 RNA in commercial passenger aircraft and cruise ship wastewater: a surveillance tool for assessing the presence of COVID-19 infected travellers

Warish Ahmed, PhD

Paul M Bertsch, PhD

Nicola Angel, PhD

Kyle Bibby, PhD

Aaron Bivins, PhD

Leanne Dierens, MSc

Janette Edson, MSc

John Ehret, MSc

Pradip Gyawali, PhD

Kerry A Hamilton, PhD

Ian Hosegood, PhD

Philip Hugenholtz, PhD

Guangming Jiang, PhD

Masaaki Kitajima, DEng

Homa T Sichani, MSc

Jiahua Shi, PhD

Katja M Shimko, MRes

Stuart L Simpson, PhD

Wendy J M Smith, MSc

Erin M Symonds, PhD

Kevin V Thomas, DSc

Rory Verhagen, MSc

Julian Zaugg, PhD

Jochen F Mueller, PhD

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and Methods

Wastewater sampling

Sample concentration and RNA extraction

Table 2.

RT-qPCR analysis

RT-ddPCR analysis

qPCR inhibition and quality control

Sequencing and bioinformatics

Effects of aircraft wastewater tank disinfectant

Ethics approval

Results

PCR inhibition, performance characteristics of RT-qPCR assays and ALOD

Table 1.

SARS-CoV-2 RNA in wastewater samples

Table 3.

Discussion

Conflict of Interest

Authors’ contribution

Supplementary Material

Acknowledgements

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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