key: cord-0710297-63xcsanb
authors: Wang, Zhenyu; Yang, Wenyu; Hua, Pei; Zhang, Jin; Krebs, Peter
title: Transmission risk of SARS-CoV-2 in the watershed triggered by domestic wastewater discharge
date: 2021-10-09
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2021.150888
sha: fee1fa493189982c9d0a99fba36953492c87aae1
doc_id: 710297
cord_uid: 63xcsanb

The outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has already become an unprecedented global pandemic. However, the transmission of SARS-CoV-2, especially the protected SARS-CoV-2 RNA (pRNA) with infectious particles in waterways, is still largely unexplored. In this study, we developed a model to estimate SARS-CoV-2 transmission from the risk source in the excretion of patients to the final exposure in surface water. The model simulated the spatial and temporal distribution of the viral pRNA concentrations in the surface water of the Elbe watershed from March 2020 to January 2021. The results show that the WWTPs with the maximum capacity of >10,000 population equivalents were responsible for 95% of the viral load discharged into the surface water. We estimated the pRNA concentrations in surface water to be 1.33 × 10−2 copies·L−1 on average in the watershed based on the model simulation on viral transmission. It had considerable variations in spatial and temporal scales, which are dominantly controlled by epidemic situations and virus transport with decay in water, respectively. A quantitative microbial risk assessment was conducted to estimate the viral infection probability from surface water ingestion with consideration of the influence of toilet usage frequency and gender/age population groups. All the infection probabilities in the study period were lower than the reference risk levels of 10−4 and 10−5. The individuals aged 15–34 years had the highest infection probability of 4.86 × 10−9 on average from surface water ingestion during swimming activities. The data provided herein suggest that the low pRNA concentrations and infection probability reflected that the waterways were unlikely to be a significant transmission route for SARS-CoV-2.

incidence was 134 per 100,000, higher than the critical rate of 100 per 100,000 (RKI, 2021) .

Therefore, controlling this rapid worldwide transmission needs global cooperation and a better understanding of the viral transmission pathway is crucial for the effective implementation of anti-epidemic measures.

In this regard, the transmission of SARS-CoV-2 through respiratory droplets from infected people, aerosols, and fomites has been intensively reported (Asadi et al., 2020; Chan et al., 2020; Chia et al., 2020) . By contrast, the other transmission pathways of SARS-CoV-2 are still largely unexplored. The investigation of a SARS-CoV-1 outbreak from an apartment building in Hong Kong in 2003 highlighted the role of the sewage system in the transmission risk of SARS-CoVs in waterways (McKinney et al., 2006) . Specifically, SARS-CoV infection may occur through consumption of contaminated water droplets or respiration of aerosols created from toilet flushing (Gormley et al., 2020) , defective plumbing systems (Yu et al., 2014) , or uncovered aerobic wastewater treatment facilities (Gholipour et al., 2021) . As the viral RNA was also found in excretions (stool and urine) of symptomatic and asymptomatic patients (Guan et al., 2020; Pan et al., 2020) as well as wastewater (Ahmed et al., 2020; Haramoto et al., 2020) , the potential infection risk of SARS-CoV-2 from waterways during the pandemic has been of increasing concern.

The SARS-COV-2 load has been reported in natural receiving water in low sanitation countries (Guerrero-Latorre et al., 2020) . However, the transmission of SARS-COV-2 in waterways is unclear due to the complicated multifactorial impacts of the epidemic situation, discharges of wastewater treatment plants (WWTPs), dynamics of water flows, and lack of main channel, Saale watershed, Mulde watershed, and Schwarze Elster watershed (Fig. 1a) .

The study areas in Saxony, Thuringia, Saxony-Anhalt, and Brandenburg account for 41.2%, 19.2%, 36.8%, and 2.9% of the rivers and streams, respectively, and 47.8%, 21.1%, 27.5%, and 3.6% of the population, respectively. The model used epidemic data from 46 districts, wastewater data from 280 WWTPs, and surface water flow data from 186 discharge monitoring sites to estimate the transmission of SARS-CoV-2 from the source in the excretion of patients to the final exposure in surface water.

The study area experienced two waves of COVID-19, namely the first wave in March-May 2020 and the second wave in August-January 2020 (Fig. 1b) . The number of active infection cases in the study period ranged from 347 to 47,781. Saxony state accounted for the highest proportion of active infection cases (25.2-68.1%) in the study region, followed by Thuringia (24.9-39.8%), and Saxony-Anhalt (21.7-34.6%). The district-level distribution of active infection cases and cases in 100,000 inhabitants are shown in Fig. 2 and Fig. S1-3, respectively.

The SARS-CoV-2 concentrations in waterways were calculated by considering the daily toilet usage, regional epidemic situation, SARS-CoV-2 removal in WWTPs, and dischargedependent transmission in surface water with dilution and decay. The discharge-dependent SARS-CoV-2 concentrations in surface water were calculated as follows :

where μ is the virus concentration (copies·L -1 ); d eff is the daily virus amount in the WWTP effluent (copies); Q is the surface-water flow rate, which is the sum of river/stream flow rates and WWTP effluent flow rates (m 3 /d), and the WWTP effluent flow rates in units of m 3 /d were yielded by averaging annual effluent volume (m 3 /year) over a year; c is the unit conversion factor; and δ is the viral inactivation rate or decay rate described as first-order J o u r n a l P r e -p r o o f distance-dependent decay kinetics shown as Eq. 2 (Fauvel et al., 2017) .

where the first-order inactivation parameter γ is parameterized by a lognormal distribution, and L is the distance (m).

The WWTP status and regional epidemic situation exert significant influences on the variable d eff (Eq. 3-5).

where r is the protected RNA to total RNA ratio (pRNA/vRNA), which was determined based on a pervious stuidy (Wurtzer et al., 2021) ; n is the total viral RNA amount discharged into sewage per person per day (copies·person -1 ·day -1 ), θ v is the virus removal efficiency of WWTPs; λ c is the population connection rate to WWTPs or the percentage of treated wastewater; N is the COVID-19 infection cases connected to WWTPs, which is proportional to population connected to WWTPs; P and the regional infection rate, β. P is estimated by the maximum structural capacity of a WWTP in population equivalents (PE) and the domestic wastewater proportion λ p . Since the temporal change of the regional population can cause the design value of P in the region to exceed the current WWTP-connected population, P in part of the districts is normalized by scaling the ∑P to λ c population in the district.

Moreover, n is calculated as follows :

where φ, v/m, and n are the toilet usage frequency constant controlled by the people's reaction to the government interventions, daily volume or mass of a patient's excretions (mL for urine; g for faeces), and the genome vRNA concentration in a patient's excretions (copies·mL -1 for urine; copies·g -1 for faeces), respectively. Subscripts s and n are stool and J o u r n a l P r e -p r o o f urine, respectively.

The values for all parameters are parameterized as shown in Table 1 based on the   published scientific literature (Table S2-6) . The unit of copies·mL -1 for the genome vRNA concentration in faeces reported in the literature was converted to copies·g -1 by using a density of wet human faeces of 1.075 g·mL −1 (Penn et al., 2018) . The best-fit distributions of parameters were selected based on the Kolmogorov-Smirnov analysis (p < 0.05).

The infection probability of SARS-CoV-2 from surface water ingestion was estimated by using the estimated genome pRNA concentrations in the surface water and the doseresponse relationship alongside QMRA models (Haas et al., 2014) . The probability of infection was characterized by a modified exponential dose-response function, as given in Eq.

7 (Watanabe et al., 2010; Zhang and Wang, 2020b) .

where k is the dose-response parameter; d ex is the exposure dose of ingested pathogens in an event in a day (copies·day -1 ), and α is the infection rate constant for the specific age and gender groups according to the German COVID-19 infection data (Table S4 ). k is modelled by using a log-normal distribution, and d ex is calculated from Eq. 8 according to the previous study .

where m is the water exposure dose per person in an event in a day (mL·person -1 ·day -1 ).

m is described by the gamma distribution for the swimming activity. The values of the model parameters are shown in Table 1 .

The estimated infection probabilities were compared with the reference risk levels suggested by the US EPA (Gholipour et al., 2021) and WHO (Mara et al., 2007) . The reference risk levels were in units of person -1 ·year -1 , and they were converted into person -J o u r n a l P r e -p r o o f 1 ·day -1 based on Eq. 9 (Moazeni et al., 2017) :

where n is the exposure frequency per year, which was 7 times per year in the study (Schets et al., 2011) .

Since there is a large variability for most of the parameters used in the model, the results were estimated by a Latin-hypercube-sampling-based Monte Carlo simulation. The Monte

Carlo simulation draws 10,000 trials to create a distribution of infection probabilities. The uncertainty of the results was described as a 95% confidence interval. The estimated median results were used to make a conservative estimation of the viral concentrations in the surface water and the infection probability.

The sensitivity of the estimated SARS-CoV-2 concentrations was analysed utilizing Sobol's method and rank correlation. Sobol's method calculated the total Sobol's sensitivity index S Ti to describe the ratio between the variance derived from the i th parameter and interactions of all the other parameters (Sobol, 2001; Vezzaro and Mikkelsen, 2012 ) (Eq. 10).

=~( ( |~)) ( )

The rank correlation was determined by utilizing the Spearman rank correlation ρ between each model parameter and the estimated infection probability (Bivins et al., 2017) .

Both methods were conducted by Latin-hypercube sampling. The numerical results of the sensitivity analysis are summarized in Table S7 -8. The uncertainty and sensitivity analysis was conducted using Python 3.7.

The risk was classified into the scenarios of toilet usage frequency caused by the patients' psychological stress during lockdown (normal and increasing usage frequency; N and I), the age groups (5-14, 15-34, 5-59, and 60-79 years old) and the gender groups (male J o u r n a l P r e -p r o o f and female; M and F) by varying the model parameters. The detailed statistical analysis of toilet usage frequency is presented in Supplementary material S1. The scenario-based variation in parameter values is shown in Table 1 .

The river discharge data were provided by the State Office for the Environment, 

Journal Pre-proof

The spatiotemporal SARS-CoV-2 pRNA distribution in the WWTPs and the surface water of the Elbe watershed was simulated by the transmission model. The median results are illustrated in Fig. 2a -d. The uncertainties of the results caused by the model parameter variability are shown as 95% confidence intervals ( Fig. S1-3) . The estimated median SARS-CoV-2 pRNA amounts in the entire wastewater system were 6.59×10 9 copies (95% CI:

3.36×10 11 -7.92×10 7 copies), 9.14×10 8 copies (95% CI: 4.66×10 10 -1.10×10 7 copies), 2.86×10 8 copies (95% CI: 1.46×10 11 -3.44×10 7 copies), and 1.13×10 11 copies (95% CI:

5.75×10 12 -1.35×10 9 copies) on 1 st April 2020, 1 st July 2020, 1 st October 2020, and 1 st January respectively. Furthermore, the WWTPs in Classes 2-5 treated the SARS-CoV-2 loads in wastewater daily from an average of 3, 5, 19, and 124 patients, respectively. The WWTPs in Classes 4 and 5 were responsible for approximately 95% of the SARS-CoV-2 input from the patients and output to the surface water.

In terms of virus concentrations, the virus amounts in the influent and effluent of WWTPs were transformed into concentrations by dividing the annual average daily flow of WWTP effluent. The estimated median pRNA influent concentrations were spatially distributed in the range of 0-3.95×10 1 copies·L -1 (95% CI: 0-2.01×10 3 copies·L -1 ), 0-6.77×10 0 copies·L -1 (95% CI: 0-3.45×10 2 copies·L -1 ), 0-1.04×10 1 copies·L -1 (95% CI: 0-5.32×10 2 copies·L -1 ) and 8.19×10 0 -3.12×10 2 copies·L -1 (95% CI: 9.84×10 -2 -1.59×10 4 copies·L -1 ) on 1 st April 2020, 1 st July 2020, 1 st October 2020, and 1 st January 2021, respectively. The highest daily influent concentration in the entire period (3.65×10 2 copies·L -1 ) occurred on 23 rd Dec 2020. The SARS-CoV-2 concentrations in the effluent were 0.01% of the influent concentrations, and less than 3.65×10 -2 copies·L -1 in the study period. Compared J o u r n a l P r e -p r o o f Journal Pre-proof with the globally-reported vRNA concentrations in influent (10 2 to 10 6 copies·L -1 ) (Ahmed et al., 2020; Kumar et al., 2021; Randazzo et al., 2020) and effluent (0 to 10 5 copies·L -1 ) (Haramoto et al., 2020; Jones et al., 2020; Randazzo et al., 2020) , the estimated daily pRNA concentrations were relatively lower due to the low proportion of protected SARS-CoV-2 genomes in wastewater. 1.64×10 -4 -1.42×10 0 copies·L -1 (95% CI: 1.33×10 -6 -1.11×10 2 copies·L -1 ) during the epidemics, followed by Saxony-Anhalt (1.45×10 -4 -4.21×10 -2 copies·L -1 ; 95% CI: 1.17×10 -6 -3.15×10 0 copies·L -1 ), Thuringia (9.75×10 -5 -1.56×10 -2 copies·L -1 ; 95% CI: 7.89×10 -7 -1.06×10 0 copies·L -1 ), and Brandenburg (2.09×10 -5 -3.01×10 -2 copies·L -1 ; 95% CI: 1.61×10 -7 -2.29×10 0 copies·L -1 ). Saxony also had the highest active infection cases and cases in 100,000

inhabitants among the states ( Fig. S1-3) , which indicates that the epidemic situation had a significant influence on the viral concentration distribution.

The pRNA concentrations were significantly different on the temporal and spatial scales (Kruskal-Wallis test p < 0.05). Specifically, the temporal and spatial deviations of the estimated median results were 5.58×10 -9 and 7.51×10 -7 , respectively. The spatial and temporal variation in pRNA concentrations was controlled by the epidemic situation as well as virus transport in water. The epidemic situation included regional active infection cases and active case density in the 100,000 inhabitants, while virus transport was parameterized by J o u r n a l P r e -p r o o f Journal Pre-proof flow dilution and viral decay in the surface water. Compared with the active infection cases, the active case density in the 100,000 inhabitants described the regional infection rate. The flow dilution and viral decay were quantified as water flow rates and the water distance to the nearest WWTP. The correlation results examined by the Mantel test with the Spearman rank correlation (p < 0.05) illustrate that the epidemic situation was dominant in the temporal variation of the pRNA concentrations (Fig. 3a) . Specifically, regional active infection cases and case density had the largest consistent correlation coefficients of 0.74 and 0.75, respectively. This shows that the deterioration of the epidemic situation mainly contributed to the increasing pRNA concentrations on the time scale.

In contrast, virus transport significantly influenced the spatial variation in the pRNA concentrations. The flow dilution exerted a more substantial influence than the decay process.

The negative correlation of the pRNA concentrations and SARS-CoV-2 transport in the surface water shows that the dilution and decay of virus in water were of importance to the reduction of viral concentrations in the surface water. The dilution performance could be parameterized as a dilution factor, which is a ratio of the surface water to wastewater flow from the WWTPs. A factor of >40 is suggested by engineering practice to prevent the infection risk of viruses (Kumar et al., 2021) . The average dilution factor near the WWTPs was 2,584.9 in the studied period. A total of 43.2%-71.1% of the dilution factors in the Elbe watershed were > 40 (Fig. 3b) . Additionally, due to viral decay, the pRNA spatial-average amount in the surface water decreased from 1.17×10 8 copies to 1.31×10 -2 copies with increasing transmission distance (from 0 m to 326,000 m) (Fig. 3c) . There were some sudden increases in the distance-dependent pRNA amount reduction, which might be caused by virus accumulation from the residual upstream.

The analysis suggests that the case density and the flow rate in the surface water were two fundamental variables and affected the spatial and temporal variation of pRNA J o u r n a l P r e -p r o o f concentrations in the surface water. The water near the WWTPs had higher pRNA concentrations.

The infection probabilities of SARS-CoV-2 from surface water ingestion during swimming were separately estimated under the scenarios of normal and increasing toilet usage frequencies, the age groups (5-14, 15-34, 5-59, and 60-79 years old), and the gender groups. The toilet usage frequency variation was caused by the patients' psychological stress during the lockdown, which resulted in the change in viral concentrations in wastewater and surface water. Regarding the population groups, men had a comparable infection probability with a spatiotemporal median of 4.30×10 -9 (95% CI: 3.74×10 -12 -2.73×10 -7 ) compared with the infection probability of women (3.66×10 -9 , 95% CI: 3.74×10 -12 -2.32×10 -7 ). This result was different from the statistics from Robert Koch-Institut (RKI, 2021) that the female adults (812,739) had more infection cases than men (764,026) in Germany. The difference might be caused by the uncertainty of the exposure water dose for men and women. Additionally, the population aged 15-34 years had the highest probability of infection (4.86×10 -9 ; 95% CI: 4.05×10 -12 -3.09×10 -7 ) among the age groups. The results were consistent with the report that the population aged 15-34 years was the most infected population in Germany (RKI, 2021) .

It should be noted that all the spatiotemporal distributed infection probabilities in the study period were significantly lower than the daily-transformed reference risk levels of 10 -4 and 10 -5 suggested by the US EPA (Gholipour et al., 2021) and WHO (Mara et al., 2007) J o u r n a l P r e -p r o o f Journal Pre-proof respectively. This suggests that waterborne transmission was a less important transmission route for SARS-CoV-2 than air transmission.

As excretions were the potential source of SARS-CoV-2 loading to wastewater and surface water, WWTPs played a crucial role in controlling the transmission of SARS-CoV-2 pRNA. The main associated technical parameters were virus removal efficiency θ v and population connection rates to WWTPs, λ c . The toilet usage frequency scenario showed significant impacts on the sensitivity results of the sewage parameters (v u , n u , m s , and n s ) (Mann-Whitney U test p < 0.05), as shown in Table S10 . Specifically, the increasing toilet usage frequency resulted in a 50% increase in S Ti (6% for ρ) of m s , and n s , but a 50% decrease in S Ti (10% for ρ) of m u , and n u . However, the gender and age groups exerted a limited impact on the sensitivity variation.

The results of the study reflected that WWTPs were an important sector between domestic wastewater and surface water. On the one hand, most of the SARS-CoV-2 discharged into the surface water came from WWTPs, especially WWTPs in urban areas.

Over 90% of SARS-CoV-2 in virus-contaminated domestic wastewater was discharged from the WWTPs with a maximum capacity of > 10,000 population equivalents. These WWTPs were located around urban cities, indicating that urban areas were the major contributors to virus-contaminated wastewater. On the other hand, the SARS-CoV-2 concentration in the wastewater was expected to be mostly removed in WWTPs. However, the removal efficiency of SARS-CoV-2 in the wastewater treatment of WWTPs ranges widely and is still largely unexplored specifically for SARS-CoV-2. Several studies and reviews have estimated or investigated the removal efficiencies of SARS-CoV-2 RNA for various conventional activated sludge (CAS) processes (Abu Ali et al., 2021; Bogler et al., 2020; Kumar et al., 2021) . The results show that the removal efficiency of SARS-CoV-2 vRNA in WWTPs would be in the range of between 2log and 6log. It seems that the infectious particles were less stable than the total RNA in the water (Sala-Comorera et al., 2021) , which suggests that the removal efficiency of pRNA should be higher. For advanced treatment processes, J o u r n a l P r e -p r o o f chlorination, ozone, and UV irradiation could significantly inactivate SARS-CoV-2 (Heilingloh et al., 2020; . However, no WWTPs in the study area have chlorination, ozone, and UV treatments, which might be a concern regarding the potential spread of SARS-CoV-2. Combined with the abovementioned removal efficiencies and the wastewater treatment process data, the removal efficiency of 3log was selected to simulate SARS-CoV-2 RNA reduction in WWTPs.

To confirm the impact of the removal efficiency in WWTPs on the infection risk of SARS-CoV-2, a removal efficiency between 3-6log was analysed. The result that the increase in removal efficiencies for wastewater treatment processes had a limited impact on the infection probability from surface water ingestion. This suggests that the improvement of the population connection rate to WWTPs took priority over the enhancement of the SARS-CoV-2 treatment technique, especially in developing countries with low population connection rates to WWTPs, although tertiary treatment is an additional important barrier (Abu Ali et al., 2021) ,.

Furthermore, the distribution of SARS-CoV-2 pRNA in the surface water was related not only to the WWTPs but also to other factors, such as the dilution of viruses by surface water. The dilution process was driven by the dynamics of water flows, which had seasonal variations (Kumar et al., 2021) . This resulted in seasonal changes in pRNA concentrations in the watershed (Fig. 2) . The dilution factors were higher in June-July 2020 (average 2,800.0) and October-November 2020 (average 3,726.4) (Fig. 3b) . Compared with the temporal changes in water flows, the spatial variation in the flow in the watershed was more important to the distribution of viral concentrations in the surface water according to the sensitivity result.

Regarding the infection probability of SARS-CoV-2 from surface water ingestion, it is worth noting that the lockdown policy in the study area could not eliminate the probability of J o u r n a l P r e -p r o o f surface water ingestion during the epidemics. The lockdown measure mainly prohibits gatherings but individual outdoor activities are allowed in Germany (Anke et al., 2021) . A survey shows that children's and adolescents' activity time of nonorganized sports increased by 100%-400% after the COVID-19 lockdown in Germany (Schmidt et al., 2020) . The increase in outdoor activities might increase the probability of surface water ingestion during swimming activities.

The infection probabilities of SARS-CoV-2 from surface water ingestion were estimated in different scenarios and population groups. Interestingly, the variation in toilet usage frequencies caused by the patients' psychological changes during the lockdown had an impact on the infection probabilities. The lockdown intervention physically distances social contact and restricts movement, which mainly reduces the risk of virus transmission through infected people and aerosols. However, it might show a distinctive effect on the infection probability of SARS-CoV-2 from surface water ingestion, as the source of the virus comes from excrements in the toilet. As shown in Supplementary material S1, the work-at-home population during lockdown relieves psychosocial stress and physical stress, increasing urination and defaecation frequency. The lockdown might slightly amplify the probability in the surface water when the lockdown starts, but the lockdown policy is still the effective approach to slow COVID-19 transmission (WHO, 2020).

The model-based results presented in this work remained uncertain, although we highly improved the reliability of the results by using pRNA instead of vRNA in the calculation of viral concentrations in the surface water and analyzing the uncertainty and sensitivity of the model parameters. By using the pRNA/vRNA ratio as a model parameter, the model was able to estimate the more infectious SARS-CoV-2 concentrations in surface water rather than the total SARS-CoV-2 RNA concentrations, including the noninfectious particles (Dada and Gyawali, 2021; Yang et al., 2020; Zaneti et al., 2021) . However, pRNA includes the J o u r n a l P r e -p r o o f protected genomes within infectious particles and noninfectious particles in a ribonucleoprotein complex (Wurtzer et al., 2021) . Therefore, there might be a slight overestimation in the results. In addition, the model-based results were affected by the model parameters. The large 95% confidence intervals of the results indicate that the model parameter variations had a significant influence on the simulation results of the viral concentration distribution and the infection probability. Except for the parameters considered in the study, the model was also inherently limited by other uncertainties from the unknown information, such as the lack of inactivation or the reduction of viral pRNA in the sewage system and the detailed physical-chemical properties of the surface water. Specifically, the parameters of pH, temperature, suspended solids, and pharmaceuticals in water could influence SARS-CoV-2 concentrations (de Oliveira et al., 2021; Dehbandi and Zazouli, 2020; Geller et al., 2012; Kumar et al., 2019) . The model could be further improved if SARS-CoV-2 was more deeply investigated and comprehensively understood.

This study provides an approach considering the transmission of SARS-CoV-2 protected RNA genomes (pRNA) from infected individuals and a (Wurtzer et al., 2021) θ v Virus removal / 3log (Kumar et al., 2021; Yang et al., 2020) J o u r n a l P r e -p r o o f 

Tracking SARS-CoV-2 RNA through the Wastewater Treatment Process

First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community

Differentiating between the possibility and probability of SARS-CoV-2 transmission associated with wastewater: empirical evidence is needed to substantiate risk

Impact of SARS-CoV-2 on the mobility behaviour in Germany

The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles? Aerosol Science and Technology

Coronavirus disease 2019 (COVID-19) outbreak: some serious consequences with urban and rural water cycle. npj Clean Water. 3, 32

Estimating Infection Risks and the Global Burden of Diarrheal Disease Attributable to Intermittent Water Supply Using QMRA

Rethinking wastewater risks and monitoring in light of the COVID-19 pandemic

Effects of a "new" human respiratory virus in volunteers

A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster

Detection of air and surface contamination by SARS-CoV-2 in hospital rooms of infected patients

The radicalized self: the impact on the self of the contested nature of the diagnosis of chronic fatigue syndrome

Quantitative microbial risk assessment (QMRA) of occupational exposure to SARS-CoV-2 in wastewater treatment plants. Science of The Total Environment

MurineHepatitis Virus Strain 1 Produces a Clinically Relevant Model of Severe Acute Respiratory

Human coronaviruses: insights into environmental resistance and its influence on the development of new antiseptic strategies

COVID-19 infection risk from exposure to aerosols of wastewater treatment plants

Kinetics and Interrelations of the Renin Aldosterone Response to Acute Psychosocial Stress: A Neglected Stress System

COVID-19: mitigating transmission via wastewater plumbing systems. The Lancet Global Health

Clinical Characteristics of Coronavirus Disease 2019 in China

Health risks in wastewater irrigation: comparing estimates from quantitative microbial risk analyses and epidemiological studies

Coronavirus Disease

Lessons Learnt from International Response and Advice to the Georgia Government. The Innovation

Environmental transmission of SARS at Amoy Gardens

Estimation of health risks caused by exposure to enteroviruses from agricultural application of wastewater effluents

The novel SARS-CoV-2 pandemic: Possible environmental transmission, detection, persistence and fate during wastewater and water treatment. Science of The Total Environment

A rapid review of mental and physical health effects of working at home: how do we optimise health?

Wastewater treatment recorded by OECD

The Physiology of Human Defecation

Viral load of SARS-CoV-2 in clinical samples. The Lancet Infectious Diseases

A review of the impact of environmental factors on the fate and transport of coronaviruses in aqueous environments

SARS-CoV-2 can be

urine, blood, anal swabs, and oropharyngeal swabs specimens

Review of synthetic human faeces and faecal sludge for sanitation and wastewater research

SARS-CoV-2 RNA in wastewater anticipated COVID-19 occurrence in a low prevalence area. Water Research

Combining urine separation with waste design: an analysis using a stochastic model for urine production

RKI, 2021. COVID-19 (Coronavirus SARS-CoV-2)

The Characterization of Feces and Urine: A Review of the Literature to Inform Advanced Treatment Technology

Decay of infectious SARS-CoV-2 and surrogates in aquatic environments

Exposure assessment for swimmers in bathing waters and swimming pools

Physical activity and screen time of children and adolescents before and during the COVID-19 lockdown in Germany: a natural experiment

Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and computers in simulation

Application of global sensitivity analysis and uncertainty quantification in dynamic modelling of micropollutants in stormwater runoff

Detection of SARS-CoV-2 in Different Types of Clinical Specimens

Development of a Dose-Response Model for SARS Coronavirus

WHO, 2020. Coronavirus disease (COVID-19): Herd immunity, lockdowns and COVID-19

WHO, 2021. WHO Coronavirus Disease (COVID-19) Dashboard

Virological assessment of hospitalized patients with COVID-2019

Several forms of SARS-CoV-2 RNA can be detected in wastewaters: Implication for wastewater-based epidemiology and risk assessment

Characteristics of pediatric SARS-CoV-2 infection and potential evidence for persistent fecal viral shedding

Estimation of the potential spread risk of COVID-19: Occurrence assessment along the Yangtze, Han, and Fu River basins in Hubei, China. Science of The Total Environment

Clinical significance of a high SARS-CoV-2 viral load in the saliva

Severe acute respiratory syndrome beyond Amoy Gardens: completing the incomplete legacy

Quantitative microbial risk assessment of SARS-CoV-2 for workers in wastewater treatment plants. Science of The Total Environment

Elimination of viruses from domestic wastewater: requirements and technologies

 Wölfel et al., 2020; Xu et al., 2020; Zhang et al., 2021)