Coronavirus Disease 2019 and Airborne Transmission: Science Rejected, Lives Lost. Can Society Do Better?

This is an account that should be heard of an important struggle: the struggle of a large group of experts who came together at the beginning of the COVID-19 pandemic to warn the world about the risk of airborne transmission and the consequences of ignoring it. We alerted the World Health Organization about the potential significance of the airborne transmission of SARS-CoV-2 and the urgent need to control it, but our concerns were dismissed. Here we describe how this happened and the consequences. We hope that by reporting this story we can raise awareness of the importance of interdisciplinary collaboration and the need to be open to new evidence, and to prevent it from happening again. Acknowledgement of an issue, and the emergence of new evidence related to it, is the first necessary step towards finding effective mitigation solutions.

Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques.

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Scaling SARS-CoV-2 wastewater concentrations to population estimates of infection.

Monitoring the progression of SARS-CoV-2 outbreaks requires accurate estimation of the unobservable fraction of the population infected over time in addition to the observed numbers of COVID-19 cases, as the latter present a distorted view of the pandemic due to changes in test frequency and coverage over time. The objective of this report is to describe and illustrate an approach that produces representative estimates of the unobservable cumulative incidence of infection by scaling the daily concentrations of SARS-CoV-2 RNA in wastewater from the consistent population contribution of fecal material to the sewage collection system.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Changes in Sewage Sludge Chemical Signatures During a COVID-19 Community Lockdown, Part 2: Nontargeted Analysis of Sludge and Evaluation with COVID-19 Metrics.

Sewage sludge and wastewater include urine and feces from an entire community, and it is highly likely that this mixture contains chemicals whose presence is dependent on levels of SARS-CoV-2 in the community. We analyzed primary sewage sludge samples collected in New Haven, Connecticut, USA, during the initial wave of the COVID-19 pandemic using liquid chromatography coupled with high-resolution mass spectrometry and performed an exploratory investigation of correlations between chemical features and COVID-19 metrics including concentrations of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) RNA in the sludge and local COVID-19 case numbers and hospital admissions. Inclusion of all chemical features in this analysis is key for discovering potential indicator compounds for COVID-19, whose structures may not be known. We found correlations with COVID-19 metrics for several identified chemicals as well as many unidentified features in the data, including three potential indicator molecules that are recommended for prioritization in future studies on COVID-19 in wastewater and sludge. These features have molecular weights of 108.0935, 318.1214, and 331.1374. While it is not possible to achieve prediction of COVID-19 epidemiological metrics from the one data set used in the present study, advances in this research area are important to share as scientists worldwide work on discovering efficient methods for tracking SARS-CoV-2 in wastewater and the environment. Environ Toxicol Chem 2022;41:1193-1201. © 2021 SETAC.

Changes in Sewage Sludge Chemical Signatures During a COVID-19 Community Lockdown, Part 1: Traffic, Drugs, Mental Health, and Disinfectants.

The early months of the COVID-19 pandemic and the associated shutdowns disrupted many aspects of daily life and thus caused changes in the use and disposal of many types of chemicals. While records of sales, prescriptions, drug overdoses, and so forth provide data about specific chemical uses during this time, wastewater and sewage sludge analysis can provide a more comprehensive overview of chemical changes within a region. We analyzed primary sludge from a wastewater-treatment plant in Connecticut, USA, collected March 19 to June 30, 2020. This time period encompassed the first wave of the pandemic, the initial statewide stay at home order, and the first phase of reopening. We used liquid chromatography-high-resolution mass spectrometry and targeted and suspect screening strategies to identify 78 chemicals of interest, which included pharmaceuticals, illicit drugs, disinfectants, ultraviolet (UV) filters, and others. We analyzed trends over time for the identified chemicals using linear trend analyses and multivariate comparisons (p < 0.05). We found trends related directly to the pandemic (e.g., hydroxychloroquine, a drug publicized for its potential to treat COVID-19, had elevated concentrations in the week following the implementation of the US Emergency Use Authorization), as well as evidence for seasonal changes in chemical use (e.g., increases for three UV-filter compounds). Though wastewater surveillance during the pandemic has largely focused on measuring severe acute respiratory syndrome-coronavirus-2 RNA concentrations, chemical analysis can also show trends that are important for revealing the public and environmental health effects of the pandemic. Environ Toxicol Chem 2022;41:1179-1192. © 2021 SETAC.

How Narrow Is the Gas Phase Mobility Distribution of Enveloped Viruses? The Case of the Φ6 Bacteriophage.

We use the Φ6 bacteriophage previously exploited as a BSL-1 surrogate of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome (MERS) coronavirus to obtain the first high-resolution gas phase mobility spectra of an enveloped virus. The relative full width at half-maximum found for the viral mobility distribution (FWHMZ < 3.7%) is substantially narrower than that reported by prior mobility or microscopy studies with other enveloped viruses. It is nevertheless not as narrow as that recently found for several non-enveloped viruses (FWHMZ ≈ 2%), presumably due to particle to particle variability of enveloped viruses. This 3.7% is an upper bound to the actual width. Nevertheless, the well-defined mobility peaks obtained indicate that gas phase mobility analysis is a more discriminating methodology than that previously demonstrated for physically based non-genetic viral diagnostic of enveloped viruses. These results are obtained by analysis of the original cell culture medium containing the virus, purified only by passage through a 0.22 µm filter and by dialysis into a 10 mM aqueous ammonium acetate buffer. We confirmed that this buffer exchange preserves infectivity. Therefore, the 63.7 nm mobility diameter found, although smaller than the 75 nm previously inferred by microscopy, corresponds to the full particle including the envelope.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions.

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics.

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0-2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0-2 d ahead of the percentage of positive tests by date of specimen collection, 1-4 d ahead of local hospital admissions and 6-8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics.

Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge.

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ± 0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.

An accessible method for screening aerosol filtration identifies poor-performing commercial masks and respirators.

BACKGROUND: The COVID-19 pandemic has presented an acute shortage of regulation-tested masks. Many of the alternatives available to hospitals have not been certified, leaving uncertainty about their ability to properly protect healthcare workers from SARS-CoV-2 transmission. OBJECTIVE: For situations where regulatory methods are not accessible, we present experimental methods to evaluate mask filtration and breathability quickly via cost-effective approaches (e.g., ~$2000 USD) that could be replicated in communities of need without extensive infrastructure. We demonstrate the need for screening by evaluating an existing diverse inventory of masks/respirators from a local hospital. METHODS: Two experimental approaches are presented to examine both aerosol filtration and flow impedance (i.e., breathability). For one of the approaches ("quick assessment"), screening for appropriate filtration could be performed under 10 min per mask, on average. Mask fit tests were conducted in tandem but are not the focus of this study. RESULTS: Tests conducted of 47 nonregulation masks reveal variable performance. A number of commercially available masks in hospital inventories perform similarly to N95 masks for aerosol filtration of 0.2 µm and above, but there is a range of masks with relatively lower filtration efficiencies (e.g., <90%) and a subset with poorer filtration (e.g., <70%). All masks functioned acceptably for breathability, and impedance was not correlated with filtration efficiency. SIGNIFICANCE: With simplified tests, organizations with mask/respirator shortages and uncertain inventories can make informed decisions about use and procurement.

How can airborne transmission of COVID-19 indoors be minimised?

During the rapid rise in COVID-19 illnesses and deaths globally, and notwithstanding recommended precautions, questions are voiced about routes of transmission for this pandemic disease. Inhaling small airborne droplets is probable as a third route of infection, in addition to more widely recognized transmission via larger respiratory droplets and direct contact with infected people or contaminated surfaces. While uncertainties remain regarding the relative contributions of the different transmission pathways, we argue that existing evidence is sufficiently strong to warrant engineering controls targeting airborne transmission as part of an overall strategy to limit infection risk indoors. Appropriate building engineering controls include sufficient and effective ventilation, possibly enhanced by particle filtration and air disinfection, avoiding air recirculation and avoiding overcrowding. Often, such measures can be easily implemented and without much cost, but if only they are recognised as significant in contributing to infection control goals. We believe that the use of engineering controls in public buildings, including hospitals, shops, offices, schools, kindergartens, libraries, restaurants, cruise ships, elevators, conference rooms or public transport, in parallel with effective application of other controls (including isolation and quarantine, social distancing and hand hygiene), would be an additional important measure globally to reduce the likelihood of transmission and thereby protect healthcare workers, patients and the general public.

COVID-19 vulnerability: the potential impact of genetic susceptibility and airborne transmission.

The recent coronavirus disease (COVID-19), caused by SARS-CoV-2, is inarguably the most challenging coronavirus outbreak relative to the previous outbreaks involving SARS-CoV and MERS-CoV. With the number of COVID-19 cases now exceeding 2 million worldwide, it is apparent that (i) transmission of SARS-CoV-2 is very high and (ii) there are large variations in disease severity, one component of which may be genetic variability in the response to the virus. Controlling current rates of infection and combating future waves require a better understanding of the routes of exposure to SARS-CoV-2 and the underlying genomic susceptibility to this disease. In this mini-review, we highlight possible genetic determinants of COVID-19 and the contribution of aerosol exposure as a potentially important transmission route of SARS-CoV-2.