Advances in virus detection methods for wastewater-based epidemiological applications

Quantitative SARS-CoV-2 exposure assessment for workers in wastewater treatment plants using Monte-Carlo simulation

Several studies on COVID-19 pandemic have shown that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originating from human stool are detected in raw sewage for several days, leading to potential health risks for workers due to the production of bioaerosols and droplets during wastewater treatment process. In this study, data of SARS-CoV-2 concentrations in wastewater were gathered from literatures, and a quantitative microbial risk assessment with Monte Carlo simulation was used to estimate the daily probability of infection risk through exposure to viable infectious viral airborne particles of the workers during four seasons and under six environmental conditions. Inhalation of bioaerosols and direct ingestion of wastewater droplets were selected as exposure pathways. Spearman rank correlation coefficients were used for sensitivity analysis to identify the variables with the greatest influence on the infection risk probability. It was found that the daily probability of infection risk decreased with temperature (T) and relative humidity (RH) increase. The probability of direct droplet ingestion exposure pathway was higher than that of the bioaerosol inhalation pathway. The sensitivity analysis indicated that the most sensitive variable for both exposure pathways was the concentration of SARS-CoV-2 in stool. So, appropriate aeration systems, covering facilities, and effective ventilation are suggested to implement in wastewater treatment plants (WWTPs) to reduce emission concentration. Further to this, the exposure time (t) had a larger variance contribution than T and RH for the bioaerosol inhalation pathway. Implementing measures such as adding more work shifts, mandating personal protective equipment for all workers, and implementing coverage for treatment processes can significantly reduce the risk of infection among workers at WWTPs. These measures are particularly effective during environmental conditions with low temperatures and humidity levels.

A phenomenological neural network powered by the National Wastewater Surveillance System for estimation of silent COVID-19 infections

Although wastewater-based epidemiology (WBE) has emerged as an inexpensive and non-intrusive method in contrast to clinical testing to track public health at community levels, there is a lack of structured interpretative criteria to translate the SARS-CoV-2 concentrations in wastewater to COVID-19 infection cases. The difficulties lie in the uncertainties of the amount of virus shed by an infected individual to wastewater as documented in clinical studies. This situation is even worse considering the existence of a population of silent infections and many other confounding factors. In this research, a quantitative framework of a phenomenological neural network (PNN) was developed to compute silent infections. The PNN was trained using the WBE data from the National Wastewater Surveillance System (NWSS) - a program launched by the CDC of the United States in 2020. It is found that the PNN excelled with superior interpretability and reduced overfitting. A big-data perspective on virus shedding by an infected population revealed more deterministic virus-shedding dynamics compared to the clinical studies perspective on virus shedding by an infected individual. With such characteristics employed as the theoretical basis for the estimation of the silent infections, a ratio of silent to reported infections was found to be 5.7 as the national median during the studied period. The study also noted the influence of temperature, sewershed population, and per-capita flow rates on the computation of silent infections. It is expected that the proposed framework in this work would facilitate public health actions guided by the SARS-CoV-2 concentrations in wastewater. In case of a new wave emergence or a new virus disease outbreak like COVID-19, the PNN powered by the NWSS would outline consolidated and systematic information that would enable rapid deployment of public health actions.

SARS-CoV-2 detection and inactivation in water and wastewater: Review on analytical methods, limitations and future research recommendations

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been detected in wastewater. Wastewater-based epidemiology (WBE) is a practical and cost-effective tool for the assessment and controlling of pandemics and probably for examining SARS-CoV-2 presence. Implementation of WBE during the outbreaks is not without limitations. Temperature, suspended solids, pH, and disinfectants affect the stability of viruses in wastewater. Due to these limitations, instruments and techniques have been utilized to detect SARS-CoV-2. SARS-CoV-2 has been detected in sewage using various concentration methods and computer-aided analyzes. RT-qPCR, ddRT-PCR, multiplex PCR, RT-LAMP, and electrochemical immunosensors have been employed to detect low levels of viral contamination. Inactivation of SARS-CoV-2 is a crucial preventive measure against coronavirus disease 2019 (COVID-19). To better assess the role of wastewater as a transmission route, detection, and quantification methods need to be refined. In this paper, the latest improvements in quantification, detection, and inactivation of SARS-CoV-2 in wastewater are explained. Finally, limitations and future research recommendations are thoroughly described.