Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks.

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples.

Wastewater-based epidemiology has emerged as a promising and efficacious surveillance system for SARS-CoV-2 and other infectious diseases in many nations. The process typically involves wastewater concentration, nucleic acid extraction, amplification of selected genomic segments, and detection and quantification of the amplified genomic segment. This methodology can similarly be leveraged to detect and quantify infectious agents, such as SARS-CoV-2, in air samples. Initially, SARS-CoV-2 was presumed to spread primarily through close personal contact with droplets generated by an infected individual while speaking, sneezing, coughing, singing, or breathing. However, a growing number of studies have reported the presence of SARS-CoV-2 RNA in the air of healthcare facilities, establishing airborne transmission as a viable route for the virus. This study presents a composite of established protocols to facilitate environmental detection, quantification, and sequencing of viruses from both wastewater and air samples.

Environmental Dissemination of SARS-CoV-2 in a University Hospital during the COVID-19 5th Wave Delta Variant Peak in Castile-León, Spain.

The dominant SARS-CoV-2 Delta variant (B.1.617.2) became the main circulating variant among countries by mid 2021. Attention was raised to the increased risk of airborne transmission, leading to nosocomial outbreaks even among vaccinated individuals. Considering the increased number of COVID-19 hospital admissions fueled by the spread of the variant, with Spain showing the highest COVID-19 rates in mainland Europe by July 2021, the aim of this study was to assess SARS-CoV-2 environmental contamination in different areas of a University Hospital in the region of Castile-León, Spain, during the peak of the 5th wave of COVID-19 in the country (July 2021). Air samples were collected from sixteen different areas of the Hospital using a Coriolis® µ air sampler. Surface samples were collected in these same areas using sterile flocked plastic swabs. RNA extraction followed by a one-step RT-qPCR were performed for detection of SARS-CoV-2 RNA. Of the 21 air samples, only one was positive for SARS-CoV-2 RNA, from the emergency waiting room. Of the 40 surface samples, 2 were positive for SARS-CoV-2 RNA, both from the microbiology laboratory. These results may be relevant for risk assessment of nosocomial infection within healthcare facilities, thus helping prevent and minimize healthcare staff's exposure to SARS-CoV-2, reinforcing the importance of always wearing appropriate and well-fit masks at all times and proper PPE when in contact with infected patients.

Centralized and decentralized wastewater-based epidemiology to infer COVID-19 transmission - A brief review.

Wastewater-based epidemiology has shown to be a promising and innovative approach to measure a wide variety of illicit drugs that are consumed in the communities. In the same way as for illicit drugs, wastewater-based epidemiology is a promising approach to understand the prevalence of viruses in a community-level. The ongoing coronavirus disease 2019 (COVID-19) pandemic created an unprecedented burden on public health and diagnostic laboratories all over the world because of the need for massive laboratory testing. Many studies have shown the applicability of a centralized wastewater-based epidemiology (WBE) approach, where samples are collected at WWTPs. A more recent concept is a decentralized approach for WBE where samples are collected at different points of the sewer system and at polluted water bodies. The second being particularly important in countries where there are insufficient connections from houses to municipal sewage pipelines and thus untreated wastewater is discharged directly in environmental waters. A decentralized approach can be used to focus the value of diagnostic tests in what we call targeted-WBE, by monitoring wastewater in parts of the population where an outbreak is likely to happen, such as student dorms, retirement homes and hospitals. A combination of centralized and decentralized WBE should be considered for an affordable, sustainable, and successful WBE implementation in high-, middle- and low-income countries.

Effect of digestate loading rates on microalgae-based treatment under low LED light intensity.

Low red-LED irradiances are an attractive alternative for enhancing microalgae photobioreactors treating digestate due to their potential contribution in decreasing area footprints with low energy consumptions. However, more information is required regarding the influence of digestate load on treatment performance and biomass valorisation when low-intensity red-LEDs are applied. Thus, this study assessed microalgae-based photobioreactors treating food waste digestate under different concentrations (5%, 25%, 50%, and 75%, v/v) at low red-LED irradiance (15 µmol·m-2·s-1). The removal efficiencies of soluble chemical oxygen demand (sCOD) at the end of the experiment ranged from 45% to 75% when treating influent loads between 5.3 and 79.1 g sCOD·m-3·d-1 (5% and 75%-digestate), respectively. Total ammonia nitrogen (TAN) was applied in loading rates between 3.2 and 48.5 g TAN·m-3·d-1 (5% and 75%, respectively) and removed with maximum efficiencies of 90%-100% in all trials. Nitrification-denitrification was proportionally more relevant when treating 5%-digestate, whereas volatilisation was the primary process in 25%, 50% and 75% concentrations. Microalgae presented adequate yields in all treatments, except in 75%-digestate, likely due to the blocking of light by the high solids concentrations. The assessment of the microalgae community and chlorophyll-a and carotenoids suggested that chlorophytes, mainly Dictyosphaerium pulchellum and Scenedesmus sp. grew autotrophically, whereas cyanobacteria Pseudanabaena sp. grew mixotrophically. Moreover, the sustainability of red LED lighting applications can be increased by anaerobic digestion or agricultural valorisation of the biomass, enabled by its high N and P contents. Low-intensity red-LEDs may have promissory applications in the treatment of high-strength wastewaters.

Viability of SARS-CoV-2 in river water and wastewater at different temperatures and solids content.

COVID-19 patients can excrete viable SARS-CoV-2 virus via urine and faeces, which has raised concerns over the possibility of COVID-19 transmission via aerosolized contaminated water or via the faecal-oral route. These concerns are especially exacerbated in many low- and middle-income countries, where untreated sewage is frequently discharged to surface waters. SARS-CoV-2 RNA has been detected in river water (RW) and raw wastewater (WW) samples. However, little is known about SARS-CoV-2 viability in these environmental matrices. Determining the persistence of SARS-CoV-2 in water under different environmental conditions is of great importance for basic assumptions in quantitative microbial risk assessment (QMRA). In this study, the persistence of SARS-CoV-2 was assessed using plaque assays following spiking of RW and WW samples with infectious SARS-CoV-2 that was previously isolated from a COVID-19 patient. These assays were carried out on autoclaved RW and WW samples, filtered (0.22 µm) and unfiltered, at 4 °C and 24 °C. Linear and nonlinear regression models were adjusted to the data. The Weibull regression model achieved the lowest root mean square error (RMSE) and was hence chosen to estimate T90 and T99 (time required for 1 log and 2 log reductions, respectively). SARS-CoV-2 remained viable longer in filtered compared with unfiltered samples. RW and WW showed T90 values of 1.9 and 1.2 day and T99 values of 6.4 and 4.0 days, respectively. When samples were filtered through 0.22 µm pore size membranes, T90 values increased to 3.3 and 1.5 days, and T99 increased to 8.5 and 4.5 days, for RW and WW samples, respectively. Remarkable increases in SARS-CoV-2 persistence were observed in assays at 4 °C, which showed T90 values of 7.7 and 5.5 days, and T99 values of 18.7 and 17.5 days for RW and WW, respectively. These results highlight the variability of SARS-CoV-2 persistence in water and wastewater matrices and can be highly relevant to efforts aimed at quantifying water-related risks, which could be valuable for understanding and controlling the pandemic.

Assessment of the performance of an anoxic-aerobic microalgal-bacterial system treating digestate.

The performance of an anoxic-aerobic microalgal-bacterial system treating synthetic food waste digestate at 10 days of hydraulic retention time via nitrification-denitrification under increasing digestate concentrations of 25%, 50%, and 100% (v/v) was assessed during Stages I, II and III, respectively. The system supported adequate treatment without external CO2 supplementation since sufficient inorganic carbon in the digestate was available for autotrophic growth. High steady-state Total Organic Carbon (TOC) and Total Nitrogen (TN) removal efficiencies of 85-96% and 73-84% were achieved in Stages I and II. Similarly, PO43--P removals of 81 ± 15% and 58 ± 4% were recorded during these stages. During Stage III, the average influent concentrations of 815 ± 35 mg TOC·L-1, 610 ± 23 mg TN·L-1, and 46 ± 11 mg PO43--P·L-1 induced O2 limiting conditions, resulting in TOC, TN and PO43--P removals of 85 ± 3%, 73 ± 3%, and 28 ± 16%, respectively. Digestate concentrations of 25% and 50% favored nitrification-denitrification mechanisms, whereas the treatment of undiluted digestate resulted in higher ammonia volatilization and hampered nitrification-denitrification. In Stages I and II, the microalgal community was dominated by Chlorella vulgaris and Cryptomonas sp., whereas Pseudoanabaena sp. was more abundant during Stage III. Illumina sequencing revealed the presence of carbon and nitrogen transforming bacteria, with dominances of the genera Gemmata, Azospirillum, and Psychrobacter during Stage I, II, and III, respectively. Finally, the high settleability of the biomass (98% of suspended solids removal in the settler) and average C (42%), N (7%), P (0.2%), and S (0.4%) contents recovered in the biomass confirmed its potential for agricultural applications, contributing to a closed-cycle management of food waste.

Treatment of food waste digestate using microalgae-based systems with low-intensity light-emitting diodes.

Anaerobic digestion of food wastes coupled with digestate post-treatment using microalgae-based systems could recover large amounts of energy and nutrients worldwide. However, the development of full-scale implementations requires overcoming microalgae inhibition by high ammonia concentrations and low light transmittances affecting photosynthesis. This study evaluated the potential of microalgae-based reactors supplied with red light-emitting diodes (LEDs) at low intensity (660 nm and 15 µmol·m-2·s-1) to treat food waste digestate. LED reactors were compared with control reactors exposed to solar radiation. From a range of species in the inoculum, Chlorella vulgaris showed high adaptation to both lighting regimes and digestate environmental conditions, characterized by a C:N:P ratio of 74:74:1. Removal efficiencies for control and LED reactors were 84.0% and 95.8% for soluble chemical oxygen demand (COD) and 89.4% and 53.0% for ammonia, respectively. Approximately 50% of ammonia in control reactor and 15% in LED reactor was lost from the systems, whereas 17% and 36% of ammonia was transformed to organic nitrogen in control and LED reactors, respectively. Low-intensity LEDs maintained microalgae growth in levels similar to solar radiation and supported efficient digestate treatment, showing a potential for further application in optimization of full scale reactors at a relatively low energy cost.