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.

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.