AI Article Synopsis
- * The study found that higher RNA levels in wastewater predicted increases in COVID-19 hospitalizations and ICU admissions by 5 to 9 days earlier than clinical data.
- * Analysis of wastewater also showed the alpha-variant was dominant during the third wave, highlighting the potential of combining clinical and environmental data for early COVID-19 detection and response modeling.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421077 | PMC |
| http://dx.doi.org/10.1016/j.scitotenv.2021.150151 | DOI Listing |
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